GoogleGemini 3.1 ProVSMistral 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 Gemini 3.1 Pro and Codestral 22B directly in their respective provider playgrounds to see which fits your specific prompt styles best.

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Model Specs

Gemini 3.1 Pro

Website

Benchmarks & Scores

Coding (swe-bench-pro)

54.2%

complex codebases, multi-file repositories, and architectural planning

Reasoning (gpqa-diamond)

94.3%

graduate-level science QA

Cost & Context

Cost (per 1M tokens)

$4.50Input: $2.00 | Output: $12.00

Context WindowLarger

1.05M tokens

Model Specs

Codestral 22B

Open SourceAPI Available

Website 🤗HF

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)10.0x cheaper

$0.45Input: $0.30 | Output: $0.90

Context Window

32.77k tokens

Read our data collection methodology

Frequently Asked Questions about Gemini 3.1 Pro vs Codestral 22B

Codestral 22B is cheaper than Gemini 3.1 Pro. Codestral 22B has a blended cost of $0.45/1M tokens, which is about 10.0x cheaper than Gemini 3.1 Pro at $4.50/1M tokens.

For coding tasks, Gemini 3.1 Pro scores 54.2% 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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Mistral AICodestral 22B

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