Head-to-Head AI Model Comparison Matrix
Select any two frontier or open-weight models to inspect direct token pricing deltas, response speed differences, and coding benchmark leads.
Claude 3.5 Haiku vs Claude 3.5 Sonnet
Claude 3.5 Haiku is 73.3% cheaper for input tokens ($0.80 vs. $3.00 per 1M tokens) and $4.00 vs. $15.00 for output tokens (3.8x cost difference). In terms of operational performance, Claude 3.5 Haiku delivers faster response latency with 140ms TTFT (180ms faster than Claude 3.5 Sonnet). Claude 3.5 Sonnet leads coding benchmarks at 63.7% SWE-bench vs. 40.6% for Claude 3.5 Haiku.
Claude 3.5 Haiku vs Claude 3.7 Sonnet
Claude 3.5 Haiku is 73.3% cheaper for input tokens ($0.80 vs. $3.00 per 1M tokens) and $4.00 vs. $15.00 for output tokens (3.8x cost difference). In terms of operational performance, Claude 3.5 Haiku delivers faster response latency with 140ms TTFT (510ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 40.6% for Claude 3.5 Haiku.
Claude 3.5 Haiku vs Claude Opus 5
Claude 3.5 Haiku is 84.0% cheaper for input tokens ($0.80 vs. $5.00 per 1M tokens) and $4.00 vs. $25.00 for output tokens (6.2x cost difference). In terms of operational performance, Claude 3.5 Haiku delivers faster response latency with 140ms TTFT (200ms faster than Claude Opus 5). Claude Opus 5 leads coding benchmarks at 82.4% SWE-bench vs. 40.6% for Claude 3.5 Haiku.
Claude 3.5 Haiku vs Codestral 25.01
Codestral 25.01 is 62.5% cheaper for input tokens ($0.30 vs. $0.80 per 1M tokens) and $0.90 vs. $4.00 for output tokens (2.7x cost difference). In terms of operational performance, Claude 3.5 Haiku delivers faster response latency with 140ms TTFT (10ms faster than Codestral 25.01). Codestral 25.01 leads coding benchmarks at 44.2% SWE-bench vs. 40.6% for Claude 3.5 Haiku.
Claude 3.5 Haiku vs Composer 2.5
Claude 3.5 Haiku is 55.6% cheaper for input tokens ($0.80 vs. $1.80 per 1M tokens) and $4.00 vs. $7.20 for output tokens (2.2x cost difference). In terms of operational performance, Claude 3.5 Haiku delivers faster response latency with 140ms TTFT (40ms faster than Composer 2.5). Composer 2.5 leads coding benchmarks at 74.6% SWE-bench vs. 40.6% for Claude 3.5 Haiku.
Claude 3.5 Haiku vs DeepSeek-R1
DeepSeek-R1 is 31.2% cheaper for input tokens ($0.55 vs. $0.80 per 1M tokens) and $2.19 vs. $4.00 for output tokens (1.5x cost difference). In terms of operational performance, Claude 3.5 Haiku delivers faster response latency with 140ms TTFT (1660ms faster than DeepSeek-R1). DeepSeek-R1 leads coding benchmarks at 49.2% SWE-bench vs. 40.6% for Claude 3.5 Haiku.
Claude 3.5 Haiku vs DeepSeek-V3
DeepSeek-V3 is 82.5% cheaper for input tokens ($0.14 vs. $0.80 per 1M tokens) and $0.28 vs. $4.00 for output tokens (5.7x cost difference). In terms of operational performance, Claude 3.5 Haiku delivers faster response latency with 140ms TTFT (200ms faster than DeepSeek-V3). DeepSeek-V3 leads coding benchmarks at 42.0% SWE-bench vs. 40.6% for Claude 3.5 Haiku.
Claude 3.5 Haiku vs DeepSeek-V4 Flash
DeepSeek-V4 Flash is 82.5% cheaper for input tokens ($0.14 vs. $0.80 per 1M tokens) and $0.56 vs. $4.00 for output tokens (5.7x cost difference). In terms of operational performance, Claude 3.5 Haiku delivers faster response latency with 140ms TTFT (10ms faster than DeepSeek-V4 Flash). DeepSeek-V4 Flash leads coding benchmarks at 62.4% SWE-bench vs. 40.6% for Claude 3.5 Haiku.
Claude 3.5 Haiku vs Fable 5
Claude 3.5 Haiku is 60.0% cheaper for input tokens ($0.80 vs. $2.00 per 1M tokens) and $4.00 vs. $8.00 for output tokens (2.5x cost difference). In terms of operational performance, Claude 3.5 Haiku delivers faster response latency with 140ms TTFT (100ms faster than Fable 5). Fable 5 leads coding benchmarks at 58.0% SWE-bench vs. 40.6% for Claude 3.5 Haiku.
Claude 3.5 Haiku vs Gemini 2.0 Flash
Gemini 2.0 Flash is 87.5% cheaper for input tokens ($0.10 vs. $0.80 per 1M tokens) and $0.40 vs. $4.00 for output tokens (8.0x cost difference). In terms of operational performance, Claude 3.5 Haiku delivers faster response latency with 140ms TTFT (240ms faster than Gemini 2.0 Flash). Gemini 2.0 Flash leads coding benchmarks at 48.0% SWE-bench vs. 40.6% for Claude 3.5 Haiku.
Claude 3.5 Haiku vs Gemini 3.7 Flash
Gemini 3.7 Flash is 90.0% cheaper for input tokens ($0.08 vs. $0.80 per 1M tokens) and $0.32 vs. $4.00 for output tokens (10.0x cost difference). In terms of operational performance, Gemini 3.7 Flash delivers faster response latency with 75ms TTFT (65ms faster than Claude 3.5 Haiku). Gemini 3.7 Flash leads coding benchmarks at 68.2% SWE-bench vs. 40.6% for Claude 3.5 Haiku.
Claude 3.5 Haiku vs GLM 5.3 Flash
GLM 5.3 Flash is 81.2% cheaper for input tokens ($0.15 vs. $0.80 per 1M tokens) and $0.50 vs. $4.00 for output tokens (5.3x cost difference). In terms of operational performance, GLM 5.3 Flash delivers faster response latency with 130ms TTFT (10ms faster than Claude 3.5 Haiku). GLM 5.3 Flash leads coding benchmarks at 54.2% SWE-bench vs. 40.6% for Claude 3.5 Haiku.
Claude 3.5 Haiku vs OpenAI GPT-4o
Claude 3.5 Haiku is 68.0% cheaper for input tokens ($0.80 vs. $2.50 per 1M tokens) and $4.00 vs. $10.00 for output tokens (3.1x cost difference). In terms of operational performance, Claude 3.5 Haiku delivers faster response latency with 140ms TTFT (140ms faster than OpenAI GPT-4o). Claude 3.5 Haiku leads coding benchmarks at 40.6% SWE-bench vs. 38.8% for OpenAI GPT-4o.
Claude 3.5 Haiku vs GPT-5.6 Luna
GPT-5.6 Luna is 77.5% cheaper for input tokens ($0.18 vs. $0.80 per 1M tokens) and $0.72 vs. $4.00 for output tokens (4.4x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (50ms faster than Claude 3.5 Haiku). GPT-5.6 Luna leads coding benchmarks at 48.5% SWE-bench vs. 40.6% for Claude 3.5 Haiku.
Claude 3.5 Haiku vs GPT-5.6 Sol
Claude 3.5 Haiku is 90.0% cheaper for input tokens ($0.80 vs. $8.00 per 1M tokens) and $4.00 vs. $32.00 for output tokens (10.0x cost difference). In terms of operational performance, Claude 3.5 Haiku delivers faster response latency with 140ms TTFT (280ms faster than GPT-5.6 Sol). GPT-5.6 Sol leads coding benchmarks at 79.5% SWE-bench vs. 40.6% for Claude 3.5 Haiku.
Claude 3.5 Haiku vs GPT-5.6 Terra
Claude 3.5 Haiku is 46.7% cheaper for input tokens ($0.80 vs. $1.50 per 1M tokens) and $4.00 vs. $6.00 for output tokens (1.9x cost difference). In terms of operational performance, Claude 3.5 Haiku delivers faster response latency with 140ms TTFT (70ms faster than GPT-5.6 Terra). GPT-5.6 Terra leads coding benchmarks at 65.4% SWE-bench vs. 40.6% for Claude 3.5 Haiku.
Claude 3.5 Haiku vs Grok 3
Claude 3.5 Haiku is 73.3% cheaper for input tokens ($0.80 vs. $3.00 per 1M tokens) and $4.00 vs. $15.00 for output tokens (3.8x cost difference). In terms of operational performance, Claude 3.5 Haiku delivers faster response latency with 140ms TTFT (710ms faster than Grok 3). Grok 3 leads coding benchmarks at 58.5% SWE-bench vs. 40.6% for Claude 3.5 Haiku.
Claude 3.5 Haiku vs xAI Grok 4.6
Claude 3.5 Haiku is 60.0% cheaper for input tokens ($0.80 vs. $2.00 per 1M tokens) and $4.00 vs. $6.00 for output tokens (2.5x cost difference). In terms of operational performance, Claude 3.5 Haiku delivers faster response latency with 140ms TTFT (140ms faster than xAI Grok 4.6). xAI Grok 4.6 leads coding benchmarks at 76.8% SWE-bench vs. 40.6% for Claude 3.5 Haiku.
Claude 3.5 Haiku vs Llama 3.3 70B Instruct
Llama 3.3 70B Instruct is 77.5% cheaper for input tokens ($0.18 vs. $0.80 per 1M tokens) and $0.40 vs. $4.00 for output tokens (4.4x cost difference). In terms of operational performance, Claude 3.5 Haiku delivers faster response latency with 140ms TTFT (280ms faster than Llama 3.3 70B Instruct). Claude 3.5 Haiku leads coding benchmarks at 40.6% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Claude 3.5 Haiku vs Mistral Large 2
Claude 3.5 Haiku is 60.0% cheaper for input tokens ($0.80 vs. $2.00 per 1M tokens) and $4.00 vs. $6.00 for output tokens (2.5x cost difference). In terms of operational performance, Claude 3.5 Haiku delivers faster response latency with 140ms TTFT (410ms faster than Mistral Large 2). Claude 3.5 Haiku leads coding benchmarks at 40.6% SWE-bench vs. 39.0% for Mistral Large 2.
Claude 3.5 Haiku vs OpenAI o1
Claude 3.5 Haiku is 94.7% cheaper for input tokens ($0.80 vs. $15.00 per 1M tokens) and $4.00 vs. $60.00 for output tokens (18.8x cost difference). In terms of operational performance, Claude 3.5 Haiku delivers faster response latency with 140ms TTFT (710ms faster than OpenAI o1). OpenAI o1 leads coding benchmarks at 48.9% SWE-bench vs. 40.6% for Claude 3.5 Haiku.
Claude 3.5 Haiku vs o3-mini
Claude 3.5 Haiku is 27.3% cheaper for input tokens ($0.80 vs. $1.10 per 1M tokens) and $4.00 vs. $4.40 for output tokens (1.4x cost difference). In terms of operational performance, Claude 3.5 Haiku delivers faster response latency with 140ms TTFT (1060ms faster than o3-mini). o3-mini leads coding benchmarks at 49.3% SWE-bench vs. 40.6% for Claude 3.5 Haiku.
Claude 3.5 Haiku vs Microsoft Phi-4 (14B)
Microsoft Phi-4 (14B) is 85.0% cheaper for input tokens ($0.12 vs. $0.80 per 1M tokens) and $0.36 vs. $4.00 for output tokens (6.7x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (30ms faster than Claude 3.5 Haiku). Microsoft Phi-4 (14B) leads coding benchmarks at 42.1% SWE-bench vs. 40.6% for Claude 3.5 Haiku.
Claude 3.5 Haiku vs Qwen 2.5 72B Instruct
Qwen 2.5 72B Instruct is 56.2% cheaper for input tokens ($0.35 vs. $0.80 per 1M tokens) and $0.40 vs. $4.00 for output tokens (2.3x cost difference). In terms of operational performance, Claude 3.5 Haiku delivers faster response latency with 140ms TTFT (280ms faster than Qwen 2.5 72B Instruct). Qwen 2.5 72B Instruct leads coding benchmarks at 44.0% SWE-bench vs. 40.6% for Claude 3.5 Haiku.
Claude 3.5 Haiku vs Qwen 2.5 Max
Qwen 2.5 Max is 65.0% cheaper for input tokens ($0.28 vs. $0.80 per 1M tokens) and $0.84 vs. $4.00 for output tokens (2.9x cost difference). In terms of operational performance, Claude 3.5 Haiku delivers faster response latency with 140ms TTFT (340ms faster than Qwen 2.5 Max). Qwen 2.5 Max leads coding benchmarks at 44.2% SWE-bench vs. 40.6% for Claude 3.5 Haiku.
Claude 3.5 Haiku vs Qwen 3.8 Flash Next
Qwen 3.8 Flash Next is 85.0% cheaper for input tokens ($0.12 vs. $0.80 per 1M tokens) and $0.48 vs. $4.00 for output tokens (6.7x cost difference). In terms of operational performance, Qwen 3.8 Flash Next delivers faster response latency with 120ms TTFT (20ms faster than Claude 3.5 Haiku). Qwen 3.8 Flash Next leads coding benchmarks at 56.8% SWE-bench vs. 40.6% for Claude 3.5 Haiku.
Claude 3.5 Sonnet vs Claude 3.7 Sonnet
Both models share identical input pricing at $3.00 per 1M tokens. In terms of operational performance, Claude 3.5 Sonnet delivers faster response latency with 320ms TTFT (330ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 63.7% for Claude 3.5 Sonnet.
Claude 3.5 Sonnet vs Claude Opus 5
Claude 3.5 Sonnet is 40.0% cheaper for input tokens ($3.00 vs. $5.00 per 1M tokens) and $15.00 vs. $25.00 for output tokens (1.7x cost difference). In terms of operational performance, Claude 3.5 Sonnet delivers faster response latency with 320ms TTFT (20ms faster than Claude Opus 5). Claude Opus 5 leads coding benchmarks at 82.4% SWE-bench vs. 63.7% for Claude 3.5 Sonnet.
Claude 3.5 Sonnet vs Codestral 25.01
Codestral 25.01 is 90.0% cheaper for input tokens ($0.30 vs. $3.00 per 1M tokens) and $0.90 vs. $15.00 for output tokens (10.0x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (170ms faster than Claude 3.5 Sonnet). Claude 3.5 Sonnet leads coding benchmarks at 63.7% SWE-bench vs. 44.2% for Codestral 25.01.
Claude 3.5 Sonnet vs Composer 2.5
Composer 2.5 is 40.0% cheaper for input tokens ($1.80 vs. $3.00 per 1M tokens) and $7.20 vs. $15.00 for output tokens (1.7x cost difference). In terms of operational performance, Composer 2.5 delivers faster response latency with 180ms TTFT (140ms faster than Claude 3.5 Sonnet). Composer 2.5 leads coding benchmarks at 74.6% SWE-bench vs. 63.7% for Claude 3.5 Sonnet.
Claude 3.5 Sonnet vs DeepSeek-R1
DeepSeek-R1 is 81.7% cheaper for input tokens ($0.55 vs. $3.00 per 1M tokens) and $2.19 vs. $15.00 for output tokens (5.5x cost difference). In terms of operational performance, Claude 3.5 Sonnet delivers faster response latency with 320ms TTFT (1480ms faster than DeepSeek-R1). Claude 3.5 Sonnet leads coding benchmarks at 63.7% SWE-bench vs. 49.2% for DeepSeek-R1.
Claude 3.5 Sonnet vs DeepSeek-V3
DeepSeek-V3 is 95.3% cheaper for input tokens ($0.14 vs. $3.00 per 1M tokens) and $0.28 vs. $15.00 for output tokens (21.4x cost difference). In terms of operational performance, Claude 3.5 Sonnet delivers faster response latency with 320ms TTFT (20ms faster than DeepSeek-V3). Claude 3.5 Sonnet leads coding benchmarks at 63.7% SWE-bench vs. 42.0% for DeepSeek-V3.
Claude 3.5 Sonnet vs DeepSeek-V4 Flash
DeepSeek-V4 Flash is 95.3% cheaper for input tokens ($0.14 vs. $3.00 per 1M tokens) and $0.56 vs. $15.00 for output tokens (21.4x cost difference). In terms of operational performance, DeepSeek-V4 Flash delivers faster response latency with 150ms TTFT (170ms faster than Claude 3.5 Sonnet). Claude 3.5 Sonnet leads coding benchmarks at 63.7% SWE-bench vs. 62.4% for DeepSeek-V4 Flash.
Claude 3.5 Sonnet vs Fable 5
Fable 5 is 33.3% cheaper for input tokens ($2.00 vs. $3.00 per 1M tokens) and $8.00 vs. $15.00 for output tokens (1.5x cost difference). In terms of operational performance, Fable 5 delivers faster response latency with 240ms TTFT (80ms faster than Claude 3.5 Sonnet). Claude 3.5 Sonnet leads coding benchmarks at 63.7% SWE-bench vs. 58.0% for Fable 5.
Claude 3.5 Sonnet vs Gemini 2.0 Flash
Gemini 2.0 Flash is 96.7% cheaper for input tokens ($0.10 vs. $3.00 per 1M tokens) and $0.40 vs. $15.00 for output tokens (30.0x cost difference). In terms of operational performance, Claude 3.5 Sonnet delivers faster response latency with 320ms TTFT (60ms faster than Gemini 2.0 Flash). Claude 3.5 Sonnet leads coding benchmarks at 63.7% SWE-bench vs. 48.0% for Gemini 2.0 Flash.
Claude 3.5 Sonnet vs Gemini 3.7 Flash
Gemini 3.7 Flash is 97.3% cheaper for input tokens ($0.08 vs. $3.00 per 1M tokens) and $0.32 vs. $15.00 for output tokens (37.5x cost difference). In terms of operational performance, Gemini 3.7 Flash delivers faster response latency with 75ms TTFT (245ms faster than Claude 3.5 Sonnet). Gemini 3.7 Flash leads coding benchmarks at 68.2% SWE-bench vs. 63.7% for Claude 3.5 Sonnet.
Claude 3.5 Sonnet vs GLM 5.3 Flash
GLM 5.3 Flash is 95.0% cheaper for input tokens ($0.15 vs. $3.00 per 1M tokens) and $0.50 vs. $15.00 for output tokens (20.0x cost difference). In terms of operational performance, GLM 5.3 Flash delivers faster response latency with 130ms TTFT (190ms faster than Claude 3.5 Sonnet). Claude 3.5 Sonnet leads coding benchmarks at 63.7% SWE-bench vs. 54.2% for GLM 5.3 Flash.
Claude 3.5 Sonnet vs OpenAI GPT-4o
OpenAI GPT-4o is 16.7% cheaper for input tokens ($2.50 vs. $3.00 per 1M tokens) and $10.00 vs. $15.00 for output tokens (1.2x cost difference). In terms of operational performance, OpenAI GPT-4o delivers faster response latency with 280ms TTFT (40ms faster than Claude 3.5 Sonnet). Claude 3.5 Sonnet leads coding benchmarks at 63.7% SWE-bench vs. 38.8% for OpenAI GPT-4o.
Claude 3.5 Sonnet vs GPT-5.6 Luna
GPT-5.6 Luna is 94.0% cheaper for input tokens ($0.18 vs. $3.00 per 1M tokens) and $0.72 vs. $15.00 for output tokens (16.7x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (230ms faster than Claude 3.5 Sonnet). Claude 3.5 Sonnet leads coding benchmarks at 63.7% SWE-bench vs. 48.5% for GPT-5.6 Luna.
Claude 3.5 Sonnet vs GPT-5.6 Sol
Claude 3.5 Sonnet is 62.5% cheaper for input tokens ($3.00 vs. $8.00 per 1M tokens) and $15.00 vs. $32.00 for output tokens (2.7x cost difference). In terms of operational performance, Claude 3.5 Sonnet delivers faster response latency with 320ms TTFT (100ms faster than GPT-5.6 Sol). GPT-5.6 Sol leads coding benchmarks at 79.5% SWE-bench vs. 63.7% for Claude 3.5 Sonnet.
Claude 3.5 Sonnet vs GPT-5.6 Terra
GPT-5.6 Terra is 50.0% cheaper for input tokens ($1.50 vs. $3.00 per 1M tokens) and $6.00 vs. $15.00 for output tokens (2.0x cost difference). In terms of operational performance, GPT-5.6 Terra delivers faster response latency with 210ms TTFT (110ms faster than Claude 3.5 Sonnet). GPT-5.6 Terra leads coding benchmarks at 65.4% SWE-bench vs. 63.7% for Claude 3.5 Sonnet.
