Model Comparison

Codestral-22B vs DeepSeek-V3.1

Comparing Codestral-22B and DeepSeek-V3.1 across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

Codestral-22B and DeepSeek-V3.1 don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Wed Apr 22 2026 • llm-stats.com
Mistral AI
Codestral-22B
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
DeepSeek
DeepSeek-V3.1
Input tokens$0.27
Output tokens$1.00
Best providerDeepinfra
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

648.8B diff

DeepSeek-V3.1 has 648.8B more parameters than Codestral-22B, making it 2922.5% larger.

Mistral AI
Codestral-22B
22.2Bparameters
DeepSeek
DeepSeek-V3.1
671.0Bparameters
22.2B
Codestral-22B
671.0B
DeepSeek-V3.1

Context Window

Maximum input and output token capacity

Only DeepSeek-V3.1 specifies input context (163,840 tokens). Only DeepSeek-V3.1 specifies output context (163,840 tokens).

Mistral AI
Codestral-22B
Input- tokens
Output- tokens
DeepSeek
DeepSeek-V3.1
Input163,840 tokens
Output163,840 tokens
Wed Apr 22 2026 • llm-stats.com

License

Usage and distribution terms

Codestral-22B is licensed under MNPL-0.1, while DeepSeek-V3.1 uses MIT.

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

Codestral-22B

MNPL-0.1

Open weights

DeepSeek-V3.1

MIT

Open weights

Release Timeline

When each model was launched

Codestral-22B was released on 2024-05-29, while DeepSeek-V3.1 was released on 2025-01-10.

DeepSeek-V3.1 is 8 months newer than Codestral-22B.

Codestral-22B

May 29, 2024

1.9 years ago

DeepSeek-V3.1

Jan 10, 2025

1.3 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

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Mistral AI
DeepSeek
Larger context window (163,840 tokens)

Detailed Comparison

AI Model Comparison Table
Feature
Mistral AI
Codestral-22B
DeepSeek
DeepSeek-V3.1

FAQ

Common questions about Codestral-22B vs DeepSeek-V3.1

Codestral-22B (Mistral AI) and DeepSeek-V3.1 (DeepSeek) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.
Codestral-22B scores HumanEvalFIM-Average: 91.6%, HumanEval: 81.1%, MBPP: 78.2%, Spider: 63.5%, HumanEval-Average: 61.5%. DeepSeek-V3.1 scores SimpleQA: 93.4%, MMLU-Redux: 91.8%, MMLU-Pro: 83.7%, GPQA: 74.9%, CodeForces: 69.7%.
Codestral-22B supports an unknown number of tokens and DeepSeek-V3.1 supports 164K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include licensing (MNPL-0.1 vs MIT). See the full comparison above for benchmark-by-benchmark results.
Codestral-22B is developed by Mistral AI and DeepSeek-V3.1 is developed by DeepSeek.