GPT-5.6 Sol
Proprietary CommercialDeveloped by OpenAI · Released 2025-01-01
What are the token costs and operational benchmarks for GPT-5.6 Sol?
Architectural Overview
Frontier foundation model developed by OpenAI featuring Flagship Autonomous Reasoning Cluster architecture.
Optimal Production Use Cases
High-order STEM research, autonomous enterprise executive workflow coordination, and heavy deep-reasoning pipelines.
Interactive Monthly Token Economics & ROI Forecaster
Model your expected production workload across prompt (input) and completion (output) tokens.
Estimated Monthly Spend
$8/1M in · $32/1M out
$0.18/1M in · $0.72/1M out
Head-to-Head Comparisons Involving GPT-5.6 Sol
GPT-5.6 Sol vs Claude 3.5 Haiku
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.
GPT-5.6 Sol vs Claude 3.5 Sonnet
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.
GPT-5.6 Sol vs Claude 3.7 Sonnet
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.
GPT-5.6 Sol vs Claude Opus 5
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.
GPT-5.6 Sol vs Codestral 25.01
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.
GPT-5.6 Sol vs Composer 2.5
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.
GPT-5.6 Sol vs DeepSeek-R1
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.
GPT-5.6 Sol vs DeepSeek-V3
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.
GPT-5.6 Sol vs DeepSeek-V4 Flash
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.
GPT-5.6 Sol vs Fable 5
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.
GPT-5.6 Sol vs Gemini 2.0 Flash
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.
GPT-5.6 Sol vs Gemini 3.7 Flash
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.
GPT-5.6 Sol vs GLM 5.3 Flash
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.
GPT-5.6 Sol vs OpenAI GPT-4o
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.
GPT-5.6 Sol vs GPT-5.6 Luna
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 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.
Frequently Asked Questions & Query Fan-Out
How much does GPT-5.6 Sol cost per 1M tokens?
GPT-5.6 Sol costs $8.00 per million prompt (input) tokens and $32.00 per million completion (output) tokens.
What is the context window for GPT-5.6 Sol?
GPT-5.6 Sol supports a maximum context window of 512,000 tokens, with a maximum single-generation output of 128,000 tokens.
What are the primary use cases for GPT-5.6 Sol?
High-order STEM research, autonomous enterprise executive workflow coordination, and heavy deep-reasoning pipelines.