Model Comparison

Devstral Medium vs Qwen3-235B-A22B-Thinking-2507Which is better in 2026?

Comparing Devstral Medium and Qwen3-235B-A22B-Thinking-2507 across benchmarks, pricing, and capabilities.

Verdict: Devstral Medium vs Qwen3-235B-A22B-Thinking-2507 — which is better?

Devstral Medium (by Mistral AI) and Qwen3-235B-A22B-Thinking-2507 (by Alibaba Cloud / Qwen Team) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.

On price, Devstral Medium is roughly 1.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

Qwen3-235B-A22B-Thinking-2507 also accepts a larger context window (262,144 input tokens), making it the stronger choice for long documents and large codebases.

Choose Devstral Medium if…

  • cost matters — it's about 1.2x cheaper per token

Choose Qwen3-235B-A22B-Thinking-2507 if…

  • you process long inputs — it offers a 262,144 token context window
  • you want the most recent training data — it shipped Jul 2025
  • you need open weights you can self-host or fine-tune
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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

Devstral Medium and Qwen3-235B-A22B-Thinking-2507don'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

Devstral Medium costs less

For input processing, Devstral Medium ($0.40/1M tokens) is 1.3x more expensive than Qwen3-235B-A22B-Thinking-2507 ($0.30/1M tokens).

For output processing, Devstral Medium ($2.00/1M tokens) is 1.5x cheaper than Qwen3-235B-A22B-Thinking-2507 ($3.00/1M tokens).

In conclusion, Qwen3-235B-A22B-Thinking-2507 is more expensive than Devstral Medium.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Thu Jun 11 2026 • llm-stats.com
Mistral AI
Devstral Medium
Input tokens$0.40
Output tokens$2.00
Best providerMistral
Alibaba Cloud / Qwen Team
Qwen3-235B-A22B-Thinking-2507
Input tokens$0.30
Output tokens$3.00
Best providerFireworks
Notice missing or incorrect data?Start an Issue→

Context Window

Maximum input and output token capacity

Qwen3-235B-A22B-Thinking-2507 accepts 262,144 input tokens compared to Devstral Medium's 128,000 tokens. Qwen3-235B-A22B-Thinking-2507 can generate longer responses up to 131,072 tokens, while Devstral Medium is limited to 128,000 tokens.

Mistral AI
Devstral Medium
Input128,000 tokens
Output128,000 tokens
Alibaba Cloud / Qwen Team
Qwen3-235B-A22B-Thinking-2507
Input262,144 tokens
Output131,072 tokens
Thu Jun 11 2026 • llm-stats.com

License

Usage and distribution terms

Devstral Medium is licensed under a proprietary license, while Qwen3-235B-A22B-Thinking-2507 uses Apache 2.0.

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

Devstral Medium

Proprietary

Closed source

Qwen3-235B-A22B-Thinking-2507

Apache 2.0

Open weights

Release Timeline

When each model was launched

Devstral Medium was released on 2025-07-10, while Qwen3-235B-A22B-Thinking-2507 was released on 2025-07-25.

Qwen3-235B-A22B-Thinking-2507 is 1 month newer than Devstral Medium.

Devstral Medium

Jul 10, 2025

11 months ago

Qwen3-235B-A22B-Thinking-2507

Jul 25, 2025

10 months ago

2w 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

Provider Availability

Devstral Medium is available from Mistral AI. Qwen3-235B-A22B-Thinking-2507 is available from Fireworks, Novita.

Devstral Medium

mistral logo
Mistral
Input Price:Input: $0.40/1MOutput Price:Output: $2.00/1M

Qwen3-235B-A22B-Thinking-2507

fireworks logo
Fireworks
Input Price:Input: $0.30/1MOutput Price:Output: $3.00/1M
novita logo
Novita
Input Price:Input: $0.30/1MOutput Price:Output: $3.00/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Mistral AI
Less expensive output tokens
Alibaba Cloud / Qwen Team
Larger context window (262,144 tokens)
Less expensive input tokens
Has open weights

Detailed Comparison

AI Model Comparison Table
Feature
Mistral AI
Devstral Medium
Alibaba Cloud / Qwen Team
Qwen3-235B-A22B-Thinking-2507

FAQ

Common questions about Devstral Medium vs Qwen3-235B-A22B-Thinking-2507.

Which is better, Devstral Medium or Qwen3-235B-A22B-Thinking-2507?

Devstral Medium (Mistral AI) and Qwen3-235B-A22B-Thinking-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.

How does Devstral Medium compare to Qwen3-235B-A22B-Thinking-2507 in benchmarks?

Devstral Medium scores SWE-Bench Verified: 61.6%. Qwen3-235B-A22B-Thinking-2507 scores MMLU-Redux: 93.8%, AIME 2025: 92.3%, WritingBench: 88.3%, IFEval: 87.8%, Creative Writing v3: 86.1%.

Is Devstral Medium cheaper than Qwen3-235B-A22B-Thinking-2507?

Qwen3-235B-A22B-Thinking-2507 is 1.3x cheaper for input tokens. Devstral Medium costs $0.40/M input and $2.00/M output via mistral. Qwen3-235B-A22B-Thinking-2507 costs $0.30/M input and $3.00/M output via fireworks.

What are the context window sizes for Devstral Medium and Qwen3-235B-A22B-Thinking-2507?

Devstral Medium supports 128K tokens and Qwen3-235B-A22B-Thinking-2507 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 Devstral Medium and Qwen3-235B-A22B-Thinking-2507?

Key differences include context window (128K vs 262K), input pricing ($0.40 vs $0.30/M), licensing (Proprietary vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes Devstral Medium and Qwen3-235B-A22B-Thinking-2507?

Devstral Medium is developed by Mistral AI and Qwen3-235B-A22B-Thinking-2507 is developed by Alibaba Cloud / Qwen Team.

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