Model Comparison

DeepSeek-R1-0528 vs Codestral-22BWhich is better in 2026?

Comparing DeepSeek-R1-0528 and Codestral-22B across benchmarks, pricing, and capabilities.

Verdict: DeepSeek-R1-0528 vs Codestral-22B — which is better?

DeepSeek-R1-0528 (by DeepSeek) and Codestral-22B (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.

Choose DeepSeek-R1-0528 if…

  • you want the most recent training data — it shipped May 2025

Choose Codestral-22B if…

  • you are already invested in the Mistral AI ecosystem
Want to compare interactively?Try the playground

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek-R1-0528 and Codestral-22Bdon'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-R1-0528 has 648.8B more parameters than Codestral-22B, making it 2922.5% larger.

DeepSeek
DeepSeek-R1-0528
671.0Bparameters
Mistral AI
Codestral-22B
22.2Bparameters
671.0B
DeepSeek-R1-0528
22.2B
Codestral-22B

Context Window

Maximum input and output token capacity

Only DeepSeek-R1-0528 specifies input context (131,072 tokens). Only DeepSeek-R1-0528 specifies output context (131,072 tokens).

DeepSeek
DeepSeek-R1-0528
Input131,072 tokens
Output131,072 tokens
Mistral AI
Codestral-22B
Input- tokens
Output- tokens
Thu Jun 11 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-R1-0528 is licensed under MIT, while Codestral-22B uses MNPL-0.1.

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

DeepSeek-R1-0528

MIT

Open weights

Codestral-22B

MNPL-0.1

Open weights

Release Timeline

When each model was launched

DeepSeek-R1-0528 was released on 2025-05-28, while Codestral-22B was released on 2024-05-29.

DeepSeek-R1-0528 is 12 months newer than Codestral-22B.

DeepSeek-R1-0528

May 28, 2025

1.0 years ago

12mo newer
Codestral-22B

May 29, 2024

2.0 years ago

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

DeepSeek
Larger context window (131,072 tokens)
Mistral AI

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

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-R1-0528
Mistral AI
Codestral-22B

FAQ

Common questions about DeepSeek-R1-0528 vs Codestral-22B.

Which is better, DeepSeek-R1-0528 or Codestral-22B?

DeepSeek-R1-0528 (DeepSeek) and Codestral-22B (Mistral AI) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does DeepSeek-R1-0528 compare to Codestral-22B in benchmarks?

DeepSeek-R1-0528 scores MMLU-Redux: 93.4%, SimpleQA: 92.3%, AIME 2024: 91.4%, AIME 2025: 87.5%, MMLU-Pro: 85.0%. Codestral-22B scores HumanEvalFIM-Average: 91.6%, HumanEval: 81.1%, MBPP: 78.2%, Spider: 63.5%, HumanEval-Average: 61.5%.

What are the context window sizes for DeepSeek-R1-0528 and Codestral-22B?

DeepSeek-R1-0528 supports 131K tokens and Codestral-22B supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between DeepSeek-R1-0528 and Codestral-22B?

Key differences include licensing (MIT vs MNPL-0.1). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-R1-0528 and Codestral-22B?

DeepSeek-R1-0528 is developed by DeepSeek and Codestral-22B is developed by Mistral AI.

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