Model Comparison

Codestral-22B vs DeepSeek R1 Distill Qwen 32B

Comparing Codestral-22B and DeepSeek R1 Distill Qwen 32B across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

Codestral-22B and DeepSeek R1 Distill Qwen 32B 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 08 2026 • llm-stats.com
Mistral AI
Codestral-22B
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
DeepSeek
DeepSeek R1 Distill Qwen 32B
Input tokens$0.12
Output tokens$0.18
Best providerDeepinfra
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

10.6B diff

DeepSeek R1 Distill Qwen 32B has 10.6B more parameters than Codestral-22B, making it 47.7% larger.

Mistral AI
Codestral-22B
22.2Bparameters
DeepSeek
DeepSeek R1 Distill Qwen 32B
32.8Bparameters
22.2B
Codestral-22B
32.8B
DeepSeek R1 Distill Qwen 32B

Context Window

Maximum input and output token capacity

Only DeepSeek R1 Distill Qwen 32B specifies input context (128,000 tokens). Only DeepSeek R1 Distill Qwen 32B specifies output context (128,000 tokens).

Mistral AI
Codestral-22B
Input- tokens
Output- tokens
DeepSeek
DeepSeek R1 Distill Qwen 32B
Input128,000 tokens
Output128,000 tokens
Wed Apr 08 2026 • llm-stats.com

License

Usage and distribution terms

Codestral-22B is licensed under MNPL-0.1, while DeepSeek R1 Distill Qwen 32B 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 Distill Qwen 32B

MIT

Open weights

Release Timeline

When each model was launched

Codestral-22B was released on 2024-05-29, while DeepSeek R1 Distill Qwen 32B was released on 2025-01-20.

DeepSeek R1 Distill Qwen 32B is 8 months newer than Codestral-22B.

Codestral-22B

May 29, 2024

1.9 years ago

DeepSeek R1 Distill Qwen 32B

Jan 20, 2025

1.2 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
DeepSeek
Larger context window (128,000 tokens)

Detailed Comparison

AI Model Comparison Table
Feature
Mistral AI
Codestral-22B
DeepSeek
DeepSeek R1 Distill Qwen 32B

FAQ

Common questions about Codestral-22B vs DeepSeek R1 Distill Qwen 32B

Codestral-22B (Mistral AI) and DeepSeek R1 Distill Qwen 32B (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 R1 Distill Qwen 32B scores MATH-500: 94.3%, AIME 2024: 83.3%, GPQA: 62.1%, LiveCodeBench: 57.2%.
Codestral-22B supports an unknown number of tokens and DeepSeek R1 Distill Qwen 32B supports 128K 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 R1 Distill Qwen 32B is developed by DeepSeek.