Model Comparison

Codestral-22B vs DeepSeek-V3

Comparing Codestral-22B and DeepSeek-V3 across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

Codestral-22B and DeepSeek-V3 don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Human preference votes

Model Size

Parameter count comparison

648.8B diff

DeepSeek-V3 has 648.8B more parameters than Codestral-22B, making it 2922.5% larger.

Mistral AI
Codestral-22B
22.2Bparameters
DeepSeek
DeepSeek-V3
671.0Bparameters
22.2B
Codestral-22B
671.0B
DeepSeek-V3

Context Window

Maximum input and output token capacity

Only DeepSeek-V3 specifies input context (131,072 tokens). Only DeepSeek-V3 specifies output context (131,072 tokens).

Mistral AI
Codestral-22B
Input- tokens
Output- tokens
DeepSeek
DeepSeek-V3
Input131,072 tokens
Output131,072 tokens
Thu May 07 2026 • llm-stats.com

License

Usage and distribution terms

Codestral-22B is licensed under MNPL-0.1, while DeepSeek-V3 uses MIT + Model License (Commercial use allowed).

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

Codestral-22B

MNPL-0.1

Open weights

DeepSeek-V3

MIT + Model License (Commercial use allowed)

Open weights

Release Timeline

When each model was launched

Codestral-22B was released on 2024-05-29, while DeepSeek-V3 was released on 2024-12-25.

DeepSeek-V3 is 7 months newer than Codestral-22B.

Codestral-22B

May 29, 2024

1.9 years ago

DeepSeek-V3

Dec 25, 2024

1.4 years ago

7mo 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

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Key Takeaways

Mistral AI

No standout differentiators in the data we have for this pair.

DeepSeek
Larger context window (131,072 tokens)

Detailed Comparison

AI Model Comparison Table
Feature
Mistral AI
Codestral-22B
DeepSeek
DeepSeek-V3

FAQ

Common questions about Codestral-22B vs DeepSeek-V3.

Which is better, Codestral-22B or DeepSeek-V3?

Codestral-22B (Mistral AI) and DeepSeek-V3 (DeepSeek) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does Codestral-22B compare to DeepSeek-V3 in benchmarks?

Codestral-22B scores HumanEvalFIM-Average: 91.6%, HumanEval: 81.1%, MBPP: 78.2%, Spider: 63.5%, HumanEval-Average: 61.5%. DeepSeek-V3 scores DROP: 91.6%, CLUEWSC: 90.9%, MATH-500: 90.2%, MMLU-Redux: 89.1%, MMLU: 88.5%.

What are the context window sizes for Codestral-22B and DeepSeek-V3?

Codestral-22B supports an unknown number of tokens and DeepSeek-V3 supports 131K 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 DeepSeek-V3?

Key differences include licensing (MNPL-0.1 vs MIT + Model License (Commercial use allowed)). See the full comparison above for benchmark-by-benchmark results.

Who makes Codestral-22B and DeepSeek-V3?

Codestral-22B is developed by Mistral AI and DeepSeek-V3 is developed by DeepSeek.