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Alibaba Cloud (Qwen)Qwen3.7-MaxVSMistral AICodestral 22B

Analysis by:the whichllmmodel Editorial Team|Updated: June 2026

Our Take

Standardized reasoning and coding benchmarks are currently pending verification for one or both of these models. We recommend testing Qwen3.7-Max and Codestral 22B directly in their respective provider playgrounds to see which fits your specific prompt styles best.
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Model Specs

Qwen3.7-Max

Benchmarks & Scores

Coding (swe-bench-pro)
60.6%

complex codebases, multi-file repositories, and architectural planning

Reasoning (gpqa-diamond)
92.4%

graduate-level science QA

Cost & Context

Cost (per 1M tokens)
$3.75Input: $2.50 | Output: $7.50
Context WindowLarger
1.05M tokens
Model Specs

Codestral 22B

Open SourceAPI Available

Benchmarks & Scores

Coding (human-eval)
81.1%

basic standalone code completion and simple functions

Reasoning (gpqa-diamond)
N/A

graduate-level science QA

Cost & Context

Cost (per 1M tokens)8.3x cheaper
$0.45Input: $0.30 | Output: $0.90
Context Window
32.77k tokens

Frequently Asked Questions about Qwen3.7-Max vs Codestral 22B

Codestral 22B is cheaper than Qwen3.7-Max. Codestral 22B has a blended cost of $0.45/1M tokens, which is about 8.3x cheaper than Qwen3.7-Max at $3.75/1M tokens.

For coding tasks, Qwen3.7-Max scores 60.6% on swe-bench-pro (complex codebases, multi-file repositories, and architectural planning), while Codestral 22B scores 81.1% on human-eval (basic standalone code completion and simple functions).

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