Claude 3.5 Sonnet vs Grok 3
Both models share identical input pricing at $3.00 per 1M tokens. In terms of operational performance, Claude 3.5 Sonnet delivers faster response latency with 320ms TTFT (530ms faster than Grok 3). Claude 3.5 Sonnet leads coding benchmarks at 63.7% SWE-bench vs. 58.5% for Grok 3.
Claude 3.5 Sonnet vs xAI Grok 4.6
xAI Grok 4.6 is 33.3% cheaper for input tokens ($2.00 vs. $3.00 per 1M tokens) and $6.00 vs. $15.00 for output tokens (1.5x cost difference). In terms of operational performance, xAI Grok 4.6 delivers faster response latency with 280ms TTFT (40ms faster than Claude 3.5 Sonnet). xAI Grok 4.6 leads coding benchmarks at 76.8% SWE-bench vs. 63.7% for Claude 3.5 Sonnet.
Claude 3.5 Sonnet vs Llama 3.3 70B Instruct
Llama 3.3 70B Instruct is 94.0% cheaper for input tokens ($0.18 vs. $3.00 per 1M tokens) and $0.40 vs. $15.00 for output tokens (16.7x cost difference). In terms of operational performance, Claude 3.5 Sonnet delivers faster response latency with 320ms TTFT (100ms faster than Llama 3.3 70B Instruct). Claude 3.5 Sonnet leads coding benchmarks at 63.7% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Claude 3.5 Sonnet vs Mistral Large 2
Mistral Large 2 is 33.3% cheaper for input tokens ($2.00 vs. $3.00 per 1M tokens) and $6.00 vs. $15.00 for output tokens (1.5x cost difference). In terms of operational performance, Claude 3.5 Sonnet delivers faster response latency with 320ms TTFT (230ms faster than Mistral Large 2). Claude 3.5 Sonnet leads coding benchmarks at 63.7% SWE-bench vs. 39.0% for Mistral Large 2.
Claude 3.5 Sonnet vs OpenAI o1
Claude 3.5 Sonnet is 80.0% cheaper for input tokens ($3.00 vs. $15.00 per 1M tokens) and $15.00 vs. $60.00 for output tokens (5.0x cost difference). In terms of operational performance, Claude 3.5 Sonnet delivers faster response latency with 320ms TTFT (530ms faster than OpenAI o1). Claude 3.5 Sonnet leads coding benchmarks at 63.7% SWE-bench vs. 48.9% for OpenAI o1.
Claude 3.5 Sonnet vs o3-mini
o3-mini is 63.3% cheaper for input tokens ($1.10 vs. $3.00 per 1M tokens) and $4.40 vs. $15.00 for output tokens (2.7x cost difference). In terms of operational performance, Claude 3.5 Sonnet delivers faster response latency with 320ms TTFT (880ms faster than o3-mini). Claude 3.5 Sonnet leads coding benchmarks at 63.7% SWE-bench vs. 49.3% for o3-mini.
Claude 3.5 Sonnet vs Microsoft Phi-4 (14B)
Microsoft Phi-4 (14B) is 96.0% cheaper for input tokens ($0.12 vs. $3.00 per 1M tokens) and $0.36 vs. $15.00 for output tokens (25.0x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (210ms faster than Claude 3.5 Sonnet). Claude 3.5 Sonnet leads coding benchmarks at 63.7% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
Claude 3.5 Sonnet vs Qwen 2.5 72B Instruct
Qwen 2.5 72B Instruct is 88.3% cheaper for input tokens ($0.35 vs. $3.00 per 1M tokens) and $0.40 vs. $15.00 for output tokens (8.6x cost difference). In terms of operational performance, Claude 3.5 Sonnet delivers faster response latency with 320ms TTFT (100ms faster than Qwen 2.5 72B Instruct). Claude 3.5 Sonnet leads coding benchmarks at 63.7% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
Claude 3.5 Sonnet vs Qwen 2.5 Max
Qwen 2.5 Max is 90.7% cheaper for input tokens ($0.28 vs. $3.00 per 1M tokens) and $0.84 vs. $15.00 for output tokens (10.7x cost difference). In terms of operational performance, Claude 3.5 Sonnet delivers faster response latency with 320ms TTFT (160ms faster than Qwen 2.5 Max). Claude 3.5 Sonnet leads coding benchmarks at 63.7% SWE-bench vs. 44.2% for Qwen 2.5 Max.
Claude 3.5 Sonnet vs Qwen 3.8 Flash Next
Qwen 3.8 Flash Next is 96.0% cheaper for input tokens ($0.12 vs. $3.00 per 1M tokens) and $0.48 vs. $15.00 for output tokens (25.0x cost difference). In terms of operational performance, Qwen 3.8 Flash Next delivers faster response latency with 120ms TTFT (200ms faster than Claude 3.5 Sonnet). Claude 3.5 Sonnet leads coding benchmarks at 63.7% SWE-bench vs. 56.8% for Qwen 3.8 Flash Next.
Claude 3.7 Sonnet vs Claude Opus 5
Claude 3.7 Sonnet is 40.0% cheaper for input tokens ($3.00 vs. $5.00 per 1M tokens) and $15.00 vs. $25.00 for output tokens (1.7x cost difference). In terms of operational performance, Claude Opus 5 delivers faster response latency with 340ms TTFT (310ms faster than Claude 3.7 Sonnet). Claude Opus 5 leads coding benchmarks at 82.4% SWE-bench vs. 70.3% for Claude 3.7 Sonnet.
Claude 3.7 Sonnet vs Codestral 25.01
Codestral 25.01 is 90.0% cheaper for input tokens ($0.30 vs. $3.00 per 1M tokens) and $0.90 vs. $15.00 for output tokens (10.0x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (500ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 44.2% for Codestral 25.01.
Claude 3.7 Sonnet vs Composer 2.5
Composer 2.5 is 40.0% cheaper for input tokens ($1.80 vs. $3.00 per 1M tokens) and $7.20 vs. $15.00 for output tokens (1.7x cost difference). In terms of operational performance, Composer 2.5 delivers faster response latency with 180ms TTFT (470ms faster than Claude 3.7 Sonnet). Composer 2.5 leads coding benchmarks at 74.6% SWE-bench vs. 70.3% for Claude 3.7 Sonnet.
Claude 3.7 Sonnet vs DeepSeek-R1
DeepSeek-R1 is 81.7% cheaper for input tokens ($0.55 vs. $3.00 per 1M tokens) and $2.19 vs. $15.00 for output tokens (5.5x cost difference). In terms of operational performance, Claude 3.7 Sonnet delivers faster response latency with 650ms TTFT (1150ms faster than DeepSeek-R1). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 49.2% for DeepSeek-R1.
Claude 3.7 Sonnet vs DeepSeek-V3
DeepSeek-V3 is 95.3% cheaper for input tokens ($0.14 vs. $3.00 per 1M tokens) and $0.28 vs. $15.00 for output tokens (21.4x cost difference). In terms of operational performance, DeepSeek-V3 delivers faster response latency with 340ms TTFT (310ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 42.0% for DeepSeek-V3.
Claude 3.7 Sonnet vs DeepSeek-V4 Flash
DeepSeek-V4 Flash is 95.3% cheaper for input tokens ($0.14 vs. $3.00 per 1M tokens) and $0.56 vs. $15.00 for output tokens (21.4x cost difference). In terms of operational performance, DeepSeek-V4 Flash delivers faster response latency with 150ms TTFT (500ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 62.4% for DeepSeek-V4 Flash.
Claude 3.7 Sonnet vs Fable 5
Fable 5 is 33.3% cheaper for input tokens ($2.00 vs. $3.00 per 1M tokens) and $8.00 vs. $15.00 for output tokens (1.5x cost difference). In terms of operational performance, Fable 5 delivers faster response latency with 240ms TTFT (410ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 58.0% for Fable 5.
Claude 3.7 Sonnet vs Gemini 2.0 Flash
Gemini 2.0 Flash is 96.7% cheaper for input tokens ($0.10 vs. $3.00 per 1M tokens) and $0.40 vs. $15.00 for output tokens (30.0x cost difference). In terms of operational performance, Gemini 2.0 Flash delivers faster response latency with 380ms TTFT (270ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 48.0% for Gemini 2.0 Flash.
Claude 3.7 Sonnet vs Gemini 3.7 Flash
Gemini 3.7 Flash is 97.3% cheaper for input tokens ($0.08 vs. $3.00 per 1M tokens) and $0.32 vs. $15.00 for output tokens (37.5x cost difference). In terms of operational performance, Gemini 3.7 Flash delivers faster response latency with 75ms TTFT (575ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 68.2% for Gemini 3.7 Flash.
Claude 3.7 Sonnet vs GLM 5.3 Flash
GLM 5.3 Flash is 95.0% cheaper for input tokens ($0.15 vs. $3.00 per 1M tokens) and $0.50 vs. $15.00 for output tokens (20.0x cost difference). In terms of operational performance, GLM 5.3 Flash delivers faster response latency with 130ms TTFT (520ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 54.2% for GLM 5.3 Flash.
Claude 3.7 Sonnet vs OpenAI GPT-4o
OpenAI GPT-4o is 16.7% cheaper for input tokens ($2.50 vs. $3.00 per 1M tokens) and $10.00 vs. $15.00 for output tokens (1.2x cost difference). In terms of operational performance, OpenAI GPT-4o delivers faster response latency with 280ms TTFT (370ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 38.8% for OpenAI GPT-4o.
Claude 3.7 Sonnet vs GPT-5.6 Luna
GPT-5.6 Luna is 94.0% cheaper for input tokens ($0.18 vs. $3.00 per 1M tokens) and $0.72 vs. $15.00 for output tokens (16.7x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (560ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 48.5% for GPT-5.6 Luna.
Claude 3.7 Sonnet vs GPT-5.6 Sol
Claude 3.7 Sonnet is 62.5% cheaper for input tokens ($3.00 vs. $8.00 per 1M tokens) and $15.00 vs. $32.00 for output tokens (2.7x cost difference). In terms of operational performance, GPT-5.6 Sol delivers faster response latency with 420ms TTFT (230ms faster than Claude 3.7 Sonnet). GPT-5.6 Sol leads coding benchmarks at 79.5% SWE-bench vs. 70.3% for Claude 3.7 Sonnet.
Claude 3.7 Sonnet vs GPT-5.6 Terra
GPT-5.6 Terra is 50.0% cheaper for input tokens ($1.50 vs. $3.00 per 1M tokens) and $6.00 vs. $15.00 for output tokens (2.0x cost difference). In terms of operational performance, GPT-5.6 Terra delivers faster response latency with 210ms TTFT (440ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 65.4% for GPT-5.6 Terra.
Claude 3.7 Sonnet vs Grok 3
Both models share identical input pricing at $3.00 per 1M tokens. In terms of operational performance, Claude 3.7 Sonnet delivers faster response latency with 650ms TTFT (200ms faster than Grok 3). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 58.5% for Grok 3.
Claude 3.7 Sonnet vs xAI Grok 4.6
xAI Grok 4.6 is 33.3% cheaper for input tokens ($2.00 vs. $3.00 per 1M tokens) and $6.00 vs. $15.00 for output tokens (1.5x cost difference). In terms of operational performance, xAI Grok 4.6 delivers faster response latency with 280ms TTFT (370ms faster than Claude 3.7 Sonnet). xAI Grok 4.6 leads coding benchmarks at 76.8% SWE-bench vs. 70.3% for Claude 3.7 Sonnet.
Claude 3.7 Sonnet vs Llama 3.3 70B Instruct
Llama 3.3 70B Instruct is 94.0% cheaper for input tokens ($0.18 vs. $3.00 per 1M tokens) and $0.40 vs. $15.00 for output tokens (16.7x cost difference). In terms of operational performance, Llama 3.3 70B Instruct delivers faster response latency with 420ms TTFT (230ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Claude 3.7 Sonnet vs Mistral Large 2
Mistral Large 2 is 33.3% cheaper for input tokens ($2.00 vs. $3.00 per 1M tokens) and $6.00 vs. $15.00 for output tokens (1.5x cost difference). In terms of operational performance, Mistral Large 2 delivers faster response latency with 550ms TTFT (100ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 39.0% for Mistral Large 2.
Claude 3.7 Sonnet vs OpenAI o1
Claude 3.7 Sonnet is 80.0% cheaper for input tokens ($3.00 vs. $15.00 per 1M tokens) and $15.00 vs. $60.00 for output tokens (5.0x cost difference). In terms of operational performance, Claude 3.7 Sonnet delivers faster response latency with 650ms TTFT (200ms faster than OpenAI o1). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 48.9% for OpenAI o1.
Claude 3.7 Sonnet vs o3-mini
o3-mini is 63.3% cheaper for input tokens ($1.10 vs. $3.00 per 1M tokens) and $4.40 vs. $15.00 for output tokens (2.7x cost difference). In terms of operational performance, Claude 3.7 Sonnet delivers faster response latency with 650ms TTFT (550ms faster than o3-mini). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 49.3% for o3-mini.
Claude 3.7 Sonnet vs Microsoft Phi-4 (14B)
Microsoft Phi-4 (14B) is 96.0% cheaper for input tokens ($0.12 vs. $3.00 per 1M tokens) and $0.36 vs. $15.00 for output tokens (25.0x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (540ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
Claude 3.7 Sonnet vs Qwen 2.5 72B Instruct
Qwen 2.5 72B Instruct is 88.3% cheaper for input tokens ($0.35 vs. $3.00 per 1M tokens) and $0.40 vs. $15.00 for output tokens (8.6x cost difference). In terms of operational performance, Qwen 2.5 72B Instruct delivers faster response latency with 420ms TTFT (230ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
Claude 3.7 Sonnet vs Qwen 2.5 Max
Qwen 2.5 Max is 90.7% cheaper for input tokens ($0.28 vs. $3.00 per 1M tokens) and $0.84 vs. $15.00 for output tokens (10.7x cost difference). In terms of operational performance, Qwen 2.5 Max delivers faster response latency with 480ms TTFT (170ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 44.2% for Qwen 2.5 Max.
Claude 3.7 Sonnet vs Qwen 3.8 Flash Next
Qwen 3.8 Flash Next is 96.0% cheaper for input tokens ($0.12 vs. $3.00 per 1M tokens) and $0.48 vs. $15.00 for output tokens (25.0x cost difference). In terms of operational performance, Qwen 3.8 Flash Next delivers faster response latency with 120ms TTFT (530ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 56.8% for Qwen 3.8 Flash Next.
Claude Opus 5 vs Codestral 25.01
Codestral 25.01 is 94.0% cheaper for input tokens ($0.30 vs. $5.00 per 1M tokens) and $0.90 vs. $25.00 for output tokens (16.7x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (190ms faster than Claude Opus 5). Claude Opus 5 leads coding benchmarks at 82.4% SWE-bench vs. 44.2% for Codestral 25.01.
Claude Opus 5 vs Composer 2.5
Composer 2.5 is 64.0% cheaper for input tokens ($1.80 vs. $5.00 per 1M tokens) and $7.20 vs. $25.00 for output tokens (2.8x cost difference). In terms of operational performance, Composer 2.5 delivers faster response latency with 180ms TTFT (160ms faster than Claude Opus 5). Claude Opus 5 leads coding benchmarks at 82.4% SWE-bench vs. 74.6% for Composer 2.5.
Claude Opus 5 vs DeepSeek-R1
DeepSeek-R1 is 89.0% cheaper for input tokens ($0.55 vs. $5.00 per 1M tokens) and $2.19 vs. $25.00 for output tokens (9.1x cost difference). In terms of operational performance, Claude Opus 5 delivers faster response latency with 340ms TTFT (1460ms faster than DeepSeek-R1). Claude Opus 5 leads coding benchmarks at 82.4% SWE-bench vs. 49.2% for DeepSeek-R1.
Claude Opus 5 vs DeepSeek-V3
DeepSeek-V3 is 97.2% cheaper for input tokens ($0.14 vs. $5.00 per 1M tokens) and $0.28 vs. $25.00 for output tokens (35.7x cost difference). In terms of operational performance, DeepSeek-V3 delivers faster response latency with 340ms TTFT (0ms faster than Claude Opus 5). Claude Opus 5 leads coding benchmarks at 82.4% SWE-bench vs. 42.0% for DeepSeek-V3.
Claude Opus 5 vs DeepSeek-V4 Flash
DeepSeek-V4 Flash is 97.2% cheaper for input tokens ($0.14 vs. $5.00 per 1M tokens) and $0.56 vs. $25.00 for output tokens (35.7x cost difference). In terms of operational performance, DeepSeek-V4 Flash delivers faster response latency with 150ms TTFT (190ms faster than Claude Opus 5). Claude Opus 5 leads coding benchmarks at 82.4% SWE-bench vs. 62.4% for DeepSeek-V4 Flash.
Claude Opus 5 vs Fable 5
Fable 5 is 60.0% cheaper for input tokens ($2.00 vs. $5.00 per 1M tokens) and $8.00 vs. $25.00 for output tokens (2.5x cost difference). In terms of operational performance, Fable 5 delivers faster response latency with 240ms TTFT (100ms faster than Claude Opus 5). Claude Opus 5 leads coding benchmarks at 82.4% SWE-bench vs. 58.0% for Fable 5.
Claude Opus 5 vs Gemini 2.0 Flash
Gemini 2.0 Flash is 98.0% cheaper for input tokens ($0.10 vs. $5.00 per 1M tokens) and $0.40 vs. $25.00 for output tokens (50.0x cost difference). In terms of operational performance, Claude Opus 5 delivers faster response latency with 340ms TTFT (40ms faster than Gemini 2.0 Flash). Claude Opus 5 leads coding benchmarks at 82.4% SWE-bench vs. 48.0% for Gemini 2.0 Flash.
Claude Opus 5 vs Gemini 3.7 Flash
Gemini 3.7 Flash is 98.4% cheaper for input tokens ($0.08 vs. $5.00 per 1M tokens) and $0.32 vs. $25.00 for output tokens (62.5x cost difference). In terms of operational performance, Gemini 3.7 Flash delivers faster response latency with 75ms TTFT (265ms faster than Claude Opus 5). Claude Opus 5 leads coding benchmarks at 82.4% SWE-bench vs. 68.2% for Gemini 3.7 Flash.
Claude Opus 5 vs GLM 5.3 Flash
GLM 5.3 Flash is 97.0% cheaper for input tokens ($0.15 vs. $5.00 per 1M tokens) and $0.50 vs. $25.00 for output tokens (33.3x cost difference). In terms of operational performance, GLM 5.3 Flash delivers faster response latency with 130ms TTFT (210ms faster than Claude Opus 5). Claude Opus 5 leads coding benchmarks at 82.4% SWE-bench vs. 54.2% for GLM 5.3 Flash.
Claude Opus 5 vs OpenAI GPT-4o
OpenAI GPT-4o is 50.0% cheaper for input tokens ($2.50 vs. $5.00 per 1M tokens) and $10.00 vs. $25.00 for output tokens (2.0x cost difference). In terms of operational performance, OpenAI GPT-4o delivers faster response latency with 280ms TTFT (60ms faster than Claude Opus 5). Claude Opus 5 leads coding benchmarks at 82.4% SWE-bench vs. 38.8% for OpenAI GPT-4o.
Claude Opus 5 vs GPT-5.6 Luna
GPT-5.6 Luna is 96.4% cheaper for input tokens ($0.18 vs. $5.00 per 1M tokens) and $0.72 vs. $25.00 for output tokens (27.8x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (250ms faster than Claude Opus 5). Claude Opus 5 leads coding benchmarks at 82.4% SWE-bench vs. 48.5% for GPT-5.6 Luna.
Claude Opus 5 vs GPT-5.6 Sol
Claude Opus 5 is 37.5% cheaper for input tokens ($5.00 vs. $8.00 per 1M tokens) and $25.00 vs. $32.00 for output tokens (1.6x cost difference). In terms of operational performance, Claude Opus 5 delivers faster response latency with 340ms TTFT (80ms faster than GPT-5.6 Sol). Claude Opus 5 leads coding benchmarks at 82.4% SWE-bench vs. 79.5% for GPT-5.6 Sol.
