Model Comparison

Codestral-22B vs Pixtral-12BWhich is better in 2026?

Codestral-22B significantly outperforms across most benchmarks.

Verdict: Codestral-22B vs Pixtral-12B — which is better?

Codestral-22B (by Mistral AI) and Pixtral-12B (by Mistral AI) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.

Codestral-22B outperforms in 1 benchmarks (HumanEval), while Pixtral-12B is better at 0 benchmarks. Codestral-22B significantly outperforms across most benchmarks.

Choose Codestral-22B if…

  • you want the strongest raw capability — it leads on 1 of 1 shared benchmarks

Choose Pixtral-12B if…

  • you want the most recent training data — it shipped Sep 2024
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Performance Benchmarks

Comparative analysis across standard metrics

1 benchmarks

Codestral-22B outperforms in 1 benchmarks (HumanEval), while Pixtral-12B is better at 0 benchmarks.

Codestral-22B significantly outperforms across most benchmarks.

Sat Jun 06 2026 • llm-stats.com

Arena Performance

Human preference votes

Model Size

Parameter count comparison

9.8B diff

Codestral-22B has 9.8B more parameters than Pixtral-12B, making it 79.0% larger.

Mistral AI
Codestral-22B
22.2Bparameters
Mistral AI
Pixtral-12B
12.4Bparameters
22.2B
Codestral-22B
12.4B
Pixtral-12B

Context Window

Maximum input and output token capacity

Only Pixtral-12B specifies input context (128,000 tokens). Only Pixtral-12B specifies output context (8,192 tokens).

Mistral AI
Codestral-22B
Input- tokens
Output- tokens
Mistral AI
Pixtral-12B
Input128,000 tokens
Output8,192 tokens
Sat Jun 06 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Pixtral-12B supports multimodal inputs, whereas Codestral-22B does not.

Pixtral-12B can handle both text and other forms of data like images, making it suitable for multimodal applications.

Codestral-22B

Text
Images
Audio
Video

Pixtral-12B

Text
Images
Audio
Video

License

Usage and distribution terms

Codestral-22B is licensed under MNPL-0.1, while Pixtral-12B uses Apache 2.0.

License differences may affect how you can use these models in commercial or open-source projects.

Codestral-22B

MNPL-0.1

Open weights

Pixtral-12B

Apache 2.0

Open weights

Release Timeline

When each model was launched

Codestral-22B was released on 2024-05-29, while Pixtral-12B was released on 2024-09-17.

Pixtral-12B is 4 months newer than Codestral-22B.

Codestral-22B

May 29, 2024

2.0 years ago

Pixtral-12B

Sep 17, 2024

1.7 years ago

3mo newer

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Mistral AI
Higher HumanEval score (81.1% vs 72.0%)
Mistral AI
Larger context window (128,000 tokens)
Supports multimodal inputs

Detailed Comparison

AI Model Comparison Table
Feature
Mistral AI
Codestral-22B
Mistral AI
Pixtral-12B

FAQ

Common questions about Codestral-22B vs Pixtral-12B.

Which is better, Codestral-22B or Pixtral-12B?

Codestral-22B significantly outperforms across most benchmarks. Codestral-22B is made by Mistral AI and Pixtral-12B is made by Mistral AI. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does Codestral-22B compare to Pixtral-12B in benchmarks?

Codestral-22B scores HumanEvalFIM-Average: 91.6%, HumanEval: 81.1%, MBPP: 78.2%, Spider: 63.5%, HumanEval-Average: 61.5%. Pixtral-12B scores DocVQA: 90.7%, ChartQA: 81.8%, VQAv2: 78.6%, MT-Bench: 76.8%, HumanEval: 72.0%.

What are the context window sizes for Codestral-22B and Pixtral-12B?

Codestral-22B supports an unknown number of tokens and Pixtral-12B supports 128K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Codestral-22B and Pixtral-12B?

Key differences include multimodal support (no vs yes), licensing (MNPL-0.1 vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

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