Model Comparison

DeepSeek-R1 vs Codestral-22B

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

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek-R1 and Codestral-22B 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
Fri Apr 03 2026 • llm-stats.com
DeepSeek
DeepSeek-R1
Input tokens$0.55
Output tokens$2.19
Best providerDeepSeek
Mistral AI
Codestral-22B
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

648.8B diff

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

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

Context Window

Maximum input and output token capacity

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

DeepSeek
DeepSeek-R1
Input131,072 tokens
Output131,072 tokens
Mistral AI
Codestral-22B
Input- tokens
Output- tokens
Fri Apr 03 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-R1 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

MIT

Open weights

Codestral-22B

MNPL-0.1

Open weights

Release Timeline

When each model was launched

DeepSeek-R1 was released on 2025-01-20, while Codestral-22B was released on 2024-05-29.

DeepSeek-R1 is 8 months newer than Codestral-22B.

DeepSeek-R1

Jan 20, 2025

1.2 years ago

7mo newer
Codestral-22B

May 29, 2024

1.8 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

Detailed Comparison

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

FAQ

Common questions about DeepSeek-R1 vs Codestral-22B

DeepSeek-R1 (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.
Codestral-22B scores HumanEvalFIM-Average: 91.6%, HumanEval: 81.1%, MBPP: 78.2%, Spider: 63.5%, HumanEval-Average: 61.5%.
DeepSeek-R1 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.
Key differences include licensing (MIT vs MNPL-0.1). See the full comparison above for benchmark-by-benchmark results.
DeepSeek-R1 is developed by DeepSeek and Codestral-22B is developed by Mistral AI.