Claude Opus 5 vs GPT-5.6 Terra
GPT-5.6 Terra is 70.0% cheaper for input tokens ($1.50 vs. $5.00 per 1M tokens) and $6.00 vs. $25.00 for output tokens (3.3x cost difference). In terms of operational performance, GPT-5.6 Terra delivers faster response latency with 210ms TTFT (130ms faster than Claude Opus 5). Claude Opus 5 leads coding benchmarks at 82.4% SWE-bench vs. 65.4% for GPT-5.6 Terra.
Claude Opus 5 vs Grok 3
Grok 3 is 40.0% cheaper for input tokens ($3.00 vs. $5.00 per 1M tokens) and $15.00 vs. $25.00 for output tokens (1.7x cost difference). In terms of operational performance, Claude Opus 5 delivers faster response latency with 340ms TTFT (510ms faster than Grok 3). Claude Opus 5 leads coding benchmarks at 82.4% SWE-bench vs. 58.5% for Grok 3.
Claude Opus 5 vs xAI Grok 4.6
xAI Grok 4.6 is 60.0% cheaper for input tokens ($2.00 vs. $5.00 per 1M tokens) and $6.00 vs. $25.00 for output tokens (2.5x cost difference). In terms of operational performance, xAI Grok 4.6 delivers faster response latency with 280ms TTFT (60ms faster than Claude Opus 5). Claude Opus 5 leads coding benchmarks at 82.4% SWE-bench vs. 76.8% for xAI Grok 4.6.
Claude Opus 5 vs Llama 3.3 70B Instruct
Llama 3.3 70B Instruct is 96.4% cheaper for input tokens ($0.18 vs. $5.00 per 1M tokens) and $0.40 vs. $25.00 for output tokens (27.8x cost difference). In terms of operational performance, Claude Opus 5 delivers faster response latency with 340ms TTFT (80ms faster than Llama 3.3 70B Instruct). Claude Opus 5 leads coding benchmarks at 82.4% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Claude Opus 5 vs Mistral Large 2
Mistral Large 2 is 60.0% cheaper for input tokens ($2.00 vs. $5.00 per 1M tokens) and $6.00 vs. $25.00 for output tokens (2.5x cost difference). In terms of operational performance, Claude Opus 5 delivers faster response latency with 340ms TTFT (210ms faster than Mistral Large 2). Claude Opus 5 leads coding benchmarks at 82.4% SWE-bench vs. 39.0% for Mistral Large 2.
Claude Opus 5 vs OpenAI o1
Claude Opus 5 is 66.7% cheaper for input tokens ($5.00 vs. $15.00 per 1M tokens) and $25.00 vs. $60.00 for output tokens (3.0x cost difference). In terms of operational performance, Claude Opus 5 delivers faster response latency with 340ms TTFT (510ms faster than OpenAI o1). Claude Opus 5 leads coding benchmarks at 82.4% SWE-bench vs. 48.9% for OpenAI o1.
Claude Opus 5 vs o3-mini
o3-mini is 78.0% cheaper for input tokens ($1.10 vs. $5.00 per 1M tokens) and $4.40 vs. $25.00 for output tokens (4.5x cost difference). In terms of operational performance, Claude Opus 5 delivers faster response latency with 340ms TTFT (860ms faster than o3-mini). Claude Opus 5 leads coding benchmarks at 82.4% SWE-bench vs. 49.3% for o3-mini.
Claude Opus 5 vs Microsoft Phi-4 (14B)
Microsoft Phi-4 (14B) is 97.6% cheaper for input tokens ($0.12 vs. $5.00 per 1M tokens) and $0.36 vs. $25.00 for output tokens (41.7x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (230ms faster than Claude Opus 5). Claude Opus 5 leads coding benchmarks at 82.4% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
Claude Opus 5 vs Qwen 2.5 72B Instruct
Qwen 2.5 72B Instruct is 93.0% cheaper for input tokens ($0.35 vs. $5.00 per 1M tokens) and $0.40 vs. $25.00 for output tokens (14.3x cost difference). In terms of operational performance, Claude Opus 5 delivers faster response latency with 340ms TTFT (80ms faster than Qwen 2.5 72B Instruct). Claude Opus 5 leads coding benchmarks at 82.4% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
Claude Opus 5 vs Qwen 2.5 Max
Qwen 2.5 Max is 94.4% cheaper for input tokens ($0.28 vs. $5.00 per 1M tokens) and $0.84 vs. $25.00 for output tokens (17.9x cost difference). In terms of operational performance, Claude Opus 5 delivers faster response latency with 340ms TTFT (140ms faster than Qwen 2.5 Max). Claude Opus 5 leads coding benchmarks at 82.4% SWE-bench vs. 44.2% for Qwen 2.5 Max.
Claude Opus 5 vs Qwen 3.8 Flash Next
Qwen 3.8 Flash Next is 97.6% cheaper for input tokens ($0.12 vs. $5.00 per 1M tokens) and $0.48 vs. $25.00 for output tokens (41.7x cost difference). In terms of operational performance, Qwen 3.8 Flash Next delivers faster response latency with 120ms TTFT (220ms faster than Claude Opus 5). Claude Opus 5 leads coding benchmarks at 82.4% SWE-bench vs. 56.8% for Qwen 3.8 Flash Next.
Codestral 25.01 vs Composer 2.5
Codestral 25.01 is 83.3% cheaper for input tokens ($0.30 vs. $1.80 per 1M tokens) and $0.90 vs. $7.20 for output tokens (6.0x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (30ms faster than Composer 2.5). Composer 2.5 leads coding benchmarks at 74.6% SWE-bench vs. 44.2% for Codestral 25.01.
Codestral 25.01 vs DeepSeek-R1
Codestral 25.01 is 45.5% cheaper for input tokens ($0.30 vs. $0.55 per 1M tokens) and $0.90 vs. $2.19 for output tokens (1.8x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (1650ms faster than DeepSeek-R1). DeepSeek-R1 leads coding benchmarks at 49.2% SWE-bench vs. 44.2% for Codestral 25.01.
Codestral 25.01 vs DeepSeek-V3
DeepSeek-V3 is 53.3% cheaper for input tokens ($0.14 vs. $0.30 per 1M tokens) and $0.28 vs. $0.90 for output tokens (2.1x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (190ms faster than DeepSeek-V3). Codestral 25.01 leads coding benchmarks at 44.2% SWE-bench vs. 42.0% for DeepSeek-V3.
Codestral 25.01 vs DeepSeek-V4 Flash
DeepSeek-V4 Flash is 53.3% cheaper for input tokens ($0.14 vs. $0.30 per 1M tokens) and $0.56 vs. $0.90 for output tokens (2.1x cost difference). In terms of operational performance, DeepSeek-V4 Flash delivers faster response latency with 150ms TTFT (0ms faster than Codestral 25.01). DeepSeek-V4 Flash leads coding benchmarks at 62.4% SWE-bench vs. 44.2% for Codestral 25.01.
Codestral 25.01 vs Fable 5
Codestral 25.01 is 85.0% cheaper for input tokens ($0.30 vs. $2.00 per 1M tokens) and $0.90 vs. $8.00 for output tokens (6.7x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (90ms faster than Fable 5). Fable 5 leads coding benchmarks at 58.0% SWE-bench vs. 44.2% for Codestral 25.01.
Codestral 25.01 vs Gemini 2.0 Flash
Gemini 2.0 Flash is 66.7% cheaper for input tokens ($0.10 vs. $0.30 per 1M tokens) and $0.40 vs. $0.90 for output tokens (3.0x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (230ms faster than Gemini 2.0 Flash). Gemini 2.0 Flash leads coding benchmarks at 48.0% SWE-bench vs. 44.2% for Codestral 25.01.
Codestral 25.01 vs Gemini 3.7 Flash
Gemini 3.7 Flash is 73.3% cheaper for input tokens ($0.08 vs. $0.30 per 1M tokens) and $0.32 vs. $0.90 for output tokens (3.8x cost difference). In terms of operational performance, Gemini 3.7 Flash delivers faster response latency with 75ms TTFT (75ms faster than Codestral 25.01). Gemini 3.7 Flash leads coding benchmarks at 68.2% SWE-bench vs. 44.2% for Codestral 25.01.
Codestral 25.01 vs GLM 5.3 Flash
GLM 5.3 Flash is 50.0% cheaper for input tokens ($0.15 vs. $0.30 per 1M tokens) and $0.50 vs. $0.90 for output tokens (2.0x cost difference). In terms of operational performance, GLM 5.3 Flash delivers faster response latency with 130ms TTFT (20ms faster than Codestral 25.01). GLM 5.3 Flash leads coding benchmarks at 54.2% SWE-bench vs. 44.2% for Codestral 25.01.
Codestral 25.01 vs OpenAI GPT-4o
Codestral 25.01 is 88.0% cheaper for input tokens ($0.30 vs. $2.50 per 1M tokens) and $0.90 vs. $10.00 for output tokens (8.3x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (130ms faster than OpenAI GPT-4o). Codestral 25.01 leads coding benchmarks at 44.2% SWE-bench vs. 38.8% for OpenAI GPT-4o.
Codestral 25.01 vs GPT-5.6 Luna
GPT-5.6 Luna is 40.0% cheaper for input tokens ($0.18 vs. $0.30 per 1M tokens) and $0.72 vs. $0.90 for output tokens (1.7x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (60ms faster than Codestral 25.01). GPT-5.6 Luna leads coding benchmarks at 48.5% SWE-bench vs. 44.2% for Codestral 25.01.
Codestral 25.01 vs GPT-5.6 Sol
Codestral 25.01 is 96.2% cheaper for input tokens ($0.30 vs. $8.00 per 1M tokens) and $0.90 vs. $32.00 for output tokens (26.7x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (270ms faster than GPT-5.6 Sol). GPT-5.6 Sol leads coding benchmarks at 79.5% SWE-bench vs. 44.2% for Codestral 25.01.
Codestral 25.01 vs GPT-5.6 Terra
Codestral 25.01 is 80.0% cheaper for input tokens ($0.30 vs. $1.50 per 1M tokens) and $0.90 vs. $6.00 for output tokens (5.0x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (60ms faster than GPT-5.6 Terra). GPT-5.6 Terra leads coding benchmarks at 65.4% SWE-bench vs. 44.2% for Codestral 25.01.
Codestral 25.01 vs Grok 3
Codestral 25.01 is 90.0% cheaper for input tokens ($0.30 vs. $3.00 per 1M tokens) and $0.90 vs. $15.00 for output tokens (10.0x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (700ms faster than Grok 3). Grok 3 leads coding benchmarks at 58.5% SWE-bench vs. 44.2% for Codestral 25.01.
Codestral 25.01 vs xAI Grok 4.6
Codestral 25.01 is 85.0% cheaper for input tokens ($0.30 vs. $2.00 per 1M tokens) and $0.90 vs. $6.00 for output tokens (6.7x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (130ms faster than xAI Grok 4.6). xAI Grok 4.6 leads coding benchmarks at 76.8% SWE-bench vs. 44.2% for Codestral 25.01.
Codestral 25.01 vs Llama 3.3 70B Instruct
Llama 3.3 70B Instruct is 40.0% cheaper for input tokens ($0.18 vs. $0.30 per 1M tokens) and $0.40 vs. $0.90 for output tokens (1.7x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (270ms faster than Llama 3.3 70B Instruct). Codestral 25.01 leads coding benchmarks at 44.2% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Codestral 25.01 vs Mistral Large 2
Codestral 25.01 is 85.0% cheaper for input tokens ($0.30 vs. $2.00 per 1M tokens) and $0.90 vs. $6.00 for output tokens (6.7x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (400ms faster than Mistral Large 2). Codestral 25.01 leads coding benchmarks at 44.2% SWE-bench vs. 39.0% for Mistral Large 2.
Codestral 25.01 vs OpenAI o1
Codestral 25.01 is 98.0% cheaper for input tokens ($0.30 vs. $15.00 per 1M tokens) and $0.90 vs. $60.00 for output tokens (50.0x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (700ms faster than OpenAI o1). OpenAI o1 leads coding benchmarks at 48.9% SWE-bench vs. 44.2% for Codestral 25.01.
Codestral 25.01 vs o3-mini
Codestral 25.01 is 72.7% cheaper for input tokens ($0.30 vs. $1.10 per 1M tokens) and $0.90 vs. $4.40 for output tokens (3.7x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (1050ms faster than o3-mini). o3-mini leads coding benchmarks at 49.3% SWE-bench vs. 44.2% for Codestral 25.01.
Codestral 25.01 vs Microsoft Phi-4 (14B)
Microsoft Phi-4 (14B) is 60.0% cheaper for input tokens ($0.12 vs. $0.30 per 1M tokens) and $0.36 vs. $0.90 for output tokens (2.5x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (40ms faster than Codestral 25.01). Codestral 25.01 leads coding benchmarks at 44.2% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
Codestral 25.01 vs Qwen 2.5 72B Instruct
Codestral 25.01 is 14.3% cheaper for input tokens ($0.30 vs. $0.35 per 1M tokens) and $0.90 vs. $0.40 for output tokens (1.2x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (270ms faster than Qwen 2.5 72B Instruct). Codestral 25.01 leads coding benchmarks at 44.2% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
Codestral 25.01 vs Qwen 2.5 Max
Qwen 2.5 Max is 6.7% cheaper for input tokens ($0.28 vs. $0.30 per 1M tokens) and $0.84 vs. $0.90 for output tokens (1.1x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (330ms faster than Qwen 2.5 Max). Both models demonstrate comparable coding benchmark scores.
Codestral 25.01 vs Qwen 3.8 Flash Next
Qwen 3.8 Flash Next is 60.0% cheaper for input tokens ($0.12 vs. $0.30 per 1M tokens) and $0.48 vs. $0.90 for output tokens (2.5x cost difference). In terms of operational performance, Qwen 3.8 Flash Next delivers faster response latency with 120ms TTFT (30ms faster than Codestral 25.01). Qwen 3.8 Flash Next leads coding benchmarks at 56.8% SWE-bench vs. 44.2% for Codestral 25.01.
Composer 2.5 vs DeepSeek-R1
DeepSeek-R1 is 69.4% cheaper for input tokens ($0.55 vs. $1.80 per 1M tokens) and $2.19 vs. $7.20 for output tokens (3.3x cost difference). In terms of operational performance, Composer 2.5 delivers faster response latency with 180ms TTFT (1620ms faster than DeepSeek-R1). Composer 2.5 leads coding benchmarks at 74.6% SWE-bench vs. 49.2% for DeepSeek-R1.
Composer 2.5 vs DeepSeek-V3
DeepSeek-V3 is 92.2% cheaper for input tokens ($0.14 vs. $1.80 per 1M tokens) and $0.28 vs. $7.20 for output tokens (12.9x cost difference). In terms of operational performance, Composer 2.5 delivers faster response latency with 180ms TTFT (160ms faster than DeepSeek-V3). Composer 2.5 leads coding benchmarks at 74.6% SWE-bench vs. 42.0% for DeepSeek-V3.
Composer 2.5 vs DeepSeek-V4 Flash
DeepSeek-V4 Flash is 92.2% cheaper for input tokens ($0.14 vs. $1.80 per 1M tokens) and $0.56 vs. $7.20 for output tokens (12.9x cost difference). In terms of operational performance, DeepSeek-V4 Flash delivers faster response latency with 150ms TTFT (30ms faster than Composer 2.5). Composer 2.5 leads coding benchmarks at 74.6% SWE-bench vs. 62.4% for DeepSeek-V4 Flash.
Composer 2.5 vs Fable 5
Composer 2.5 is 10.0% cheaper for input tokens ($1.80 vs. $2.00 per 1M tokens) and $7.20 vs. $8.00 for output tokens (1.1x cost difference). In terms of operational performance, Composer 2.5 delivers faster response latency with 180ms TTFT (60ms faster than Fable 5). Composer 2.5 leads coding benchmarks at 74.6% SWE-bench vs. 58.0% for Fable 5.
Composer 2.5 vs Gemini 2.0 Flash
Gemini 2.0 Flash is 94.4% cheaper for input tokens ($0.10 vs. $1.80 per 1M tokens) and $0.40 vs. $7.20 for output tokens (18.0x cost difference). In terms of operational performance, Composer 2.5 delivers faster response latency with 180ms TTFT (200ms faster than Gemini 2.0 Flash). Composer 2.5 leads coding benchmarks at 74.6% SWE-bench vs. 48.0% for Gemini 2.0 Flash.
Composer 2.5 vs Gemini 3.7 Flash
Gemini 3.7 Flash is 95.6% cheaper for input tokens ($0.08 vs. $1.80 per 1M tokens) and $0.32 vs. $7.20 for output tokens (22.5x cost difference). In terms of operational performance, Gemini 3.7 Flash delivers faster response latency with 75ms TTFT (105ms faster than Composer 2.5). Composer 2.5 leads coding benchmarks at 74.6% SWE-bench vs. 68.2% for Gemini 3.7 Flash.
Composer 2.5 vs GLM 5.3 Flash
GLM 5.3 Flash is 91.7% cheaper for input tokens ($0.15 vs. $1.80 per 1M tokens) and $0.50 vs. $7.20 for output tokens (12.0x cost difference). In terms of operational performance, GLM 5.3 Flash delivers faster response latency with 130ms TTFT (50ms faster than Composer 2.5). Composer 2.5 leads coding benchmarks at 74.6% SWE-bench vs. 54.2% for GLM 5.3 Flash.
Composer 2.5 vs OpenAI GPT-4o
Composer 2.5 is 28.0% cheaper for input tokens ($1.80 vs. $2.50 per 1M tokens) and $7.20 vs. $10.00 for output tokens (1.4x cost difference). In terms of operational performance, Composer 2.5 delivers faster response latency with 180ms TTFT (100ms faster than OpenAI GPT-4o). Composer 2.5 leads coding benchmarks at 74.6% SWE-bench vs. 38.8% for OpenAI GPT-4o.
Composer 2.5 vs GPT-5.6 Luna
GPT-5.6 Luna is 90.0% cheaper for input tokens ($0.18 vs. $1.80 per 1M tokens) and $0.72 vs. $7.20 for output tokens (10.0x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (90ms faster than Composer 2.5). Composer 2.5 leads coding benchmarks at 74.6% SWE-bench vs. 48.5% for GPT-5.6 Luna.
Composer 2.5 vs GPT-5.6 Sol
Composer 2.5 is 77.5% cheaper for input tokens ($1.80 vs. $8.00 per 1M tokens) and $7.20 vs. $32.00 for output tokens (4.4x cost difference). In terms of operational performance, Composer 2.5 delivers faster response latency with 180ms TTFT (240ms faster than GPT-5.6 Sol). GPT-5.6 Sol leads coding benchmarks at 79.5% SWE-bench vs. 74.6% for Composer 2.5.
Composer 2.5 vs GPT-5.6 Terra
GPT-5.6 Terra is 16.7% cheaper for input tokens ($1.50 vs. $1.80 per 1M tokens) and $6.00 vs. $7.20 for output tokens (1.2x cost difference). In terms of operational performance, Composer 2.5 delivers faster response latency with 180ms TTFT (30ms faster than GPT-5.6 Terra). Composer 2.5 leads coding benchmarks at 74.6% SWE-bench vs. 65.4% for GPT-5.6 Terra.
Composer 2.5 vs Grok 3
Composer 2.5 is 40.0% cheaper for input tokens ($1.80 vs. $3.00 per 1M tokens) and $7.20 vs. $15.00 for output tokens (1.7x cost difference). In terms of operational performance, Composer 2.5 delivers faster response latency with 180ms TTFT (670ms faster than Grok 3). Composer 2.5 leads coding benchmarks at 74.6% SWE-bench vs. 58.5% for Grok 3.
Composer 2.5 vs xAI Grok 4.6
Composer 2.5 is 10.0% cheaper for input tokens ($1.80 vs. $2.00 per 1M tokens) and $7.20 vs. $6.00 for output tokens (1.1x cost difference). In terms of operational performance, Composer 2.5 delivers faster response latency with 180ms TTFT (100ms faster than xAI Grok 4.6). xAI Grok 4.6 leads coding benchmarks at 76.8% SWE-bench vs. 74.6% for Composer 2.5.
Composer 2.5 vs Llama 3.3 70B Instruct
Llama 3.3 70B Instruct is 90.0% cheaper for input tokens ($0.18 vs. $1.80 per 1M tokens) and $0.40 vs. $7.20 for output tokens (10.0x cost difference). In terms of operational performance, Composer 2.5 delivers faster response latency with 180ms TTFT (240ms faster than Llama 3.3 70B Instruct). Composer 2.5 leads coding benchmarks at 74.6% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Composer 2.5 vs Mistral Large 2
Composer 2.5 is 10.0% cheaper for input tokens ($1.80 vs. $2.00 per 1M tokens) and $7.20 vs. $6.00 for output tokens (1.1x cost difference). In terms of operational performance, Composer 2.5 delivers faster response latency with 180ms TTFT (370ms faster than Mistral Large 2). Composer 2.5 leads coding benchmarks at 74.6% SWE-bench vs. 39.0% for Mistral Large 2.
