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Codestral-22B vs DeepSeek-R1-0528

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

Mistral AI · DeepSeek · Updated for 2026

Which is better?

Codestral-22B and DeepSeek-R1-0528 trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.

Based on current benchmark, pricing, and model metadata for 2026.

Choose Codestral-22B

  • you are already invested in the Mistral AI ecosystem

Choose DeepSeek-R1-0528

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

At a glance

The differences that matter most.

Codestral-22B
Mistral AI
DeepSeek-R1-0528
DeepSeek
Benchmark wins
—
—
Input price
— / M
$0.50 / M
Output price
— / M
$2.15 / M
Context window
—
131,072
Released
May 2024
May 2025
License
MNPL-0.1
MIT

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

Codestral-22B and DeepSeek-R1-0528don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Playground indexes and blind preference scores

Model Size

Parameter count comparison

648.8B diff

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

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

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).

Mistral AI
Codestral-22B
Input- tokens
Output- tokens
DeepSeek
DeepSeek-R1-0528
Input131,072 tokens
Output131,072 tokens
Wed Aug 26 2026 • llm-stats.com

License

Usage and distribution terms

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

MIT

Open weights

Release Timeline

When each model was launched

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

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

Codestral-22B

May 29, 2024

2.2 years ago

DeepSeek-R1-0528

May 28, 2025

1.2 years ago

12mo 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→

Judge for yourself.

Run your own prompts against Codestral-22B and DeepSeek-R1-0528 side-by-side, then vote on the output you prefer.

Codestral-22B
✓ Preferred
DeepSeek-R1-0528
Open in Playground

FAQ

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

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

Codestral-22B (Mistral AI) and DeepSeek-R1-0528 (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-R1-0528 in benchmarks?

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

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

Codestral-22B supports an unknown number of tokens and DeepSeek-R1-0528 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-R1-0528?

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

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

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

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