Model Comparison

Codestral-22B vs Qwen3-235B-A22B-Instruct-2507

Comparing Codestral-22B and Qwen3-235B-A22B-Instruct-2507 across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

Codestral-22B and Qwen3-235B-A22B-Instruct-2507 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 May 01 2026 • llm-stats.com
Mistral AI
Codestral-22B
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Alibaba Cloud / Qwen Team
Qwen3-235B-A22B-Instruct-2507
Input tokens$0.15
Output tokens$0.80
Best providerFireworks
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

212.8B diff

Qwen3-235B-A22B-Instruct-2507 has 212.8B more parameters than Codestral-22B, making it 958.6% larger.

Mistral AI
Codestral-22B
22.2Bparameters
Alibaba Cloud / Qwen Team
Qwen3-235B-A22B-Instruct-2507
235.0Bparameters
22.2B
Codestral-22B
235.0B
Qwen3-235B-A22B-Instruct-2507

Context Window

Maximum input and output token capacity

Only Qwen3-235B-A22B-Instruct-2507 specifies input context (262,144 tokens). Only Qwen3-235B-A22B-Instruct-2507 specifies output context (131,072 tokens).

Mistral AI
Codestral-22B
Input- tokens
Output- tokens
Alibaba Cloud / Qwen Team
Qwen3-235B-A22B-Instruct-2507
Input262,144 tokens
Output131,072 tokens
Fri May 01 2026 • llm-stats.com

License

Usage and distribution terms

Codestral-22B is licensed under MNPL-0.1, while Qwen3-235B-A22B-Instruct-2507 uses Apache 2.0.

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

Codestral-22B

MNPL-0.1

Open weights

Qwen3-235B-A22B-Instruct-2507

Apache 2.0

Open weights

Release Timeline

When each model was launched

Codestral-22B was released on 2024-05-29, while Qwen3-235B-A22B-Instruct-2507 was released on 2025-07-22.

Qwen3-235B-A22B-Instruct-2507 is 14 months newer than Codestral-22B.

Codestral-22B

May 29, 2024

1.9 years ago

Qwen3-235B-A22B-Instruct-2507

Jul 22, 2025

9 months ago

1.1yr 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
Alibaba Cloud / Qwen Team
Larger context window (262,144 tokens)

Detailed Comparison

AI Model Comparison Table
Feature
Mistral AI
Codestral-22B
Alibaba Cloud / Qwen Team
Qwen3-235B-A22B-Instruct-2507

FAQ

Common questions about Codestral-22B vs Qwen3-235B-A22B-Instruct-2507

Codestral-22B (Mistral AI) and Qwen3-235B-A22B-Instruct-2507 (Alibaba Cloud / Qwen Team) 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%. Qwen3-235B-A22B-Instruct-2507 scores ZebraLogic: 95.0%, MMLU-Redux: 93.1%, IFEval: 88.7%, MultiPL-E: 87.9%, Creative Writing v3: 87.5%.
Codestral-22B supports an unknown number of tokens and Qwen3-235B-A22B-Instruct-2507 supports 262K 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 Apache 2.0). See the full comparison above for benchmark-by-benchmark results.
Codestral-22B is developed by Mistral AI and Qwen3-235B-A22B-Instruct-2507 is developed by Alibaba Cloud / Qwen Team.