Composer 2.5 vs OpenAI o1
Composer 2.5 is 88.0% cheaper for input tokens ($1.80 vs. $15.00 per 1M tokens) and $7.20 vs. $60.00 for output tokens (8.3x cost difference). In terms of operational performance, Composer 2.5 delivers faster response latency with 180ms TTFT (670ms faster than OpenAI o1). Composer 2.5 leads coding benchmarks at 74.6% SWE-bench vs. 48.9% for OpenAI o1.
Composer 2.5 vs o3-mini
o3-mini is 38.9% cheaper for input tokens ($1.10 vs. $1.80 per 1M tokens) and $4.40 vs. $7.20 for output tokens (1.6x cost difference). In terms of operational performance, Composer 2.5 delivers faster response latency with 180ms TTFT (1020ms faster than o3-mini). Composer 2.5 leads coding benchmarks at 74.6% SWE-bench vs. 49.3% for o3-mini.
Composer 2.5 vs Microsoft Phi-4 (14B)
Microsoft Phi-4 (14B) is 93.3% cheaper for input tokens ($0.12 vs. $1.80 per 1M tokens) and $0.36 vs. $7.20 for output tokens (15.0x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (70ms faster than Composer 2.5). Composer 2.5 leads coding benchmarks at 74.6% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
Composer 2.5 vs Qwen 2.5 72B Instruct
Qwen 2.5 72B Instruct is 80.6% cheaper for input tokens ($0.35 vs. $1.80 per 1M tokens) and $0.40 vs. $7.20 for output tokens (5.1x cost difference). In terms of operational performance, Composer 2.5 delivers faster response latency with 180ms TTFT (240ms faster than Qwen 2.5 72B Instruct). Composer 2.5 leads coding benchmarks at 74.6% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
Composer 2.5 vs Qwen 2.5 Max
Qwen 2.5 Max is 84.4% cheaper for input tokens ($0.28 vs. $1.80 per 1M tokens) and $0.84 vs. $7.20 for output tokens (6.4x cost difference). In terms of operational performance, Composer 2.5 delivers faster response latency with 180ms TTFT (300ms faster than Qwen 2.5 Max). Composer 2.5 leads coding benchmarks at 74.6% SWE-bench vs. 44.2% for Qwen 2.5 Max.
Composer 2.5 vs Qwen 3.8 Flash Next
Qwen 3.8 Flash Next is 93.3% cheaper for input tokens ($0.12 vs. $1.80 per 1M tokens) and $0.48 vs. $7.20 for output tokens (15.0x cost difference). In terms of operational performance, Qwen 3.8 Flash Next delivers faster response latency with 120ms TTFT (60ms faster than Composer 2.5). Composer 2.5 leads coding benchmarks at 74.6% SWE-bench vs. 56.8% for Qwen 3.8 Flash Next.
DeepSeek-R1 vs DeepSeek-V3
DeepSeek-V3 is 74.5% cheaper for input tokens ($0.14 vs. $0.55 per 1M tokens) and $0.28 vs. $2.19 for output tokens (3.9x cost difference). In terms of operational performance, DeepSeek-V3 delivers faster response latency with 340ms TTFT (1460ms faster than DeepSeek-R1). DeepSeek-R1 leads coding benchmarks at 49.2% SWE-bench vs. 42.0% for DeepSeek-V3.
DeepSeek-R1 vs DeepSeek-V4 Flash
DeepSeek-V4 Flash is 74.5% cheaper for input tokens ($0.14 vs. $0.55 per 1M tokens) and $0.56 vs. $2.19 for output tokens (3.9x cost difference). In terms of operational performance, DeepSeek-V4 Flash delivers faster response latency with 150ms TTFT (1650ms faster than DeepSeek-R1). DeepSeek-V4 Flash leads coding benchmarks at 62.4% SWE-bench vs. 49.2% for DeepSeek-R1.
DeepSeek-R1 vs Fable 5
DeepSeek-R1 is 72.5% cheaper for input tokens ($0.55 vs. $2.00 per 1M tokens) and $2.19 vs. $8.00 for output tokens (3.6x cost difference). In terms of operational performance, Fable 5 delivers faster response latency with 240ms TTFT (1560ms faster than DeepSeek-R1). Fable 5 leads coding benchmarks at 58.0% SWE-bench vs. 49.2% for DeepSeek-R1.
DeepSeek-R1 vs Gemini 2.0 Flash
Gemini 2.0 Flash is 81.8% cheaper for input tokens ($0.10 vs. $0.55 per 1M tokens) and $0.40 vs. $2.19 for output tokens (5.5x cost difference). In terms of operational performance, Gemini 2.0 Flash delivers faster response latency with 380ms TTFT (1420ms faster than DeepSeek-R1). DeepSeek-R1 leads coding benchmarks at 49.2% SWE-bench vs. 48.0% for Gemini 2.0 Flash.
DeepSeek-R1 vs Gemini 3.7 Flash
Gemini 3.7 Flash is 85.5% cheaper for input tokens ($0.08 vs. $0.55 per 1M tokens) and $0.32 vs. $2.19 for output tokens (6.9x cost difference). In terms of operational performance, Gemini 3.7 Flash delivers faster response latency with 75ms TTFT (1725ms faster than DeepSeek-R1). Gemini 3.7 Flash leads coding benchmarks at 68.2% SWE-bench vs. 49.2% for DeepSeek-R1.
DeepSeek-R1 vs GLM 5.3 Flash
GLM 5.3 Flash is 72.7% cheaper for input tokens ($0.15 vs. $0.55 per 1M tokens) and $0.50 vs. $2.19 for output tokens (3.7x cost difference). In terms of operational performance, GLM 5.3 Flash delivers faster response latency with 130ms TTFT (1670ms faster than DeepSeek-R1). GLM 5.3 Flash leads coding benchmarks at 54.2% SWE-bench vs. 49.2% for DeepSeek-R1.
DeepSeek-R1 vs OpenAI GPT-4o
DeepSeek-R1 is 78.0% cheaper for input tokens ($0.55 vs. $2.50 per 1M tokens) and $2.19 vs. $10.00 for output tokens (4.5x cost difference). In terms of operational performance, OpenAI GPT-4o delivers faster response latency with 280ms TTFT (1520ms faster than DeepSeek-R1). DeepSeek-R1 leads coding benchmarks at 49.2% SWE-bench vs. 38.8% for OpenAI GPT-4o.
DeepSeek-R1 vs GPT-5.6 Luna
GPT-5.6 Luna is 67.3% cheaper for input tokens ($0.18 vs. $0.55 per 1M tokens) and $0.72 vs. $2.19 for output tokens (3.1x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (1710ms faster than DeepSeek-R1). DeepSeek-R1 leads coding benchmarks at 49.2% SWE-bench vs. 48.5% for GPT-5.6 Luna.
DeepSeek-R1 vs GPT-5.6 Sol
DeepSeek-R1 is 93.1% cheaper for input tokens ($0.55 vs. $8.00 per 1M tokens) and $2.19 vs. $32.00 for output tokens (14.5x cost difference). In terms of operational performance, GPT-5.6 Sol delivers faster response latency with 420ms TTFT (1380ms faster than DeepSeek-R1). GPT-5.6 Sol leads coding benchmarks at 79.5% SWE-bench vs. 49.2% for DeepSeek-R1.
DeepSeek-R1 vs GPT-5.6 Terra
DeepSeek-R1 is 63.3% cheaper for input tokens ($0.55 vs. $1.50 per 1M tokens) and $2.19 vs. $6.00 for output tokens (2.7x cost difference). In terms of operational performance, GPT-5.6 Terra delivers faster response latency with 210ms TTFT (1590ms faster than DeepSeek-R1). GPT-5.6 Terra leads coding benchmarks at 65.4% SWE-bench vs. 49.2% for DeepSeek-R1.
DeepSeek-R1 vs Grok 3
DeepSeek-R1 is 81.7% cheaper for input tokens ($0.55 vs. $3.00 per 1M tokens) and $2.19 vs. $15.00 for output tokens (5.5x cost difference). In terms of operational performance, Grok 3 delivers faster response latency with 850ms TTFT (950ms faster than DeepSeek-R1). Grok 3 leads coding benchmarks at 58.5% SWE-bench vs. 49.2% for DeepSeek-R1.
DeepSeek-R1 vs xAI Grok 4.6
DeepSeek-R1 is 72.5% cheaper for input tokens ($0.55 vs. $2.00 per 1M tokens) and $2.19 vs. $6.00 for output tokens (3.6x cost difference). In terms of operational performance, xAI Grok 4.6 delivers faster response latency with 280ms TTFT (1520ms faster than DeepSeek-R1). xAI Grok 4.6 leads coding benchmarks at 76.8% SWE-bench vs. 49.2% for DeepSeek-R1.
DeepSeek-R1 vs Llama 3.3 70B Instruct
Llama 3.3 70B Instruct is 67.3% cheaper for input tokens ($0.18 vs. $0.55 per 1M tokens) and $0.40 vs. $2.19 for output tokens (3.1x cost difference). In terms of operational performance, Llama 3.3 70B Instruct delivers faster response latency with 420ms TTFT (1380ms faster than DeepSeek-R1). DeepSeek-R1 leads coding benchmarks at 49.2% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
DeepSeek-R1 vs Mistral Large 2
DeepSeek-R1 is 72.5% cheaper for input tokens ($0.55 vs. $2.00 per 1M tokens) and $2.19 vs. $6.00 for output tokens (3.6x cost difference). In terms of operational performance, Mistral Large 2 delivers faster response latency with 550ms TTFT (1250ms faster than DeepSeek-R1). DeepSeek-R1 leads coding benchmarks at 49.2% SWE-bench vs. 39.0% for Mistral Large 2.
DeepSeek-R1 vs OpenAI o1
DeepSeek-R1 is 96.3% cheaper for input tokens ($0.55 vs. $15.00 per 1M tokens) and $2.19 vs. $60.00 for output tokens (27.3x cost difference). In terms of operational performance, OpenAI o1 delivers faster response latency with 850ms TTFT (950ms faster than DeepSeek-R1). DeepSeek-R1 leads coding benchmarks at 49.2% SWE-bench vs. 48.9% for OpenAI o1.
DeepSeek-R1 vs o3-mini
DeepSeek-R1 is 50.0% cheaper for input tokens ($0.55 vs. $1.10 per 1M tokens) and $2.19 vs. $4.40 for output tokens (2.0x cost difference). In terms of operational performance, o3-mini delivers faster response latency with 1200ms TTFT (600ms faster than DeepSeek-R1). o3-mini leads coding benchmarks at 49.3% SWE-bench vs. 49.2% for DeepSeek-R1.
DeepSeek-R1 vs Microsoft Phi-4 (14B)
Microsoft Phi-4 (14B) is 78.2% cheaper for input tokens ($0.12 vs. $0.55 per 1M tokens) and $0.36 vs. $2.19 for output tokens (4.6x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (1690ms faster than DeepSeek-R1). DeepSeek-R1 leads coding benchmarks at 49.2% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
DeepSeek-R1 vs Qwen 2.5 72B Instruct
Qwen 2.5 72B Instruct is 36.4% cheaper for input tokens ($0.35 vs. $0.55 per 1M tokens) and $0.40 vs. $2.19 for output tokens (1.6x cost difference). In terms of operational performance, Qwen 2.5 72B Instruct delivers faster response latency with 420ms TTFT (1380ms faster than DeepSeek-R1). DeepSeek-R1 leads coding benchmarks at 49.2% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
DeepSeek-R1 vs Qwen 2.5 Max
Qwen 2.5 Max is 49.1% cheaper for input tokens ($0.28 vs. $0.55 per 1M tokens) and $0.84 vs. $2.19 for output tokens (2.0x cost difference). In terms of operational performance, Qwen 2.5 Max delivers faster response latency with 480ms TTFT (1320ms faster than DeepSeek-R1). DeepSeek-R1 leads coding benchmarks at 49.2% SWE-bench vs. 44.2% for Qwen 2.5 Max.
DeepSeek-R1 vs Qwen 3.8 Flash Next
Qwen 3.8 Flash Next is 78.2% cheaper for input tokens ($0.12 vs. $0.55 per 1M tokens) and $0.48 vs. $2.19 for output tokens (4.6x cost difference). In terms of operational performance, Qwen 3.8 Flash Next delivers faster response latency with 120ms TTFT (1680ms faster than DeepSeek-R1). Qwen 3.8 Flash Next leads coding benchmarks at 56.8% SWE-bench vs. 49.2% for DeepSeek-R1.
DeepSeek-V3 vs DeepSeek-V4 Flash
Both models share identical input pricing at $0.14 per 1M tokens. In terms of operational performance, DeepSeek-V4 Flash delivers faster response latency with 150ms TTFT (190ms faster than DeepSeek-V3). DeepSeek-V4 Flash leads coding benchmarks at 62.4% SWE-bench vs. 42.0% for DeepSeek-V3.
DeepSeek-V3 vs Fable 5
DeepSeek-V3 is 93.0% cheaper for input tokens ($0.14 vs. $2.00 per 1M tokens) and $0.28 vs. $8.00 for output tokens (14.3x cost difference). In terms of operational performance, Fable 5 delivers faster response latency with 240ms TTFT (100ms faster than DeepSeek-V3). Fable 5 leads coding benchmarks at 58.0% SWE-bench vs. 42.0% for DeepSeek-V3.
DeepSeek-V3 vs Gemini 2.0 Flash
Gemini 2.0 Flash is 28.6% cheaper for input tokens ($0.10 vs. $0.14 per 1M tokens) and $0.40 vs. $0.28 for output tokens (1.4x cost difference). In terms of operational performance, DeepSeek-V3 delivers faster response latency with 340ms TTFT (40ms faster than Gemini 2.0 Flash). Gemini 2.0 Flash leads coding benchmarks at 48.0% SWE-bench vs. 42.0% for DeepSeek-V3.
DeepSeek-V3 vs Gemini 3.7 Flash
Gemini 3.7 Flash is 42.9% cheaper for input tokens ($0.08 vs. $0.14 per 1M tokens) and $0.32 vs. $0.28 for output tokens (1.8x cost difference). In terms of operational performance, Gemini 3.7 Flash delivers faster response latency with 75ms TTFT (265ms faster than DeepSeek-V3). Gemini 3.7 Flash leads coding benchmarks at 68.2% SWE-bench vs. 42.0% for DeepSeek-V3.
DeepSeek-V3 vs GLM 5.3 Flash
DeepSeek-V3 is 6.7% cheaper for input tokens ($0.14 vs. $0.15 per 1M tokens) and $0.28 vs. $0.50 for output tokens (1.1x cost difference). In terms of operational performance, GLM 5.3 Flash delivers faster response latency with 130ms TTFT (210ms faster than DeepSeek-V3). GLM 5.3 Flash leads coding benchmarks at 54.2% SWE-bench vs. 42.0% for DeepSeek-V3.
DeepSeek-V3 vs OpenAI GPT-4o
DeepSeek-V3 is 94.4% cheaper for input tokens ($0.14 vs. $2.50 per 1M tokens) and $0.28 vs. $10.00 for output tokens (17.9x cost difference). In terms of operational performance, OpenAI GPT-4o delivers faster response latency with 280ms TTFT (60ms faster than DeepSeek-V3). DeepSeek-V3 leads coding benchmarks at 42.0% SWE-bench vs. 38.8% for OpenAI GPT-4o.
DeepSeek-V3 vs GPT-5.6 Luna
DeepSeek-V3 is 22.2% cheaper for input tokens ($0.14 vs. $0.18 per 1M tokens) and $0.28 vs. $0.72 for output tokens (1.3x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (250ms faster than DeepSeek-V3). GPT-5.6 Luna leads coding benchmarks at 48.5% SWE-bench vs. 42.0% for DeepSeek-V3.
DeepSeek-V3 vs GPT-5.6 Sol
DeepSeek-V3 is 98.2% cheaper for input tokens ($0.14 vs. $8.00 per 1M tokens) and $0.28 vs. $32.00 for output tokens (57.1x cost difference). In terms of operational performance, DeepSeek-V3 delivers faster response latency with 340ms TTFT (80ms faster than GPT-5.6 Sol). GPT-5.6 Sol leads coding benchmarks at 79.5% SWE-bench vs. 42.0% for DeepSeek-V3.
DeepSeek-V3 vs GPT-5.6 Terra
DeepSeek-V3 is 90.7% cheaper for input tokens ($0.14 vs. $1.50 per 1M tokens) and $0.28 vs. $6.00 for output tokens (10.7x cost difference). In terms of operational performance, GPT-5.6 Terra delivers faster response latency with 210ms TTFT (130ms faster than DeepSeek-V3). GPT-5.6 Terra leads coding benchmarks at 65.4% SWE-bench vs. 42.0% for DeepSeek-V3.
DeepSeek-V3 vs Grok 3
DeepSeek-V3 is 95.3% cheaper for input tokens ($0.14 vs. $3.00 per 1M tokens) and $0.28 vs. $15.00 for output tokens (21.4x cost difference). In terms of operational performance, DeepSeek-V3 delivers faster response latency with 340ms TTFT (510ms faster than Grok 3). Grok 3 leads coding benchmarks at 58.5% SWE-bench vs. 42.0% for DeepSeek-V3.
DeepSeek-V3 vs xAI Grok 4.6
DeepSeek-V3 is 93.0% cheaper for input tokens ($0.14 vs. $2.00 per 1M tokens) and $0.28 vs. $6.00 for output tokens (14.3x cost difference). In terms of operational performance, xAI Grok 4.6 delivers faster response latency with 280ms TTFT (60ms faster than DeepSeek-V3). xAI Grok 4.6 leads coding benchmarks at 76.8% SWE-bench vs. 42.0% for DeepSeek-V3.
DeepSeek-V3 vs Llama 3.3 70B Instruct
DeepSeek-V3 is 22.2% cheaper for input tokens ($0.14 vs. $0.18 per 1M tokens) and $0.28 vs. $0.40 for output tokens (1.3x cost difference). In terms of operational performance, DeepSeek-V3 delivers faster response latency with 340ms TTFT (80ms faster than Llama 3.3 70B Instruct). DeepSeek-V3 leads coding benchmarks at 42.0% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
DeepSeek-V3 vs Mistral Large 2
DeepSeek-V3 is 93.0% cheaper for input tokens ($0.14 vs. $2.00 per 1M tokens) and $0.28 vs. $6.00 for output tokens (14.3x cost difference). In terms of operational performance, DeepSeek-V3 delivers faster response latency with 340ms TTFT (210ms faster than Mistral Large 2). DeepSeek-V3 leads coding benchmarks at 42.0% SWE-bench vs. 39.0% for Mistral Large 2.
DeepSeek-V3 vs OpenAI o1
DeepSeek-V3 is 99.1% cheaper for input tokens ($0.14 vs. $15.00 per 1M tokens) and $0.28 vs. $60.00 for output tokens (107.1x cost difference). In terms of operational performance, DeepSeek-V3 delivers faster response latency with 340ms TTFT (510ms faster than OpenAI o1). OpenAI o1 leads coding benchmarks at 48.9% SWE-bench vs. 42.0% for DeepSeek-V3.
DeepSeek-V3 vs o3-mini
DeepSeek-V3 is 87.3% cheaper for input tokens ($0.14 vs. $1.10 per 1M tokens) and $0.28 vs. $4.40 for output tokens (7.9x cost difference). In terms of operational performance, DeepSeek-V3 delivers faster response latency with 340ms TTFT (860ms faster than o3-mini). o3-mini leads coding benchmarks at 49.3% SWE-bench vs. 42.0% for DeepSeek-V3.
DeepSeek-V3 vs Microsoft Phi-4 (14B)
Microsoft Phi-4 (14B) is 14.3% cheaper for input tokens ($0.12 vs. $0.14 per 1M tokens) and $0.36 vs. $0.28 for output tokens (1.2x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (230ms faster than DeepSeek-V3). Microsoft Phi-4 (14B) leads coding benchmarks at 42.1% SWE-bench vs. 42.0% for DeepSeek-V3.
