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Codestral-22B vs Qwen3.8 Flash

Qwen3.8 Flash leads the LLM Stats Score 49.6 to 0.2.

Mistral AI · Alibaba Cloud / Qwen Team · Updated for 2026

Which is better?

Qwen3.8 Flash leads the overall LLM Stats Score 49.6 to 0.2, ranking #16 overall.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose Codestral-22B

  • you need open weights you can self-host or fine-tune

Choose Qwen3.8 Flash

  • overall performance matters — it scores 49.6 and ranks #16 on LLM Stats
  • your work emphasizes reasoning and coding — it leads those capability indexes
  • you want the most recent training data — it shipped Aug 2026

At a glance

The differences that matter most.

Codestral-22B
Mistral AI
Qwen3.8 Flash
Alibaba Cloud / Qwen Team
Core performance indexes
LLM Stats Score
0.2
#309
49.6
#16
Reasoning
0.1
#303
49.2
#16
Coding
2.5
#213
36.6
#20
Cost, coverage & limits
Benchmark wins
—
—
Input price
— / M
$0.15 / M
Output price
— / M
$0.47 / M
Context window
—
1,000,000

Individual benchmarks

7 reported for Codestral-22B · 22 for Qwen3.8 Flash

No common benchmarks found

Codestral-22B and Qwen3.8 Flashdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

102.8B diff

Qwen3.8 Flash has 102.8B more parameters than Codestral-22B, making it 463.1% larger.

Mistral AI
Codestral-22B
22.2Bparameters
Alibaba Cloud / Qwen Team
Qwen3.8 Flash
125.0Bparameters
22.2B
Codestral-22B
125.0B
Qwen3.8 Flash

Context Window

Maximum input and output token capacity

Only Qwen3.8 Flash specifies input context (1,000,000 tokens). Only Qwen3.8 Flash specifies output context (131,072 tokens).

Mistral AI
Codestral-22B
Input- tokens
Output- tokens
Alibaba Cloud / Qwen Team
Qwen3.8 Flash
Input1,000,000 tokens
Output131,072 tokens
Mon Aug 31 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Qwen3.8 Flash supports multimodal inputs, whereas Codestral-22B does not.

Qwen3.8 Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.

Codestral-22B

Text
Images
Audio
Video

Qwen3.8 Flash

Text
Images
Audio
Video

License

Usage and distribution terms

Codestral-22B is licensed under MNPL-0.1, while Qwen3.8 Flash uses a proprietary license.

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

Codestral-22B

MNPL-0.1

Open weights

Qwen3.8 Flash

Proprietary

Closed source

Release Timeline

When each model was launched

Codestral-22B was released on 2024-05-29, while Qwen3.8 Flash was released on 2026-08-26.

Qwen3.8 Flash is 27 months newer than Codestral-22B.

Codestral-22B

May 29, 2024

2.3 years ago

Qwen3.8 Flash

Aug 26, 2026

4 days ago

2.2yr 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 Qwen3.8 Flash side-by-side, then vote on the output you prefer.

Codestral-22B
✓ Preferred
Qwen3.8 Flash
Open in Playground

FAQ

Common questions about Codestral-22B vs Qwen3.8 Flash.

Which is better, Codestral-22B or Qwen3.8 Flash?

Qwen3.8 Flash leads the LLM Stats Score 49.6 to 0.2. Codestral-22B is made by Mistral AI and Qwen3.8 Flash is made by Alibaba Cloud / Qwen Team. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does Codestral-22B compare to Qwen3.8 Flash in benchmarks?

Codestral-22B scores HumanEvalFIM-Average: 91.6%, HumanEval: 81.1%, MBPP: 78.2%, Spider: 63.5%, HumanEval-Average: 61.5%. Qwen3.8 Flash scores MathVision: 95.7%, LiveCodeBench v6: 91.9%, GPQA: 91.7%, CharXiv-R: 90.6%, RealWorldQA: 88.5%.

What are the context window sizes for Codestral-22B and Qwen3.8 Flash?

Codestral-22B supports an unknown number of tokens and Qwen3.8 Flash supports 1.0M 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 Qwen3.8 Flash?

Key differences include LLM Stats Score (0.2 vs 49.6), multimodal support (no vs yes), licensing (MNPL-0.1 vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes Codestral-22B and Qwen3.8 Flash?

Codestral-22B is developed by Mistral AI and Qwen3.8 Flash is developed by Alibaba Cloud / Qwen Team.

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