Qwen 2.5 Max
Proprietary CommercialDeveloped by Alibaba Cloud · Released 2025-01-01
What are the token costs and operational benchmarks for Qwen 2.5 Max?
Architectural Overview
Frontier foundation model developed by Alibaba Cloud featuring MoE architecture.
Optimal Production Use Cases
High-performance enterprise reasoning across Asian and Western languages with advanced structured output handling.
Interactive Monthly Token Economics & ROI Forecaster
Model your expected production workload across prompt (input) and completion (output) tokens.
Estimated Monthly Spend
$0.28/1M in · $0.84/1M out
$0.18/1M in · $0.72/1M out
Head-to-Head Comparisons Involving Qwen 2.5 Max
Qwen 2.5 Max vs Claude 3.5 Haiku
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.
Qwen 2.5 Max vs Claude 3.5 Sonnet
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.
Qwen 2.5 Max vs Claude 3.7 Sonnet
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.
Qwen 2.5 Max vs Claude Opus 5
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.
Qwen 2.5 Max vs Codestral 25.01
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.
Qwen 2.5 Max vs Composer 2.5
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.
Qwen 2.5 Max vs DeepSeek-R1
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.
Qwen 2.5 Max vs DeepSeek-V3
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.
Qwen 2.5 Max vs DeepSeek-V4 Flash
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.
Qwen 2.5 Max vs Fable 5
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.
Qwen 2.5 Max vs Gemini 2.0 Flash
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.
Qwen 2.5 Max vs Gemini 3.7 Flash
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.
Qwen 2.5 Max vs GLM 5.3 Flash
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.
Qwen 2.5 Max vs OpenAI GPT-4o
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.
Qwen 2.5 Max vs GPT-5.6 Luna
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.
Qwen 2.5 Max vs GPT-5.6 Sol
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.
Qwen 2.5 Max vs GPT-5.6 Terra
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.
Qwen 2.5 Max vs Grok 3
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.
Qwen 2.5 Max vs xAI Grok 4.6
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.
Qwen 2.5 Max vs Llama 3.3 70B Instruct
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.
Qwen 2.5 Max vs Mistral Large 2
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.
Qwen 2.5 Max vs OpenAI o1
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.
Qwen 2.5 Max vs o3-mini
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.
Qwen 2.5 Max vs Microsoft Phi-4 (14B)
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).
Qwen 2.5 Max vs Qwen 2.5 72B Instruct
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 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.
Frequently Asked Questions & Query Fan-Out
How much does Qwen 2.5 Max cost per 1M tokens?
Qwen 2.5 Max costs $0.28 per million prompt (input) tokens and $0.84 per million completion (output) tokens.
What is the context window for Qwen 2.5 Max?
Qwen 2.5 Max supports a maximum context window of 131,072 tokens, with a maximum single-generation output of 8,192 tokens.
What are the primary use cases for Qwen 2.5 Max?
High-performance enterprise reasoning across Asian and Western languages with advanced structured output handling.