DeepSeek-V3 vs Qwen 2.5 72B Instruct
DeepSeek-V3 is 60.0% cheaper for input tokens ($0.14 vs. $0.35 per 1M tokens) and $0.28 vs. $0.40 for output tokens (2.5x cost difference). In terms of operational performance, DeepSeek-V3 delivers faster response latency with 340ms TTFT (80ms faster than Qwen 2.5 72B Instruct). Qwen 2.5 72B Instruct leads coding benchmarks at 44.0% SWE-bench vs. 42.0% for DeepSeek-V3.
DeepSeek-V3 vs Qwen 2.5 Max
DeepSeek-V3 is 50.0% cheaper for input tokens ($0.14 vs. $0.28 per 1M tokens) and $0.28 vs. $0.84 for output tokens (2.0x cost difference). In terms of operational performance, DeepSeek-V3 delivers faster response latency with 340ms TTFT (140ms faster than Qwen 2.5 Max). Qwen 2.5 Max leads coding benchmarks at 44.2% SWE-bench vs. 42.0% for DeepSeek-V3.
DeepSeek-V3 vs Qwen 3.8 Flash Next
Qwen 3.8 Flash Next is 14.3% cheaper for input tokens ($0.12 vs. $0.14 per 1M tokens) and $0.48 vs. $0.28 for output tokens (1.2x cost difference). In terms of operational performance, Qwen 3.8 Flash Next delivers faster response latency with 120ms TTFT (220ms faster than DeepSeek-V3). Qwen 3.8 Flash Next leads coding benchmarks at 56.8% SWE-bench vs. 42.0% for DeepSeek-V3.
DeepSeek-V4 Flash vs Fable 5
DeepSeek-V4 Flash is 93.0% cheaper for input tokens ($0.14 vs. $2.00 per 1M tokens) and $0.56 vs. $8.00 for output tokens (14.3x cost difference). In terms of operational performance, DeepSeek-V4 Flash delivers faster response latency with 150ms TTFT (90ms faster than Fable 5). DeepSeek-V4 Flash leads coding benchmarks at 62.4% SWE-bench vs. 58.0% for Fable 5.
DeepSeek-V4 Flash vs Gemini 2.0 Flash
Gemini 2.0 Flash is 28.6% cheaper for input tokens ($0.10 vs. $0.14 per 1M tokens) and $0.40 vs. $0.56 for output tokens (1.4x cost difference). In terms of operational performance, DeepSeek-V4 Flash delivers faster response latency with 150ms TTFT (230ms faster than Gemini 2.0 Flash). DeepSeek-V4 Flash leads coding benchmarks at 62.4% SWE-bench vs. 48.0% for Gemini 2.0 Flash.
DeepSeek-V4 Flash vs Gemini 3.7 Flash
Gemini 3.7 Flash is 42.9% cheaper for input tokens ($0.08 vs. $0.14 per 1M tokens) and $0.32 vs. $0.56 for output tokens (1.8x cost difference). In terms of operational performance, Gemini 3.7 Flash delivers faster response latency with 75ms TTFT (75ms faster than DeepSeek-V4 Flash). Gemini 3.7 Flash leads coding benchmarks at 68.2% SWE-bench vs. 62.4% for DeepSeek-V4 Flash.
DeepSeek-V4 Flash vs GLM 5.3 Flash
DeepSeek-V4 Flash is 6.7% cheaper for input tokens ($0.14 vs. $0.15 per 1M tokens) and $0.56 vs. $0.50 for output tokens (1.1x cost difference). In terms of operational performance, GLM 5.3 Flash delivers faster response latency with 130ms TTFT (20ms faster than DeepSeek-V4 Flash). DeepSeek-V4 Flash leads coding benchmarks at 62.4% SWE-bench vs. 54.2% for GLM 5.3 Flash.
DeepSeek-V4 Flash vs OpenAI GPT-4o
DeepSeek-V4 Flash is 94.4% cheaper for input tokens ($0.14 vs. $2.50 per 1M tokens) and $0.56 vs. $10.00 for output tokens (17.9x cost difference). In terms of operational performance, DeepSeek-V4 Flash delivers faster response latency with 150ms TTFT (130ms faster than OpenAI GPT-4o). DeepSeek-V4 Flash leads coding benchmarks at 62.4% SWE-bench vs. 38.8% for OpenAI GPT-4o.
DeepSeek-V4 Flash vs GPT-5.6 Luna
DeepSeek-V4 Flash is 22.2% cheaper for input tokens ($0.14 vs. $0.18 per 1M tokens) and $0.56 vs. $0.72 for output tokens (1.3x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (60ms faster than DeepSeek-V4 Flash). DeepSeek-V4 Flash leads coding benchmarks at 62.4% SWE-bench vs. 48.5% for GPT-5.6 Luna.
DeepSeek-V4 Flash vs GPT-5.6 Sol
DeepSeek-V4 Flash is 98.2% cheaper for input tokens ($0.14 vs. $8.00 per 1M tokens) and $0.56 vs. $32.00 for output tokens (57.1x cost difference). In terms of operational performance, DeepSeek-V4 Flash delivers faster response latency with 150ms TTFT (270ms faster than GPT-5.6 Sol). GPT-5.6 Sol leads coding benchmarks at 79.5% SWE-bench vs. 62.4% for DeepSeek-V4 Flash.
DeepSeek-V4 Flash vs GPT-5.6 Terra
DeepSeek-V4 Flash is 90.7% cheaper for input tokens ($0.14 vs. $1.50 per 1M tokens) and $0.56 vs. $6.00 for output tokens (10.7x cost difference). In terms of operational performance, DeepSeek-V4 Flash delivers faster response latency with 150ms TTFT (60ms faster than GPT-5.6 Terra). GPT-5.6 Terra leads coding benchmarks at 65.4% SWE-bench vs. 62.4% for DeepSeek-V4 Flash.
DeepSeek-V4 Flash vs Grok 3
DeepSeek-V4 Flash is 95.3% cheaper for input tokens ($0.14 vs. $3.00 per 1M tokens) and $0.56 vs. $15.00 for output tokens (21.4x cost difference). In terms of operational performance, DeepSeek-V4 Flash delivers faster response latency with 150ms TTFT (700ms faster than Grok 3). DeepSeek-V4 Flash leads coding benchmarks at 62.4% SWE-bench vs. 58.5% for Grok 3.
DeepSeek-V4 Flash vs xAI Grok 4.6
DeepSeek-V4 Flash is 93.0% cheaper for input tokens ($0.14 vs. $2.00 per 1M tokens) and $0.56 vs. $6.00 for output tokens (14.3x cost difference). In terms of operational performance, DeepSeek-V4 Flash delivers faster response latency with 150ms TTFT (130ms faster than xAI Grok 4.6). xAI Grok 4.6 leads coding benchmarks at 76.8% SWE-bench vs. 62.4% for DeepSeek-V4 Flash.
DeepSeek-V4 Flash vs Llama 3.3 70B Instruct
DeepSeek-V4 Flash is 22.2% cheaper for input tokens ($0.14 vs. $0.18 per 1M tokens) and $0.56 vs. $0.40 for output tokens (1.3x cost difference). In terms of operational performance, DeepSeek-V4 Flash delivers faster response latency with 150ms TTFT (270ms faster than Llama 3.3 70B Instruct). DeepSeek-V4 Flash leads coding benchmarks at 62.4% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
DeepSeek-V4 Flash vs Mistral Large 2
DeepSeek-V4 Flash is 93.0% cheaper for input tokens ($0.14 vs. $2.00 per 1M tokens) and $0.56 vs. $6.00 for output tokens (14.3x cost difference). In terms of operational performance, DeepSeek-V4 Flash delivers faster response latency with 150ms TTFT (400ms faster than Mistral Large 2). DeepSeek-V4 Flash leads coding benchmarks at 62.4% SWE-bench vs. 39.0% for Mistral Large 2.
DeepSeek-V4 Flash vs OpenAI o1
DeepSeek-V4 Flash is 99.1% cheaper for input tokens ($0.14 vs. $15.00 per 1M tokens) and $0.56 vs. $60.00 for output tokens (107.1x cost difference). In terms of operational performance, DeepSeek-V4 Flash delivers faster response latency with 150ms TTFT (700ms faster than OpenAI o1). DeepSeek-V4 Flash leads coding benchmarks at 62.4% SWE-bench vs. 48.9% for OpenAI o1.
DeepSeek-V4 Flash vs o3-mini
DeepSeek-V4 Flash is 87.3% cheaper for input tokens ($0.14 vs. $1.10 per 1M tokens) and $0.56 vs. $4.40 for output tokens (7.9x cost difference). In terms of operational performance, DeepSeek-V4 Flash delivers faster response latency with 150ms TTFT (1050ms faster than o3-mini). DeepSeek-V4 Flash leads coding benchmarks at 62.4% SWE-bench vs. 49.3% for o3-mini.
DeepSeek-V4 Flash vs Microsoft Phi-4 (14B)
Microsoft Phi-4 (14B) is 14.3% cheaper for input tokens ($0.12 vs. $0.14 per 1M tokens) and $0.36 vs. $0.56 for output tokens (1.2x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (40ms faster than DeepSeek-V4 Flash). DeepSeek-V4 Flash leads coding benchmarks at 62.4% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
DeepSeek-V4 Flash vs Qwen 2.5 72B Instruct
DeepSeek-V4 Flash is 60.0% cheaper for input tokens ($0.14 vs. $0.35 per 1M tokens) and $0.56 vs. $0.40 for output tokens (2.5x cost difference). In terms of operational performance, DeepSeek-V4 Flash delivers faster response latency with 150ms TTFT (270ms faster than Qwen 2.5 72B Instruct). DeepSeek-V4 Flash leads coding benchmarks at 62.4% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
DeepSeek-V4 Flash vs Qwen 2.5 Max
DeepSeek-V4 Flash is 50.0% cheaper for input tokens ($0.14 vs. $0.28 per 1M tokens) and $0.56 vs. $0.84 for output tokens (2.0x cost difference). In terms of operational performance, DeepSeek-V4 Flash delivers faster response latency with 150ms TTFT (330ms faster than Qwen 2.5 Max). DeepSeek-V4 Flash leads coding benchmarks at 62.4% SWE-bench vs. 44.2% for Qwen 2.5 Max.
DeepSeek-V4 Flash vs Qwen 3.8 Flash Next
Qwen 3.8 Flash Next is 14.3% cheaper for input tokens ($0.12 vs. $0.14 per 1M tokens) and $0.48 vs. $0.56 for output tokens (1.2x cost difference). In terms of operational performance, Qwen 3.8 Flash Next delivers faster response latency with 120ms TTFT (30ms faster than DeepSeek-V4 Flash). DeepSeek-V4 Flash leads coding benchmarks at 62.4% SWE-bench vs. 56.8% for Qwen 3.8 Flash Next.
Fable 5 vs Gemini 2.0 Flash
Gemini 2.0 Flash is 95.0% cheaper for input tokens ($0.10 vs. $2.00 per 1M tokens) and $0.40 vs. $8.00 for output tokens (20.0x cost difference). In terms of operational performance, Fable 5 delivers faster response latency with 240ms TTFT (140ms faster than Gemini 2.0 Flash). Fable 5 leads coding benchmarks at 58.0% SWE-bench vs. 48.0% for Gemini 2.0 Flash.
Fable 5 vs Gemini 3.7 Flash
Gemini 3.7 Flash is 96.0% cheaper for input tokens ($0.08 vs. $2.00 per 1M tokens) and $0.32 vs. $8.00 for output tokens (25.0x cost difference). In terms of operational performance, Gemini 3.7 Flash delivers faster response latency with 75ms TTFT (165ms faster than Fable 5). Gemini 3.7 Flash leads coding benchmarks at 68.2% SWE-bench vs. 58.0% for Fable 5.
Fable 5 vs GLM 5.3 Flash
GLM 5.3 Flash is 92.5% cheaper for input tokens ($0.15 vs. $2.00 per 1M tokens) and $0.50 vs. $8.00 for output tokens (13.3x cost difference). In terms of operational performance, GLM 5.3 Flash delivers faster response latency with 130ms TTFT (110ms faster than Fable 5). Fable 5 leads coding benchmarks at 58.0% SWE-bench vs. 54.2% for GLM 5.3 Flash.
Fable 5 vs OpenAI GPT-4o
Fable 5 is 20.0% cheaper for input tokens ($2.00 vs. $2.50 per 1M tokens) and $8.00 vs. $10.00 for output tokens (1.2x cost difference). In terms of operational performance, Fable 5 delivers faster response latency with 240ms TTFT (40ms faster than OpenAI GPT-4o). Fable 5 leads coding benchmarks at 58.0% SWE-bench vs. 38.8% for OpenAI GPT-4o.
Fable 5 vs GPT-5.6 Luna
GPT-5.6 Luna is 91.0% cheaper for input tokens ($0.18 vs. $2.00 per 1M tokens) and $0.72 vs. $8.00 for output tokens (11.1x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (150ms faster than Fable 5). Fable 5 leads coding benchmarks at 58.0% SWE-bench vs. 48.5% for GPT-5.6 Luna.
Fable 5 vs GPT-5.6 Sol
Fable 5 is 75.0% cheaper for input tokens ($2.00 vs. $8.00 per 1M tokens) and $8.00 vs. $32.00 for output tokens (4.0x cost difference). In terms of operational performance, Fable 5 delivers faster response latency with 240ms TTFT (180ms faster than GPT-5.6 Sol). GPT-5.6 Sol leads coding benchmarks at 79.5% SWE-bench vs. 58.0% for Fable 5.
Fable 5 vs GPT-5.6 Terra
GPT-5.6 Terra is 25.0% cheaper for input tokens ($1.50 vs. $2.00 per 1M tokens) and $6.00 vs. $8.00 for output tokens (1.3x cost difference). In terms of operational performance, GPT-5.6 Terra delivers faster response latency with 210ms TTFT (30ms faster than Fable 5). GPT-5.6 Terra leads coding benchmarks at 65.4% SWE-bench vs. 58.0% for Fable 5.
Fable 5 vs Grok 3
Fable 5 is 33.3% cheaper for input tokens ($2.00 vs. $3.00 per 1M tokens) and $8.00 vs. $15.00 for output tokens (1.5x cost difference). In terms of operational performance, Fable 5 delivers faster response latency with 240ms TTFT (610ms faster than Grok 3). Grok 3 leads coding benchmarks at 58.5% SWE-bench vs. 58.0% for Fable 5.
Fable 5 vs xAI Grok 4.6
Both models share identical input pricing at $2.00 per 1M tokens. In terms of operational performance, Fable 5 delivers faster response latency with 240ms TTFT (40ms faster than xAI Grok 4.6). xAI Grok 4.6 leads coding benchmarks at 76.8% SWE-bench vs. 58.0% for Fable 5.
Fable 5 vs Llama 3.3 70B Instruct
Llama 3.3 70B Instruct is 91.0% cheaper for input tokens ($0.18 vs. $2.00 per 1M tokens) and $0.40 vs. $8.00 for output tokens (11.1x cost difference). In terms of operational performance, Fable 5 delivers faster response latency with 240ms TTFT (180ms faster than Llama 3.3 70B Instruct). Fable 5 leads coding benchmarks at 58.0% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Fable 5 vs Mistral Large 2
Both models share identical input pricing at $2.00 per 1M tokens. In terms of operational performance, Fable 5 delivers faster response latency with 240ms TTFT (310ms faster than Mistral Large 2). Fable 5 leads coding benchmarks at 58.0% SWE-bench vs. 39.0% for Mistral Large 2.
Fable 5 vs OpenAI o1
Fable 5 is 86.7% cheaper for input tokens ($2.00 vs. $15.00 per 1M tokens) and $8.00 vs. $60.00 for output tokens (7.5x cost difference). In terms of operational performance, Fable 5 delivers faster response latency with 240ms TTFT (610ms faster than OpenAI o1). Fable 5 leads coding benchmarks at 58.0% SWE-bench vs. 48.9% for OpenAI o1.
Fable 5 vs o3-mini
o3-mini is 45.0% cheaper for input tokens ($1.10 vs. $2.00 per 1M tokens) and $4.40 vs. $8.00 for output tokens (1.8x cost difference). In terms of operational performance, Fable 5 delivers faster response latency with 240ms TTFT (960ms faster than o3-mini). Fable 5 leads coding benchmarks at 58.0% SWE-bench vs. 49.3% for o3-mini.
Fable 5 vs Microsoft Phi-4 (14B)
Microsoft Phi-4 (14B) is 94.0% cheaper for input tokens ($0.12 vs. $2.00 per 1M tokens) and $0.36 vs. $8.00 for output tokens (16.7x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (130ms faster than Fable 5). Fable 5 leads coding benchmarks at 58.0% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
Fable 5 vs Qwen 2.5 72B Instruct
Qwen 2.5 72B Instruct is 82.5% cheaper for input tokens ($0.35 vs. $2.00 per 1M tokens) and $0.40 vs. $8.00 for output tokens (5.7x cost difference). In terms of operational performance, Fable 5 delivers faster response latency with 240ms TTFT (180ms faster than Qwen 2.5 72B Instruct). Fable 5 leads coding benchmarks at 58.0% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
Fable 5 vs Qwen 2.5 Max
Qwen 2.5 Max is 86.0% cheaper for input tokens ($0.28 vs. $2.00 per 1M tokens) and $0.84 vs. $8.00 for output tokens (7.1x cost difference). In terms of operational performance, Fable 5 delivers faster response latency with 240ms TTFT (240ms faster than Qwen 2.5 Max). Fable 5 leads coding benchmarks at 58.0% SWE-bench vs. 44.2% for Qwen 2.5 Max.
Fable 5 vs Qwen 3.8 Flash Next
Qwen 3.8 Flash Next is 94.0% cheaper for input tokens ($0.12 vs. $2.00 per 1M tokens) and $0.48 vs. $8.00 for output tokens (16.7x cost difference). In terms of operational performance, Qwen 3.8 Flash Next delivers faster response latency with 120ms TTFT (120ms faster than Fable 5). Fable 5 leads coding benchmarks at 58.0% SWE-bench vs. 56.8% for Qwen 3.8 Flash Next.
Gemini 2.0 Flash vs Gemini 3.7 Flash
Gemini 3.7 Flash is 20.0% cheaper for input tokens ($0.08 vs. $0.10 per 1M tokens) and $0.32 vs. $0.40 for output tokens (1.2x cost difference). In terms of operational performance, Gemini 3.7 Flash delivers faster response latency with 75ms TTFT (305ms faster than Gemini 2.0 Flash). Gemini 3.7 Flash leads coding benchmarks at 68.2% SWE-bench vs. 48.0% for Gemini 2.0 Flash.
Gemini 2.0 Flash vs GLM 5.3 Flash
Gemini 2.0 Flash is 33.3% cheaper for input tokens ($0.10 vs. $0.15 per 1M tokens) and $0.40 vs. $0.50 for output tokens (1.5x cost difference). In terms of operational performance, GLM 5.3 Flash delivers faster response latency with 130ms TTFT (250ms faster than Gemini 2.0 Flash). GLM 5.3 Flash leads coding benchmarks at 54.2% SWE-bench vs. 48.0% for Gemini 2.0 Flash.
Gemini 2.0 Flash vs OpenAI GPT-4o
Gemini 2.0 Flash is 96.0% cheaper for input tokens ($0.10 vs. $2.50 per 1M tokens) and $0.40 vs. $10.00 for output tokens (25.0x cost difference). In terms of operational performance, OpenAI GPT-4o delivers faster response latency with 280ms TTFT (100ms faster than Gemini 2.0 Flash). Gemini 2.0 Flash leads coding benchmarks at 48.0% SWE-bench vs. 38.8% for OpenAI GPT-4o.
Gemini 2.0 Flash vs GPT-5.6 Luna
Gemini 2.0 Flash is 44.4% cheaper for input tokens ($0.10 vs. $0.18 per 1M tokens) and $0.40 vs. $0.72 for output tokens (1.8x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (290ms faster than Gemini 2.0 Flash). GPT-5.6 Luna leads coding benchmarks at 48.5% SWE-bench vs. 48.0% for Gemini 2.0 Flash.
Gemini 2.0 Flash vs GPT-5.6 Sol
Gemini 2.0 Flash is 98.8% cheaper for input tokens ($0.10 vs. $8.00 per 1M tokens) and $0.40 vs. $32.00 for output tokens (80.0x cost difference). In terms of operational performance, Gemini 2.0 Flash delivers faster response latency with 380ms TTFT (40ms faster than GPT-5.6 Sol). GPT-5.6 Sol leads coding benchmarks at 79.5% SWE-bench vs. 48.0% for Gemini 2.0 Flash.
