Model Comparison

Codestral-22B vs Qwen3 VL 4B Instruct

Comparing Codestral-22B and Qwen3 VL 4B Instruct across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

Codestral-22B and Qwen3 VL 4B Instruct don'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

18.2B diff

Codestral-22B has 18.2B more parameters than Qwen3 VL 4B Instruct, making it 455.0% larger.

Codestral-22B

22.2Bparameters

Qwen3 VL 4B Instruct

4.0Bparameters

22.2B

Codestral-22B

4.0B

Qwen3 VL 4B Instruct

Context Window

Maximum input and output token capacity

Only Qwen3 VL 4B Instruct specifies input context (262,144 tokens). Only Qwen3 VL 4B Instruct specifies output context (262,144 tokens).

Codestral-22B

Input- tokens

Output- tokens

Qwen3 VL 4B Instruct

Input262,144 tokens

Output262,144 tokens

Mon May 25 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Qwen3 VL 4B Instruct supports multimodal inputs, whereas Codestral-22B does not.

Qwen3 VL 4B Instruct can handle both text and other forms of data like images, making it suitable for multimodal applications.

Codestral-22B

Text

Images

Audio

Video

Qwen3 VL 4B Instruct

Text

Images

Audio

Video

License

Usage and distribution terms

Codestral-22B is licensed under MNPL-0.1, while Qwen3 VL 4B Instruct 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 VL 4B Instruct

Apache 2.0

Open weights

Release Timeline

When each model was launched

Codestral-22B was released on 2024-05-29, while Qwen3 VL 4B Instruct was released on 2025-09-22.

Qwen3 VL 4B Instruct is 16 months newer than Codestral-22B.

Codestral-22B

May 29, 2024

2.0 years ago

Qwen3 VL 4B Instruct

Sep 22, 2025

8 months ago

1.3yr 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

Codestral-22B

View details

Mistral AI

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

Qwen3 VL 4B Instruct

View details

Alibaba Cloud / Qwen Team

Larger context window (262,144 tokens)

Supports multimodal inputs

Detailed Comparison

AI Model Comparison Table
Feature	Codestral-22B	Qwen3 VL 4B Instruct

FAQ

Common questions about Codestral-22B vs Qwen3 VL 4B Instruct.

Which is better, Codestral-22B or Qwen3 VL 4B Instruct?

Codestral-22B (Mistral AI) and Qwen3 VL 4B Instruct (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.

How does Codestral-22B compare to Qwen3 VL 4B Instruct in benchmarks?

Codestral-22B scores HumanEvalFIM-Average: 91.6%, HumanEval: 81.1%, MBPP: 78.2%, Spider: 63.5%, HumanEval-Average: 61.5%. Qwen3 VL 4B Instruct scores DocVQAtest: 95.3%, ScreenSpot: 94.0%, OCRBench: 88.1%, MMBench-V1.1: 85.1%, AI2D: 84.1%.

What are the context window sizes for Codestral-22B and Qwen3 VL 4B Instruct?

Codestral-22B supports an unknown number of tokens and Qwen3 VL 4B Instruct supports 262K 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 Qwen3 VL 4B Instruct?

Key differences include multimodal support (no vs yes), licensing (MNPL-0.1 vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes Codestral-22B and Qwen3 VL 4B Instruct?

Codestral-22B is developed by Mistral AI and Qwen3 VL 4B Instruct is developed by Alibaba Cloud / Qwen Team.