Gemini 2.0 Flash vs GPT-5.6 Terra
Gemini 2.0 Flash is 93.3% cheaper for input tokens ($0.10 vs. $1.50 per 1M tokens) and $0.40 vs. $6.00 for output tokens (15.0x cost difference). In terms of operational performance, GPT-5.6 Terra delivers faster response latency with 210ms TTFT (170ms faster than Gemini 2.0 Flash). GPT-5.6 Terra leads coding benchmarks at 65.4% SWE-bench vs. 48.0% for Gemini 2.0 Flash.
Gemini 2.0 Flash vs Grok 3
Gemini 2.0 Flash is 96.7% cheaper for input tokens ($0.10 vs. $3.00 per 1M tokens) and $0.40 vs. $15.00 for output tokens (30.0x cost difference). In terms of operational performance, Gemini 2.0 Flash delivers faster response latency with 380ms TTFT (470ms faster than Grok 3). Grok 3 leads coding benchmarks at 58.5% SWE-bench vs. 48.0% for Gemini 2.0 Flash.
Gemini 2.0 Flash vs xAI Grok 4.6
Gemini 2.0 Flash is 95.0% cheaper for input tokens ($0.10 vs. $2.00 per 1M tokens) and $0.40 vs. $6.00 for output tokens (20.0x cost difference). In terms of operational performance, xAI Grok 4.6 delivers faster response latency with 280ms TTFT (100ms faster than Gemini 2.0 Flash). xAI Grok 4.6 leads coding benchmarks at 76.8% SWE-bench vs. 48.0% for Gemini 2.0 Flash.
Gemini 2.0 Flash vs Llama 3.3 70B Instruct
Gemini 2.0 Flash is 44.4% cheaper for input tokens ($0.10 vs. $0.18 per 1M tokens) and $0.40 vs. $0.40 for output tokens (1.8x cost difference). In terms of operational performance, Gemini 2.0 Flash delivers faster response latency with 380ms TTFT (40ms faster than Llama 3.3 70B Instruct). Gemini 2.0 Flash leads coding benchmarks at 48.0% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Gemini 2.0 Flash vs Mistral Large 2
Gemini 2.0 Flash is 95.0% cheaper for input tokens ($0.10 vs. $2.00 per 1M tokens) and $0.40 vs. $6.00 for output tokens (20.0x cost difference). In terms of operational performance, Gemini 2.0 Flash delivers faster response latency with 380ms TTFT (170ms faster than Mistral Large 2). Gemini 2.0 Flash leads coding benchmarks at 48.0% SWE-bench vs. 39.0% for Mistral Large 2.
Gemini 2.0 Flash vs OpenAI o1
Gemini 2.0 Flash is 99.3% cheaper for input tokens ($0.10 vs. $15.00 per 1M tokens) and $0.40 vs. $60.00 for output tokens (150.0x cost difference). In terms of operational performance, Gemini 2.0 Flash delivers faster response latency with 380ms TTFT (470ms faster than OpenAI o1). OpenAI o1 leads coding benchmarks at 48.9% SWE-bench vs. 48.0% for Gemini 2.0 Flash.
Gemini 2.0 Flash vs o3-mini
Gemini 2.0 Flash is 90.9% cheaper for input tokens ($0.10 vs. $1.10 per 1M tokens) and $0.40 vs. $4.40 for output tokens (11.0x cost difference). In terms of operational performance, Gemini 2.0 Flash delivers faster response latency with 380ms TTFT (820ms faster than o3-mini). o3-mini leads coding benchmarks at 49.3% SWE-bench vs. 48.0% for Gemini 2.0 Flash.
Gemini 2.0 Flash vs Microsoft Phi-4 (14B)
Gemini 2.0 Flash is 16.7% cheaper for input tokens ($0.10 vs. $0.12 per 1M tokens) and $0.40 vs. $0.36 for output tokens (1.2x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (270ms faster than Gemini 2.0 Flash). Gemini 2.0 Flash leads coding benchmarks at 48.0% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
Gemini 2.0 Flash vs Qwen 2.5 72B Instruct
Gemini 2.0 Flash is 71.4% cheaper for input tokens ($0.10 vs. $0.35 per 1M tokens) and $0.40 vs. $0.40 for output tokens (3.5x cost difference). In terms of operational performance, Gemini 2.0 Flash delivers faster response latency with 380ms TTFT (40ms faster than Qwen 2.5 72B Instruct). Gemini 2.0 Flash leads coding benchmarks at 48.0% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
Gemini 2.0 Flash vs Qwen 2.5 Max
Gemini 2.0 Flash is 64.3% cheaper for input tokens ($0.10 vs. $0.28 per 1M tokens) and $0.40 vs. $0.84 for output tokens (2.8x cost difference). In terms of operational performance, Gemini 2.0 Flash delivers faster response latency with 380ms TTFT (100ms faster than Qwen 2.5 Max). Gemini 2.0 Flash leads coding benchmarks at 48.0% SWE-bench vs. 44.2% for Qwen 2.5 Max.
Gemini 2.0 Flash vs Qwen 3.8 Flash Next
Gemini 2.0 Flash is 16.7% cheaper for input tokens ($0.10 vs. $0.12 per 1M tokens) and $0.40 vs. $0.48 for output tokens (1.2x cost difference). In terms of operational performance, Qwen 3.8 Flash Next delivers faster response latency with 120ms TTFT (260ms faster than Gemini 2.0 Flash). Qwen 3.8 Flash Next leads coding benchmarks at 56.8% SWE-bench vs. 48.0% for Gemini 2.0 Flash.
Gemini 3.7 Flash vs GLM 5.3 Flash
Gemini 3.7 Flash is 46.7% cheaper for input tokens ($0.08 vs. $0.15 per 1M tokens) and $0.32 vs. $0.50 for output tokens (1.9x cost difference). In terms of operational performance, Gemini 3.7 Flash delivers faster response latency with 75ms TTFT (55ms faster than GLM 5.3 Flash). Gemini 3.7 Flash leads coding benchmarks at 68.2% SWE-bench vs. 54.2% for GLM 5.3 Flash.
Gemini 3.7 Flash vs OpenAI GPT-4o
Gemini 3.7 Flash is 96.8% cheaper for input tokens ($0.08 vs. $2.50 per 1M tokens) and $0.32 vs. $10.00 for output tokens (31.2x cost difference). In terms of operational performance, Gemini 3.7 Flash delivers faster response latency with 75ms TTFT (205ms faster than OpenAI GPT-4o). Gemini 3.7 Flash leads coding benchmarks at 68.2% SWE-bench vs. 38.8% for OpenAI GPT-4o.
Gemini 3.7 Flash vs GPT-5.6 Luna
Gemini 3.7 Flash is 55.6% cheaper for input tokens ($0.08 vs. $0.18 per 1M tokens) and $0.32 vs. $0.72 for output tokens (2.2x cost difference). In terms of operational performance, Gemini 3.7 Flash delivers faster response latency with 75ms TTFT (15ms faster than GPT-5.6 Luna). Gemini 3.7 Flash leads coding benchmarks at 68.2% SWE-bench vs. 48.5% for GPT-5.6 Luna.
Gemini 3.7 Flash vs GPT-5.6 Sol
Gemini 3.7 Flash is 99.0% cheaper for input tokens ($0.08 vs. $8.00 per 1M tokens) and $0.32 vs. $32.00 for output tokens (100.0x cost difference). In terms of operational performance, Gemini 3.7 Flash delivers faster response latency with 75ms TTFT (345ms faster than GPT-5.6 Sol). GPT-5.6 Sol leads coding benchmarks at 79.5% SWE-bench vs. 68.2% for Gemini 3.7 Flash.
Gemini 3.7 Flash vs GPT-5.6 Terra
Gemini 3.7 Flash is 94.7% cheaper for input tokens ($0.08 vs. $1.50 per 1M tokens) and $0.32 vs. $6.00 for output tokens (18.8x cost difference). In terms of operational performance, Gemini 3.7 Flash delivers faster response latency with 75ms TTFT (135ms faster than GPT-5.6 Terra). Gemini 3.7 Flash leads coding benchmarks at 68.2% SWE-bench vs. 65.4% for GPT-5.6 Terra.
Gemini 3.7 Flash vs Grok 3
Gemini 3.7 Flash is 97.3% cheaper for input tokens ($0.08 vs. $3.00 per 1M tokens) and $0.32 vs. $15.00 for output tokens (37.5x cost difference). In terms of operational performance, Gemini 3.7 Flash delivers faster response latency with 75ms TTFT (775ms faster than Grok 3). Gemini 3.7 Flash leads coding benchmarks at 68.2% SWE-bench vs. 58.5% for Grok 3.
Gemini 3.7 Flash vs xAI Grok 4.6
Gemini 3.7 Flash is 96.0% cheaper for input tokens ($0.08 vs. $2.00 per 1M tokens) and $0.32 vs. $6.00 for output tokens (25.0x cost difference). In terms of operational performance, Gemini 3.7 Flash delivers faster response latency with 75ms TTFT (205ms faster than xAI Grok 4.6). xAI Grok 4.6 leads coding benchmarks at 76.8% SWE-bench vs. 68.2% for Gemini 3.7 Flash.
Gemini 3.7 Flash vs Llama 3.3 70B Instruct
Gemini 3.7 Flash is 55.6% cheaper for input tokens ($0.08 vs. $0.18 per 1M tokens) and $0.32 vs. $0.40 for output tokens (2.2x cost difference). In terms of operational performance, Gemini 3.7 Flash delivers faster response latency with 75ms TTFT (345ms faster than Llama 3.3 70B Instruct). Gemini 3.7 Flash leads coding benchmarks at 68.2% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Gemini 3.7 Flash vs Mistral Large 2
Gemini 3.7 Flash is 96.0% cheaper for input tokens ($0.08 vs. $2.00 per 1M tokens) and $0.32 vs. $6.00 for output tokens (25.0x cost difference). In terms of operational performance, Gemini 3.7 Flash delivers faster response latency with 75ms TTFT (475ms faster than Mistral Large 2). Gemini 3.7 Flash leads coding benchmarks at 68.2% SWE-bench vs. 39.0% for Mistral Large 2.
Gemini 3.7 Flash vs OpenAI o1
Gemini 3.7 Flash is 99.5% cheaper for input tokens ($0.08 vs. $15.00 per 1M tokens) and $0.32 vs. $60.00 for output tokens (187.5x cost difference). In terms of operational performance, Gemini 3.7 Flash delivers faster response latency with 75ms TTFT (775ms faster than OpenAI o1). Gemini 3.7 Flash leads coding benchmarks at 68.2% SWE-bench vs. 48.9% for OpenAI o1.
Gemini 3.7 Flash vs o3-mini
Gemini 3.7 Flash is 92.7% cheaper for input tokens ($0.08 vs. $1.10 per 1M tokens) and $0.32 vs. $4.40 for output tokens (13.8x cost difference). In terms of operational performance, Gemini 3.7 Flash delivers faster response latency with 75ms TTFT (1125ms faster than o3-mini). Gemini 3.7 Flash leads coding benchmarks at 68.2% SWE-bench vs. 49.3% for o3-mini.
Gemini 3.7 Flash vs Microsoft Phi-4 (14B)
Gemini 3.7 Flash is 33.3% cheaper for input tokens ($0.08 vs. $0.12 per 1M tokens) and $0.32 vs. $0.36 for output tokens (1.5x cost difference). In terms of operational performance, Gemini 3.7 Flash delivers faster response latency with 75ms TTFT (35ms faster than Microsoft Phi-4 (14B)). Gemini 3.7 Flash leads coding benchmarks at 68.2% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
Gemini 3.7 Flash vs Qwen 2.5 72B Instruct
Gemini 3.7 Flash is 77.1% cheaper for input tokens ($0.08 vs. $0.35 per 1M tokens) and $0.32 vs. $0.40 for output tokens (4.4x cost difference). In terms of operational performance, Gemini 3.7 Flash delivers faster response latency with 75ms TTFT (345ms faster than Qwen 2.5 72B Instruct). Gemini 3.7 Flash leads coding benchmarks at 68.2% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
Gemini 3.7 Flash vs Qwen 2.5 Max
Gemini 3.7 Flash is 71.4% cheaper for input tokens ($0.08 vs. $0.28 per 1M tokens) and $0.32 vs. $0.84 for output tokens (3.5x cost difference). In terms of operational performance, Gemini 3.7 Flash delivers faster response latency with 75ms TTFT (405ms faster than Qwen 2.5 Max). Gemini 3.7 Flash leads coding benchmarks at 68.2% SWE-bench vs. 44.2% for Qwen 2.5 Max.
Gemini 3.7 Flash vs Qwen 3.8 Flash Next
Gemini 3.7 Flash is 33.3% cheaper for input tokens ($0.08 vs. $0.12 per 1M tokens) and $0.32 vs. $0.48 for output tokens (1.5x cost difference). In terms of operational performance, Gemini 3.7 Flash delivers faster response latency with 75ms TTFT (45ms faster than Qwen 3.8 Flash Next). Gemini 3.7 Flash leads coding benchmarks at 68.2% SWE-bench vs. 56.8% for Qwen 3.8 Flash Next.
GLM 5.3 Flash vs OpenAI GPT-4o
GLM 5.3 Flash is 94.0% cheaper for input tokens ($0.15 vs. $2.50 per 1M tokens) and $0.50 vs. $10.00 for output tokens (16.7x cost difference). In terms of operational performance, GLM 5.3 Flash delivers faster response latency with 130ms TTFT (150ms faster than OpenAI GPT-4o). GLM 5.3 Flash leads coding benchmarks at 54.2% SWE-bench vs. 38.8% for OpenAI GPT-4o.
GLM 5.3 Flash vs GPT-5.6 Luna
GLM 5.3 Flash is 16.7% cheaper for input tokens ($0.15 vs. $0.18 per 1M tokens) and $0.50 vs. $0.72 for output tokens (1.2x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (40ms faster than GLM 5.3 Flash). GLM 5.3 Flash leads coding benchmarks at 54.2% SWE-bench vs. 48.5% for GPT-5.6 Luna.
GLM 5.3 Flash vs GPT-5.6 Sol
GLM 5.3 Flash is 98.1% cheaper for input tokens ($0.15 vs. $8.00 per 1M tokens) and $0.50 vs. $32.00 for output tokens (53.3x cost difference). In terms of operational performance, GLM 5.3 Flash delivers faster response latency with 130ms TTFT (290ms faster than GPT-5.6 Sol). GPT-5.6 Sol leads coding benchmarks at 79.5% SWE-bench vs. 54.2% for GLM 5.3 Flash.
GLM 5.3 Flash vs GPT-5.6 Terra
GLM 5.3 Flash is 90.0% cheaper for input tokens ($0.15 vs. $1.50 per 1M tokens) and $0.50 vs. $6.00 for output tokens (10.0x cost difference). In terms of operational performance, GLM 5.3 Flash delivers faster response latency with 130ms TTFT (80ms faster than GPT-5.6 Terra). GPT-5.6 Terra leads coding benchmarks at 65.4% SWE-bench vs. 54.2% for GLM 5.3 Flash.
GLM 5.3 Flash vs Grok 3
GLM 5.3 Flash is 95.0% cheaper for input tokens ($0.15 vs. $3.00 per 1M tokens) and $0.50 vs. $15.00 for output tokens (20.0x cost difference). In terms of operational performance, GLM 5.3 Flash delivers faster response latency with 130ms TTFT (720ms faster than Grok 3). Grok 3 leads coding benchmarks at 58.5% SWE-bench vs. 54.2% for GLM 5.3 Flash.
GLM 5.3 Flash vs xAI Grok 4.6
GLM 5.3 Flash is 92.5% cheaper for input tokens ($0.15 vs. $2.00 per 1M tokens) and $0.50 vs. $6.00 for output tokens (13.3x cost difference). In terms of operational performance, GLM 5.3 Flash delivers faster response latency with 130ms TTFT (150ms faster than xAI Grok 4.6). xAI Grok 4.6 leads coding benchmarks at 76.8% SWE-bench vs. 54.2% for GLM 5.3 Flash.
GLM 5.3 Flash vs Llama 3.3 70B Instruct
GLM 5.3 Flash is 16.7% cheaper for input tokens ($0.15 vs. $0.18 per 1M tokens) and $0.50 vs. $0.40 for output tokens (1.2x cost difference). In terms of operational performance, GLM 5.3 Flash delivers faster response latency with 130ms TTFT (290ms faster than Llama 3.3 70B Instruct). GLM 5.3 Flash leads coding benchmarks at 54.2% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
GLM 5.3 Flash vs Mistral Large 2
GLM 5.3 Flash is 92.5% cheaper for input tokens ($0.15 vs. $2.00 per 1M tokens) and $0.50 vs. $6.00 for output tokens (13.3x cost difference). In terms of operational performance, GLM 5.3 Flash delivers faster response latency with 130ms TTFT (420ms faster than Mistral Large 2). GLM 5.3 Flash leads coding benchmarks at 54.2% SWE-bench vs. 39.0% for Mistral Large 2.
GLM 5.3 Flash vs OpenAI o1
GLM 5.3 Flash is 99.0% cheaper for input tokens ($0.15 vs. $15.00 per 1M tokens) and $0.50 vs. $60.00 for output tokens (100.0x cost difference). In terms of operational performance, GLM 5.3 Flash delivers faster response latency with 130ms TTFT (720ms faster than OpenAI o1). GLM 5.3 Flash leads coding benchmarks at 54.2% SWE-bench vs. 48.9% for OpenAI o1.
GLM 5.3 Flash vs o3-mini
GLM 5.3 Flash is 86.4% cheaper for input tokens ($0.15 vs. $1.10 per 1M tokens) and $0.50 vs. $4.40 for output tokens (7.3x cost difference). In terms of operational performance, GLM 5.3 Flash delivers faster response latency with 130ms TTFT (1070ms faster than o3-mini). GLM 5.3 Flash leads coding benchmarks at 54.2% SWE-bench vs. 49.3% for o3-mini.
GLM 5.3 Flash vs Microsoft Phi-4 (14B)
Microsoft Phi-4 (14B) is 20.0% cheaper for input tokens ($0.12 vs. $0.15 per 1M tokens) and $0.36 vs. $0.50 for output tokens (1.2x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (20ms faster than GLM 5.3 Flash). GLM 5.3 Flash leads coding benchmarks at 54.2% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
GLM 5.3 Flash vs Qwen 2.5 72B Instruct
GLM 5.3 Flash is 57.1% cheaper for input tokens ($0.15 vs. $0.35 per 1M tokens) and $0.50 vs. $0.40 for output tokens (2.3x cost difference). In terms of operational performance, GLM 5.3 Flash delivers faster response latency with 130ms TTFT (290ms faster than Qwen 2.5 72B Instruct). GLM 5.3 Flash leads coding benchmarks at 54.2% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
GLM 5.3 Flash vs Qwen 2.5 Max
GLM 5.3 Flash is 46.4% cheaper for input tokens ($0.15 vs. $0.28 per 1M tokens) and $0.50 vs. $0.84 for output tokens (1.9x cost difference). In terms of operational performance, GLM 5.3 Flash delivers faster response latency with 130ms TTFT (350ms faster than Qwen 2.5 Max). GLM 5.3 Flash leads coding benchmarks at 54.2% SWE-bench vs. 44.2% for Qwen 2.5 Max.
GLM 5.3 Flash vs Qwen 3.8 Flash Next
Qwen 3.8 Flash Next is 20.0% cheaper for input tokens ($0.12 vs. $0.15 per 1M tokens) and $0.48 vs. $0.50 for output tokens (1.2x cost difference). In terms of operational performance, Qwen 3.8 Flash Next delivers faster response latency with 120ms TTFT (10ms faster than GLM 5.3 Flash). Qwen 3.8 Flash Next leads coding benchmarks at 56.8% SWE-bench vs. 54.2% for GLM 5.3 Flash.
OpenAI GPT-4o vs GPT-5.6 Luna
GPT-5.6 Luna is 92.8% cheaper for input tokens ($0.18 vs. $2.50 per 1M tokens) and $0.72 vs. $10.00 for output tokens (13.9x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (190ms faster than OpenAI GPT-4o). GPT-5.6 Luna leads coding benchmarks at 48.5% SWE-bench vs. 38.8% for OpenAI GPT-4o.
OpenAI GPT-4o vs GPT-5.6 Sol
OpenAI GPT-4o is 68.8% cheaper for input tokens ($2.50 vs. $8.00 per 1M tokens) and $10.00 vs. $32.00 for output tokens (3.2x cost difference). In terms of operational performance, OpenAI GPT-4o delivers faster response latency with 280ms TTFT (140ms faster than GPT-5.6 Sol). GPT-5.6 Sol leads coding benchmarks at 79.5% SWE-bench vs. 38.8% for OpenAI GPT-4o.
OpenAI GPT-4o vs GPT-5.6 Terra
GPT-5.6 Terra is 40.0% cheaper for input tokens ($1.50 vs. $2.50 per 1M tokens) and $6.00 vs. $10.00 for output tokens (1.7x cost difference). In terms of operational performance, GPT-5.6 Terra delivers faster response latency with 210ms TTFT (70ms faster than OpenAI GPT-4o). GPT-5.6 Terra leads coding benchmarks at 65.4% SWE-bench vs. 38.8% for OpenAI GPT-4o.
OpenAI GPT-4o vs Grok 3
OpenAI GPT-4o is 16.7% cheaper for input tokens ($2.50 vs. $3.00 per 1M tokens) and $10.00 vs. $15.00 for output tokens (1.2x cost difference). In terms of operational performance, OpenAI GPT-4o delivers faster response latency with 280ms TTFT (570ms faster than Grok 3). Grok 3 leads coding benchmarks at 58.5% SWE-bench vs. 38.8% for OpenAI GPT-4o.
OpenAI GPT-4o vs xAI Grok 4.6
xAI Grok 4.6 is 20.0% cheaper for input tokens ($2.00 vs. $2.50 per 1M tokens) and $6.00 vs. $10.00 for output tokens (1.2x cost difference). In terms of operational performance, xAI Grok 4.6 delivers faster response latency with 280ms TTFT (0ms faster than OpenAI GPT-4o). xAI Grok 4.6 leads coding benchmarks at 76.8% SWE-bench vs. 38.8% for OpenAI GPT-4o.
OpenAI GPT-4o vs Llama 3.3 70B Instruct
Llama 3.3 70B Instruct is 92.8% cheaper for input tokens ($0.18 vs. $2.50 per 1M tokens) and $0.40 vs. $10.00 for output tokens (13.9x cost difference). In terms of operational performance, OpenAI GPT-4o delivers faster response latency with 280ms TTFT (140ms faster than Llama 3.3 70B Instruct). Both models demonstrate comparable coding benchmark scores.
OpenAI GPT-4o vs Mistral Large 2
Mistral Large 2 is 20.0% cheaper for input tokens ($2.00 vs. $2.50 per 1M tokens) and $6.00 vs. $10.00 for output tokens (1.2x cost difference). In terms of operational performance, OpenAI GPT-4o delivers faster response latency with 280ms TTFT (270ms faster than Mistral Large 2). Mistral Large 2 leads coding benchmarks at 39.0% SWE-bench vs. 38.8% for OpenAI GPT-4o.
OpenAI GPT-4o vs OpenAI o1
OpenAI GPT-4o is 83.3% cheaper for input tokens ($2.50 vs. $15.00 per 1M tokens) and $10.00 vs. $60.00 for output tokens (6.0x cost difference). In terms of operational performance, OpenAI GPT-4o delivers faster response latency with 280ms TTFT (570ms faster than OpenAI o1). OpenAI o1 leads coding benchmarks at 48.9% SWE-bench vs. 38.8% for OpenAI GPT-4o.
OpenAI GPT-4o vs o3-mini
o3-mini is 56.0% cheaper for input tokens ($1.10 vs. $2.50 per 1M tokens) and $4.40 vs. $10.00 for output tokens (2.3x cost difference). In terms of operational performance, OpenAI GPT-4o delivers faster response latency with 280ms TTFT (920ms faster than o3-mini). o3-mini leads coding benchmarks at 49.3% SWE-bench vs. 38.8% for OpenAI GPT-4o.
OpenAI GPT-4o vs Microsoft Phi-4 (14B)
Microsoft Phi-4 (14B) is 95.2% cheaper for input tokens ($0.12 vs. $2.50 per 1M tokens) and $0.36 vs. $10.00 for output tokens (20.8x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (170ms faster than OpenAI GPT-4o). Microsoft Phi-4 (14B) leads coding benchmarks at 42.1% SWE-bench vs. 38.8% for OpenAI GPT-4o.
OpenAI GPT-4o vs Qwen 2.5 72B Instruct
Qwen 2.5 72B Instruct is 86.0% cheaper for input tokens ($0.35 vs. $2.50 per 1M tokens) and $0.40 vs. $10.00 for output tokens (7.1x cost difference). In terms of operational performance, OpenAI GPT-4o delivers faster response latency with 280ms TTFT (140ms faster than Qwen 2.5 72B Instruct). Qwen 2.5 72B Instruct leads coding benchmarks at 44.0% SWE-bench vs. 38.8% for OpenAI GPT-4o.
OpenAI GPT-4o vs Qwen 2.5 Max
Qwen 2.5 Max is 88.8% cheaper for input tokens ($0.28 vs. $2.50 per 1M tokens) and $0.84 vs. $10.00 for output tokens (8.9x cost difference). In terms of operational performance, OpenAI GPT-4o delivers faster response latency with 280ms TTFT (200ms faster than Qwen 2.5 Max). Qwen 2.5 Max leads coding benchmarks at 44.2% SWE-bench vs. 38.8% for OpenAI GPT-4o.
OpenAI GPT-4o vs Qwen 3.8 Flash Next
Qwen 3.8 Flash Next is 95.2% cheaper for input tokens ($0.12 vs. $2.50 per 1M tokens) and $0.48 vs. $10.00 for output tokens (20.8x cost difference). In terms of operational performance, Qwen 3.8 Flash Next delivers faster response latency with 120ms TTFT (160ms faster than OpenAI GPT-4o). Qwen 3.8 Flash Next leads coding benchmarks at 56.8% SWE-bench vs. 38.8% for OpenAI GPT-4o.
GPT-5.6 Luna vs GPT-5.6 Sol
GPT-5.6 Luna is 97.8% cheaper for input tokens ($0.18 vs. $8.00 per 1M tokens) and $0.72 vs. $32.00 for output tokens (44.4x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (330ms faster than GPT-5.6 Sol). GPT-5.6 Sol leads coding benchmarks at 79.5% SWE-bench vs. 48.5% for GPT-5.6 Luna.
GPT-5.6 Luna vs GPT-5.6 Terra
GPT-5.6 Luna is 88.0% cheaper for input tokens ($0.18 vs. $1.50 per 1M tokens) and $0.72 vs. $6.00 for output tokens (8.3x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (120ms faster than GPT-5.6 Terra). GPT-5.6 Terra leads coding benchmarks at 65.4% SWE-bench vs. 48.5% for GPT-5.6 Luna.
GPT-5.6 Luna vs Grok 3
GPT-5.6 Luna is 94.0% cheaper for input tokens ($0.18 vs. $3.00 per 1M tokens) and $0.72 vs. $15.00 for output tokens (16.7x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (760ms faster than Grok 3). Grok 3 leads coding benchmarks at 58.5% SWE-bench vs. 48.5% for GPT-5.6 Luna.
GPT-5.6 Luna vs xAI Grok 4.6
GPT-5.6 Luna is 91.0% cheaper for input tokens ($0.18 vs. $2.00 per 1M tokens) and $0.72 vs. $6.00 for output tokens (11.1x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (190ms faster than xAI Grok 4.6). xAI Grok 4.6 leads coding benchmarks at 76.8% SWE-bench vs. 48.5% for GPT-5.6 Luna.
GPT-5.6 Luna vs Llama 3.3 70B Instruct
Both models share identical input pricing at $0.18 per 1M tokens. In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (330ms faster than Llama 3.3 70B Instruct). GPT-5.6 Luna leads coding benchmarks at 48.5% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
GPT-5.6 Luna vs Mistral Large 2
GPT-5.6 Luna is 91.0% cheaper for input tokens ($0.18 vs. $2.00 per 1M tokens) and $0.72 vs. $6.00 for output tokens (11.1x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (460ms faster than Mistral Large 2). GPT-5.6 Luna leads coding benchmarks at 48.5% SWE-bench vs. 39.0% for Mistral Large 2.
GPT-5.6 Luna vs OpenAI o1
GPT-5.6 Luna is 98.8% cheaper for input tokens ($0.18 vs. $15.00 per 1M tokens) and $0.72 vs. $60.00 for output tokens (83.3x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (760ms faster than OpenAI o1). OpenAI o1 leads coding benchmarks at 48.9% SWE-bench vs. 48.5% for GPT-5.6 Luna.
GPT-5.6 Luna vs o3-mini
GPT-5.6 Luna is 83.6% cheaper for input tokens ($0.18 vs. $1.10 per 1M tokens) and $0.72 vs. $4.40 for output tokens (6.1x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (1110ms faster than o3-mini). o3-mini leads coding benchmarks at 49.3% SWE-bench vs. 48.5% for GPT-5.6 Luna.
GPT-5.6 Luna vs Microsoft Phi-4 (14B)
Microsoft Phi-4 (14B) is 33.3% cheaper for input tokens ($0.12 vs. $0.18 per 1M tokens) and $0.36 vs. $0.72 for output tokens (1.5x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (20ms faster than Microsoft Phi-4 (14B)). GPT-5.6 Luna leads coding benchmarks at 48.5% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
GPT-5.6 Luna vs Qwen 2.5 72B Instruct
GPT-5.6 Luna is 48.6% cheaper for input tokens ($0.18 vs. $0.35 per 1M tokens) and $0.72 vs. $0.40 for output tokens (1.9x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (330ms faster than Qwen 2.5 72B Instruct). GPT-5.6 Luna leads coding benchmarks at 48.5% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
GPT-5.6 Luna vs Qwen 2.5 Max
GPT-5.6 Luna is 35.7% cheaper for input tokens ($0.18 vs. $0.28 per 1M tokens) and $0.72 vs. $0.84 for output tokens (1.6x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (390ms faster than Qwen 2.5 Max). GPT-5.6 Luna leads coding benchmarks at 48.5% SWE-bench vs. 44.2% for Qwen 2.5 Max.
GPT-5.6 Luna vs Qwen 3.8 Flash Next
Qwen 3.8 Flash Next is 33.3% cheaper for input tokens ($0.12 vs. $0.18 per 1M tokens) and $0.48 vs. $0.72 for output tokens (1.5x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (30ms faster than Qwen 3.8 Flash Next). Qwen 3.8 Flash Next leads coding benchmarks at 56.8% SWE-bench vs. 48.5% for GPT-5.6 Luna.
GPT-5.6 Sol vs GPT-5.6 Terra
GPT-5.6 Terra is 81.2% cheaper for input tokens ($1.50 vs. $8.00 per 1M tokens) and $6.00 vs. $32.00 for output tokens (5.3x cost difference). In terms of operational performance, GPT-5.6 Terra delivers faster response latency with 210ms TTFT (210ms faster than GPT-5.6 Sol). GPT-5.6 Sol leads coding benchmarks at 79.5% SWE-bench vs. 65.4% for GPT-5.6 Terra.
GPT-5.6 Sol vs Grok 3
Grok 3 is 62.5% cheaper for input tokens ($3.00 vs. $8.00 per 1M tokens) and $15.00 vs. $32.00 for output tokens (2.7x cost difference). In terms of operational performance, GPT-5.6 Sol delivers faster response latency with 420ms TTFT (430ms faster than Grok 3). GPT-5.6 Sol leads coding benchmarks at 79.5% SWE-bench vs. 58.5% for Grok 3.
GPT-5.6 Sol vs xAI Grok 4.6
xAI Grok 4.6 is 75.0% cheaper for input tokens ($2.00 vs. $8.00 per 1M tokens) and $6.00 vs. $32.00 for output tokens (4.0x cost difference). In terms of operational performance, xAI Grok 4.6 delivers faster response latency with 280ms TTFT (140ms faster than GPT-5.6 Sol). GPT-5.6 Sol leads coding benchmarks at 79.5% SWE-bench vs. 76.8% for xAI Grok 4.6.
GPT-5.6 Sol vs Llama 3.3 70B Instruct
Llama 3.3 70B Instruct is 97.8% cheaper for input tokens ($0.18 vs. $8.00 per 1M tokens) and $0.40 vs. $32.00 for output tokens (44.4x cost difference). In terms of operational performance, Llama 3.3 70B Instruct delivers faster response latency with 420ms TTFT (0ms faster than GPT-5.6 Sol). GPT-5.6 Sol leads coding benchmarks at 79.5% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
GPT-5.6 Sol vs Mistral Large 2
Mistral Large 2 is 75.0% cheaper for input tokens ($2.00 vs. $8.00 per 1M tokens) and $6.00 vs. $32.00 for output tokens (4.0x cost difference). In terms of operational performance, GPT-5.6 Sol delivers faster response latency with 420ms TTFT (130ms faster than Mistral Large 2). GPT-5.6 Sol leads coding benchmarks at 79.5% SWE-bench vs. 39.0% for Mistral Large 2.
GPT-5.6 Sol vs OpenAI o1
GPT-5.6 Sol is 46.7% cheaper for input tokens ($8.00 vs. $15.00 per 1M tokens) and $32.00 vs. $60.00 for output tokens (1.9x cost difference). In terms of operational performance, GPT-5.6 Sol delivers faster response latency with 420ms TTFT (430ms faster than OpenAI o1). GPT-5.6 Sol leads coding benchmarks at 79.5% SWE-bench vs. 48.9% for OpenAI o1.
GPT-5.6 Sol vs o3-mini
o3-mini is 86.2% cheaper for input tokens ($1.10 vs. $8.00 per 1M tokens) and $4.40 vs. $32.00 for output tokens (7.3x cost difference). In terms of operational performance, GPT-5.6 Sol delivers faster response latency with 420ms TTFT (780ms faster than o3-mini). GPT-5.6 Sol leads coding benchmarks at 79.5% SWE-bench vs. 49.3% for o3-mini.
GPT-5.6 Sol vs Microsoft Phi-4 (14B)
Microsoft Phi-4 (14B) is 98.5% cheaper for input tokens ($0.12 vs. $8.00 per 1M tokens) and $0.36 vs. $32.00 for output tokens (66.7x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (310ms faster than GPT-5.6 Sol). GPT-5.6 Sol leads coding benchmarks at 79.5% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
GPT-5.6 Sol vs Qwen 2.5 72B Instruct
Qwen 2.5 72B Instruct is 95.6% cheaper for input tokens ($0.35 vs. $8.00 per 1M tokens) and $0.40 vs. $32.00 for output tokens (22.9x cost difference). In terms of operational performance, Qwen 2.5 72B Instruct delivers faster response latency with 420ms TTFT (0ms faster than GPT-5.6 Sol). GPT-5.6 Sol leads coding benchmarks at 79.5% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
GPT-5.6 Sol vs Qwen 2.5 Max
Qwen 2.5 Max is 96.5% cheaper for input tokens ($0.28 vs. $8.00 per 1M tokens) and $0.84 vs. $32.00 for output tokens (28.6x cost difference). In terms of operational performance, GPT-5.6 Sol delivers faster response latency with 420ms TTFT (60ms faster than Qwen 2.5 Max). GPT-5.6 Sol leads coding benchmarks at 79.5% SWE-bench vs. 44.2% for Qwen 2.5 Max.
GPT-5.6 Sol vs Qwen 3.8 Flash Next
Qwen 3.8 Flash Next is 98.5% cheaper for input tokens ($0.12 vs. $8.00 per 1M tokens) and $0.48 vs. $32.00 for output tokens (66.7x cost difference). In terms of operational performance, Qwen 3.8 Flash Next delivers faster response latency with 120ms TTFT (300ms faster than GPT-5.6 Sol). GPT-5.6 Sol leads coding benchmarks at 79.5% SWE-bench vs. 56.8% for Qwen 3.8 Flash Next.
GPT-5.6 Terra vs Grok 3
GPT-5.6 Terra is 50.0% cheaper for input tokens ($1.50 vs. $3.00 per 1M tokens) and $6.00 vs. $15.00 for output tokens (2.0x cost difference). In terms of operational performance, GPT-5.6 Terra delivers faster response latency with 210ms TTFT (640ms faster than Grok 3). GPT-5.6 Terra leads coding benchmarks at 65.4% SWE-bench vs. 58.5% for Grok 3.
GPT-5.6 Terra vs xAI Grok 4.6
GPT-5.6 Terra is 25.0% cheaper for input tokens ($1.50 vs. $2.00 per 1M tokens) and $6.00 vs. $6.00 for output tokens (1.3x cost difference). In terms of operational performance, GPT-5.6 Terra delivers faster response latency with 210ms TTFT (70ms faster than xAI Grok 4.6). xAI Grok 4.6 leads coding benchmarks at 76.8% SWE-bench vs. 65.4% for GPT-5.6 Terra.
GPT-5.6 Terra vs Llama 3.3 70B Instruct
Llama 3.3 70B Instruct is 88.0% cheaper for input tokens ($0.18 vs. $1.50 per 1M tokens) and $0.40 vs. $6.00 for output tokens (8.3x cost difference). In terms of operational performance, GPT-5.6 Terra delivers faster response latency with 210ms TTFT (210ms faster than Llama 3.3 70B Instruct). GPT-5.6 Terra leads coding benchmarks at 65.4% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
GPT-5.6 Terra vs Mistral Large 2
GPT-5.6 Terra is 25.0% cheaper for input tokens ($1.50 vs. $2.00 per 1M tokens) and $6.00 vs. $6.00 for output tokens (1.3x cost difference). In terms of operational performance, GPT-5.6 Terra delivers faster response latency with 210ms TTFT (340ms faster than Mistral Large 2). GPT-5.6 Terra leads coding benchmarks at 65.4% SWE-bench vs. 39.0% for Mistral Large 2.
GPT-5.6 Terra vs OpenAI o1
GPT-5.6 Terra is 90.0% cheaper for input tokens ($1.50 vs. $15.00 per 1M tokens) and $6.00 vs. $60.00 for output tokens (10.0x cost difference). In terms of operational performance, GPT-5.6 Terra delivers faster response latency with 210ms TTFT (640ms faster than OpenAI o1). GPT-5.6 Terra leads coding benchmarks at 65.4% SWE-bench vs. 48.9% for OpenAI o1.
GPT-5.6 Terra vs o3-mini
o3-mini is 26.7% cheaper for input tokens ($1.10 vs. $1.50 per 1M tokens) and $4.40 vs. $6.00 for output tokens (1.4x cost difference). In terms of operational performance, GPT-5.6 Terra delivers faster response latency with 210ms TTFT (990ms faster than o3-mini). GPT-5.6 Terra leads coding benchmarks at 65.4% SWE-bench vs. 49.3% for o3-mini.
GPT-5.6 Terra vs Microsoft Phi-4 (14B)
Microsoft Phi-4 (14B) is 92.0% cheaper for input tokens ($0.12 vs. $1.50 per 1M tokens) and $0.36 vs. $6.00 for output tokens (12.5x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (100ms faster than GPT-5.6 Terra). GPT-5.6 Terra leads coding benchmarks at 65.4% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
GPT-5.6 Terra vs Qwen 2.5 72B Instruct
Qwen 2.5 72B Instruct is 76.7% cheaper for input tokens ($0.35 vs. $1.50 per 1M tokens) and $0.40 vs. $6.00 for output tokens (4.3x cost difference). In terms of operational performance, GPT-5.6 Terra delivers faster response latency with 210ms TTFT (210ms faster than Qwen 2.5 72B Instruct). GPT-5.6 Terra leads coding benchmarks at 65.4% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
GPT-5.6 Terra vs Qwen 2.5 Max
Qwen 2.5 Max is 81.3% cheaper for input tokens ($0.28 vs. $1.50 per 1M tokens) and $0.84 vs. $6.00 for output tokens (5.4x cost difference). In terms of operational performance, GPT-5.6 Terra delivers faster response latency with 210ms TTFT (270ms faster than Qwen 2.5 Max). GPT-5.6 Terra leads coding benchmarks at 65.4% SWE-bench vs. 44.2% for Qwen 2.5 Max.
GPT-5.6 Terra vs Qwen 3.8 Flash Next
Qwen 3.8 Flash Next is 92.0% cheaper for input tokens ($0.12 vs. $1.50 per 1M tokens) and $0.48 vs. $6.00 for output tokens (12.5x cost difference). In terms of operational performance, Qwen 3.8 Flash Next delivers faster response latency with 120ms TTFT (90ms faster than GPT-5.6 Terra). GPT-5.6 Terra leads coding benchmarks at 65.4% SWE-bench vs. 56.8% for Qwen 3.8 Flash Next.
Grok 3 vs xAI Grok 4.6
xAI Grok 4.6 is 33.3% cheaper for input tokens ($2.00 vs. $3.00 per 1M tokens) and $6.00 vs. $15.00 for output tokens (1.5x cost difference). In terms of operational performance, xAI Grok 4.6 delivers faster response latency with 280ms TTFT (570ms faster than Grok 3). xAI Grok 4.6 leads coding benchmarks at 76.8% SWE-bench vs. 58.5% for Grok 3.
Grok 3 vs Llama 3.3 70B Instruct
Llama 3.3 70B Instruct is 94.0% cheaper for input tokens ($0.18 vs. $3.00 per 1M tokens) and $0.40 vs. $15.00 for output tokens (16.7x cost difference). In terms of operational performance, Llama 3.3 70B Instruct delivers faster response latency with 420ms TTFT (430ms faster than Grok 3). Grok 3 leads coding benchmarks at 58.5% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Grok 3 vs Mistral Large 2
Mistral Large 2 is 33.3% cheaper for input tokens ($2.00 vs. $3.00 per 1M tokens) and $6.00 vs. $15.00 for output tokens (1.5x cost difference). In terms of operational performance, Mistral Large 2 delivers faster response latency with 550ms TTFT (300ms faster than Grok 3). Grok 3 leads coding benchmarks at 58.5% SWE-bench vs. 39.0% for Mistral Large 2.
Grok 3 vs OpenAI o1
Grok 3 is 80.0% cheaper for input tokens ($3.00 vs. $15.00 per 1M tokens) and $15.00 vs. $60.00 for output tokens (5.0x cost difference). In terms of operational performance, OpenAI o1 delivers faster response latency with 850ms TTFT (0ms faster than Grok 3). Grok 3 leads coding benchmarks at 58.5% SWE-bench vs. 48.9% for OpenAI o1.
Grok 3 vs o3-mini
o3-mini is 63.3% cheaper for input tokens ($1.10 vs. $3.00 per 1M tokens) and $4.40 vs. $15.00 for output tokens (2.7x cost difference). In terms of operational performance, Grok 3 delivers faster response latency with 850ms TTFT (350ms faster than o3-mini). Grok 3 leads coding benchmarks at 58.5% SWE-bench vs. 49.3% for o3-mini.
Grok 3 vs Microsoft Phi-4 (14B)
Microsoft Phi-4 (14B) is 96.0% cheaper for input tokens ($0.12 vs. $3.00 per 1M tokens) and $0.36 vs. $15.00 for output tokens (25.0x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (740ms faster than Grok 3). Grok 3 leads coding benchmarks at 58.5% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
Grok 3 vs Qwen 2.5 72B Instruct
Qwen 2.5 72B Instruct is 88.3% cheaper for input tokens ($0.35 vs. $3.00 per 1M tokens) and $0.40 vs. $15.00 for output tokens (8.6x cost difference). In terms of operational performance, Qwen 2.5 72B Instruct delivers faster response latency with 420ms TTFT (430ms faster than Grok 3). Grok 3 leads coding benchmarks at 58.5% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
Grok 3 vs Qwen 2.5 Max
Qwen 2.5 Max is 90.7% cheaper for input tokens ($0.28 vs. $3.00 per 1M tokens) and $0.84 vs. $15.00 for output tokens (10.7x cost difference). In terms of operational performance, Qwen 2.5 Max delivers faster response latency with 480ms TTFT (370ms faster than Grok 3). Grok 3 leads coding benchmarks at 58.5% SWE-bench vs. 44.2% for Qwen 2.5 Max.
Grok 3 vs Qwen 3.8 Flash Next
Qwen 3.8 Flash Next is 96.0% cheaper for input tokens ($0.12 vs. $3.00 per 1M tokens) and $0.48 vs. $15.00 for output tokens (25.0x cost difference). In terms of operational performance, Qwen 3.8 Flash Next delivers faster response latency with 120ms TTFT (730ms faster than Grok 3). Grok 3 leads coding benchmarks at 58.5% SWE-bench vs. 56.8% for Qwen 3.8 Flash Next.
xAI Grok 4.6 vs Llama 3.3 70B Instruct
Llama 3.3 70B Instruct is 91.0% cheaper for input tokens ($0.18 vs. $2.00 per 1M tokens) and $0.40 vs. $6.00 for output tokens (11.1x cost difference). In terms of operational performance, xAI Grok 4.6 delivers faster response latency with 280ms TTFT (140ms faster than Llama 3.3 70B Instruct). xAI Grok 4.6 leads coding benchmarks at 76.8% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
xAI Grok 4.6 vs Mistral Large 2
Both models share identical input pricing at $2.00 per 1M tokens. In terms of operational performance, xAI Grok 4.6 delivers faster response latency with 280ms TTFT (270ms faster than Mistral Large 2). xAI Grok 4.6 leads coding benchmarks at 76.8% SWE-bench vs. 39.0% for Mistral Large 2.
xAI Grok 4.6 vs OpenAI o1
xAI Grok 4.6 is 86.7% cheaper for input tokens ($2.00 vs. $15.00 per 1M tokens) and $6.00 vs. $60.00 for output tokens (7.5x cost difference). In terms of operational performance, xAI Grok 4.6 delivers faster response latency with 280ms TTFT (570ms faster than OpenAI o1). xAI Grok 4.6 leads coding benchmarks at 76.8% SWE-bench vs. 48.9% for OpenAI o1.
xAI Grok 4.6 vs o3-mini
o3-mini is 45.0% cheaper for input tokens ($1.10 vs. $2.00 per 1M tokens) and $4.40 vs. $6.00 for output tokens (1.8x cost difference). In terms of operational performance, xAI Grok 4.6 delivers faster response latency with 280ms TTFT (920ms faster than o3-mini). xAI Grok 4.6 leads coding benchmarks at 76.8% SWE-bench vs. 49.3% for o3-mini.
xAI Grok 4.6 vs Microsoft Phi-4 (14B)
Microsoft Phi-4 (14B) is 94.0% cheaper for input tokens ($0.12 vs. $2.00 per 1M tokens) and $0.36 vs. $6.00 for output tokens (16.7x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (170ms faster than xAI Grok 4.6). xAI Grok 4.6 leads coding benchmarks at 76.8% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
xAI Grok 4.6 vs Qwen 2.5 72B Instruct
Qwen 2.5 72B Instruct is 82.5% cheaper for input tokens ($0.35 vs. $2.00 per 1M tokens) and $0.40 vs. $6.00 for output tokens (5.7x cost difference). In terms of operational performance, xAI Grok 4.6 delivers faster response latency with 280ms TTFT (140ms faster than Qwen 2.5 72B Instruct). xAI Grok 4.6 leads coding benchmarks at 76.8% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
xAI Grok 4.6 vs Qwen 2.5 Max
Qwen 2.5 Max is 86.0% cheaper for input tokens ($0.28 vs. $2.00 per 1M tokens) and $0.84 vs. $6.00 for output tokens (7.1x cost difference). In terms of operational performance, xAI Grok 4.6 delivers faster response latency with 280ms TTFT (200ms faster than Qwen 2.5 Max). xAI Grok 4.6 leads coding benchmarks at 76.8% SWE-bench vs. 44.2% for Qwen 2.5 Max.
xAI Grok 4.6 vs Qwen 3.8 Flash Next
Qwen 3.8 Flash Next is 94.0% cheaper for input tokens ($0.12 vs. $2.00 per 1M tokens) and $0.48 vs. $6.00 for output tokens (16.7x cost difference). In terms of operational performance, Qwen 3.8 Flash Next delivers faster response latency with 120ms TTFT (160ms faster than xAI Grok 4.6). xAI Grok 4.6 leads coding benchmarks at 76.8% SWE-bench vs. 56.8% for Qwen 3.8 Flash Next.
Llama 3.3 70B Instruct vs Mistral Large 2
Llama 3.3 70B Instruct is 91.0% cheaper for input tokens ($0.18 vs. $2.00 per 1M tokens) and $0.40 vs. $6.00 for output tokens (11.1x cost difference). In terms of operational performance, Llama 3.3 70B Instruct delivers faster response latency with 420ms TTFT (130ms faster than Mistral Large 2). Mistral Large 2 leads coding benchmarks at 39.0% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Llama 3.3 70B Instruct vs OpenAI o1
Llama 3.3 70B Instruct is 98.8% cheaper for input tokens ($0.18 vs. $15.00 per 1M tokens) and $0.40 vs. $60.00 for output tokens (83.3x cost difference). In terms of operational performance, Llama 3.3 70B Instruct delivers faster response latency with 420ms TTFT (430ms faster than OpenAI o1). OpenAI o1 leads coding benchmarks at 48.9% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Llama 3.3 70B Instruct vs o3-mini
Llama 3.3 70B Instruct is 83.6% cheaper for input tokens ($0.18 vs. $1.10 per 1M tokens) and $0.40 vs. $4.40 for output tokens (6.1x cost difference). In terms of operational performance, Llama 3.3 70B Instruct delivers faster response latency with 420ms TTFT (780ms faster than o3-mini). o3-mini leads coding benchmarks at 49.3% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Llama 3.3 70B Instruct vs Microsoft Phi-4 (14B)
Microsoft Phi-4 (14B) is 33.3% cheaper for input tokens ($0.12 vs. $0.18 per 1M tokens) and $0.36 vs. $0.40 for output tokens (1.5x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (310ms faster than Llama 3.3 70B Instruct). Microsoft Phi-4 (14B) leads coding benchmarks at 42.1% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Llama 3.3 70B Instruct vs Qwen 2.5 72B Instruct
Llama 3.3 70B Instruct is 48.6% cheaper for input tokens ($0.18 vs. $0.35 per 1M tokens) and $0.40 vs. $0.40 for output tokens (1.9x cost difference). In terms of operational performance, Qwen 2.5 72B Instruct delivers faster response latency with 420ms TTFT (0ms faster than Llama 3.3 70B Instruct). Qwen 2.5 72B Instruct leads coding benchmarks at 44.0% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Llama 3.3 70B Instruct vs Qwen 2.5 Max
Llama 3.3 70B Instruct is 35.7% cheaper for input tokens ($0.18 vs. $0.28 per 1M tokens) and $0.40 vs. $0.84 for output tokens (1.6x cost difference). In terms of operational performance, Llama 3.3 70B Instruct delivers faster response latency with 420ms TTFT (60ms faster than Qwen 2.5 Max). Qwen 2.5 Max leads coding benchmarks at 44.2% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Llama 3.3 70B Instruct vs Qwen 3.8 Flash Next
Qwen 3.8 Flash Next is 33.3% cheaper for input tokens ($0.12 vs. $0.18 per 1M tokens) and $0.48 vs. $0.40 for output tokens (1.5x cost difference). In terms of operational performance, Qwen 3.8 Flash Next delivers faster response latency with 120ms TTFT (300ms faster than Llama 3.3 70B Instruct). Qwen 3.8 Flash Next leads coding benchmarks at 56.8% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Mistral Large 2 vs OpenAI o1
Mistral Large 2 is 86.7% cheaper for input tokens ($2.00 vs. $15.00 per 1M tokens) and $6.00 vs. $60.00 for output tokens (7.5x cost difference). In terms of operational performance, Mistral Large 2 delivers faster response latency with 550ms TTFT (300ms faster than OpenAI o1). OpenAI o1 leads coding benchmarks at 48.9% SWE-bench vs. 39.0% for Mistral Large 2.
Mistral Large 2 vs o3-mini
o3-mini is 45.0% cheaper for input tokens ($1.10 vs. $2.00 per 1M tokens) and $4.40 vs. $6.00 for output tokens (1.8x cost difference). In terms of operational performance, Mistral Large 2 delivers faster response latency with 550ms TTFT (650ms faster than o3-mini). o3-mini leads coding benchmarks at 49.3% SWE-bench vs. 39.0% for Mistral Large 2.
Mistral Large 2 vs Microsoft Phi-4 (14B)
Microsoft Phi-4 (14B) is 94.0% cheaper for input tokens ($0.12 vs. $2.00 per 1M tokens) and $0.36 vs. $6.00 for output tokens (16.7x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (440ms faster than Mistral Large 2). Microsoft Phi-4 (14B) leads coding benchmarks at 42.1% SWE-bench vs. 39.0% for Mistral Large 2.
Mistral Large 2 vs Qwen 2.5 72B Instruct
Qwen 2.5 72B Instruct is 82.5% cheaper for input tokens ($0.35 vs. $2.00 per 1M tokens) and $0.40 vs. $6.00 for output tokens (5.7x cost difference). In terms of operational performance, Qwen 2.5 72B Instruct delivers faster response latency with 420ms TTFT (130ms faster than Mistral Large 2). Qwen 2.5 72B Instruct leads coding benchmarks at 44.0% SWE-bench vs. 39.0% for Mistral Large 2.
Mistral Large 2 vs Qwen 2.5 Max
Qwen 2.5 Max is 86.0% cheaper for input tokens ($0.28 vs. $2.00 per 1M tokens) and $0.84 vs. $6.00 for output tokens (7.1x cost difference). In terms of operational performance, Qwen 2.5 Max delivers faster response latency with 480ms TTFT (70ms faster than Mistral Large 2). Qwen 2.5 Max leads coding benchmarks at 44.2% SWE-bench vs. 39.0% for Mistral Large 2.
Mistral Large 2 vs Qwen 3.8 Flash Next
Qwen 3.8 Flash Next is 94.0% cheaper for input tokens ($0.12 vs. $2.00 per 1M tokens) and $0.48 vs. $6.00 for output tokens (16.7x cost difference). In terms of operational performance, Qwen 3.8 Flash Next delivers faster response latency with 120ms TTFT (430ms faster than Mistral Large 2). Qwen 3.8 Flash Next leads coding benchmarks at 56.8% SWE-bench vs. 39.0% for Mistral Large 2.
OpenAI o1 vs o3-mini
o3-mini is 92.7% cheaper for input tokens ($1.10 vs. $15.00 per 1M tokens) and $4.40 vs. $60.00 for output tokens (13.6x cost difference). In terms of operational performance, OpenAI o1 delivers faster response latency with 850ms TTFT (350ms faster than o3-mini). o3-mini leads coding benchmarks at 49.3% SWE-bench vs. 48.9% for OpenAI o1.
OpenAI o1 vs Microsoft Phi-4 (14B)
Microsoft Phi-4 (14B) is 99.2% cheaper for input tokens ($0.12 vs. $15.00 per 1M tokens) and $0.36 vs. $60.00 for output tokens (125.0x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (740ms faster than OpenAI o1). OpenAI o1 leads coding benchmarks at 48.9% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
OpenAI o1 vs Qwen 2.5 72B Instruct
Qwen 2.5 72B Instruct is 97.7% cheaper for input tokens ($0.35 vs. $15.00 per 1M tokens) and $0.40 vs. $60.00 for output tokens (42.9x cost difference). In terms of operational performance, Qwen 2.5 72B Instruct delivers faster response latency with 420ms TTFT (430ms faster than OpenAI o1). OpenAI o1 leads coding benchmarks at 48.9% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
OpenAI o1 vs Qwen 2.5 Max
Qwen 2.5 Max is 98.1% cheaper for input tokens ($0.28 vs. $15.00 per 1M tokens) and $0.84 vs. $60.00 for output tokens (53.6x cost difference). In terms of operational performance, Qwen 2.5 Max delivers faster response latency with 480ms TTFT (370ms faster than OpenAI o1). OpenAI o1 leads coding benchmarks at 48.9% SWE-bench vs. 44.2% for Qwen 2.5 Max.
OpenAI o1 vs Qwen 3.8 Flash Next
Qwen 3.8 Flash Next is 99.2% cheaper for input tokens ($0.12 vs. $15.00 per 1M tokens) and $0.48 vs. $60.00 for output tokens (125.0x cost difference). In terms of operational performance, Qwen 3.8 Flash Next delivers faster response latency with 120ms TTFT (730ms faster than OpenAI o1). Qwen 3.8 Flash Next leads coding benchmarks at 56.8% SWE-bench vs. 48.9% for OpenAI o1.
o3-mini vs Microsoft Phi-4 (14B)
Microsoft Phi-4 (14B) is 89.1% cheaper for input tokens ($0.12 vs. $1.10 per 1M tokens) and $0.36 vs. $4.40 for output tokens (9.2x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (1090ms faster than o3-mini). o3-mini leads coding benchmarks at 49.3% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
o3-mini vs Qwen 2.5 72B Instruct
Qwen 2.5 72B Instruct is 68.2% cheaper for input tokens ($0.35 vs. $1.10 per 1M tokens) and $0.40 vs. $4.40 for output tokens (3.1x cost difference). In terms of operational performance, Qwen 2.5 72B Instruct delivers faster response latency with 420ms TTFT (780ms faster than o3-mini). o3-mini leads coding benchmarks at 49.3% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
o3-mini vs Qwen 2.5 Max
Qwen 2.5 Max is 74.5% cheaper for input tokens ($0.28 vs. $1.10 per 1M tokens) and $0.84 vs. $4.40 for output tokens (3.9x cost difference). In terms of operational performance, Qwen 2.5 Max delivers faster response latency with 480ms TTFT (720ms faster than o3-mini). o3-mini leads coding benchmarks at 49.3% SWE-bench vs. 44.2% for Qwen 2.5 Max.
o3-mini vs Qwen 3.8 Flash Next
Qwen 3.8 Flash Next is 89.1% cheaper for input tokens ($0.12 vs. $1.10 per 1M tokens) and $0.48 vs. $4.40 for output tokens (9.2x cost difference). In terms of operational performance, Qwen 3.8 Flash Next delivers faster response latency with 120ms TTFT (1080ms faster than o3-mini). Qwen 3.8 Flash Next leads coding benchmarks at 56.8% SWE-bench vs. 49.3% for o3-mini.
Microsoft Phi-4 (14B) vs Qwen 2.5 72B Instruct
Microsoft Phi-4 (14B) is 65.7% cheaper for input tokens ($0.12 vs. $0.35 per 1M tokens) and $0.36 vs. $0.40 for output tokens (2.9x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (310ms faster than Qwen 2.5 72B Instruct). Qwen 2.5 72B Instruct leads coding benchmarks at 44.0% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
Microsoft Phi-4 (14B) vs Qwen 2.5 Max
Microsoft Phi-4 (14B) is 57.1% cheaper for input tokens ($0.12 vs. $0.28 per 1M tokens) and $0.36 vs. $0.84 for output tokens (2.3x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (370ms faster than Qwen 2.5 Max). Qwen 2.5 Max leads coding benchmarks at 44.2% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
Microsoft Phi-4 (14B) vs Qwen 3.8 Flash Next
Both models share identical input pricing at $0.12 per 1M tokens. In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (10ms faster than Qwen 3.8 Flash Next). Qwen 3.8 Flash Next leads coding benchmarks at 56.8% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
Qwen 2.5 72B Instruct vs Qwen 2.5 Max
Qwen 2.5 Max is 20.0% cheaper for input tokens ($0.28 vs. $0.35 per 1M tokens) and $0.84 vs. $0.40 for output tokens (1.2x cost difference). In terms of operational performance, Qwen 2.5 72B Instruct delivers faster response latency with 420ms TTFT (60ms faster than Qwen 2.5 Max). Qwen 2.5 Max leads coding benchmarks at 44.2% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
Qwen 2.5 72B Instruct vs Qwen 3.8 Flash Next
Qwen 3.8 Flash Next is 65.7% cheaper for input tokens ($0.12 vs. $0.35 per 1M tokens) and $0.48 vs. $0.40 for output tokens (2.9x cost difference). In terms of operational performance, Qwen 3.8 Flash Next delivers faster response latency with 120ms TTFT (300ms faster than Qwen 2.5 72B Instruct). Qwen 3.8 Flash Next leads coding benchmarks at 56.8% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
Qwen 2.5 Max vs Qwen 3.8 Flash Next
Qwen 3.8 Flash Next is 57.1% cheaper for input tokens ($0.12 vs. $0.28 per 1M tokens) and $0.48 vs. $0.84 for output tokens (2.3x cost difference). In terms of operational performance, Qwen 3.8 Flash Next delivers faster response latency with 120ms TTFT (360ms faster than Qwen 2.5 Max). Qwen 3.8 Flash Next leads coding benchmarks at 56.8% SWE-bench vs. 44.2% for Qwen 2.5 Max.