Model Comparison
Devstral Small 1.1 vs Qwen3-235B-A22B-Thinking-2507Which is better in 2026?
Comparing Devstral Small 1.1 and Qwen3-235B-A22B-Thinking-2507 across benchmarks, pricing, and capabilities.
Verdict: Devstral Small 1.1 vs Qwen3-235B-A22B-Thinking-2507 — which is better?
Devstral Small 1.1 (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 Small 1.1 is roughly 6.5x 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 Small 1.1 if…
- cost matters — it's about 6.5x 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
Performance Benchmarks
Comparative analysis across standard metrics
Devstral Small 1.1 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
For input processing, Devstral Small 1.1 ($0.10/1M tokens) is 3.0x cheaper than Qwen3-235B-A22B-Thinking-2507 ($0.30/1M tokens).
For output processing, Devstral Small 1.1 ($0.30/1M tokens) is 10.0x cheaper than Qwen3-235B-A22B-Thinking-2507 ($3.00/1M tokens).
In conclusion, Qwen3-235B-A22B-Thinking-2507 is more expensive than Devstral Small 1.1.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Qwen3-235B-A22B-Thinking-2507 has 211.0B more parameters than Devstral Small 1.1, making it 879.2% larger.
Context Window
Maximum input and output token capacity
Qwen3-235B-A22B-Thinking-2507 accepts 262,144 input tokens compared to Devstral Small 1.1's 128,000 tokens. Qwen3-235B-A22B-Thinking-2507 can generate longer responses up to 131,072 tokens, while Devstral Small 1.1 is limited to 128,000 tokens.
License
Usage and distribution terms
Both models are licensed under Apache 2.0.
Both models share the same licensing terms, providing consistent usage rights.
Apache 2.0
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
Devstral Small 1.1 was released on 2025-07-11, while Qwen3-235B-A22B-Thinking-2507 was released on 2025-07-25.
Qwen3-235B-A22B-Thinking-2507 is 0 month newer than Devstral Small 1.1.
Jul 11, 2025
11 months ago
Jul 25, 2025
11 months ago
2w newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
Devstral Small 1.1 is available from Mistral AI. Qwen3-235B-A22B-Thinking-2507 is available from Fireworks, Novita.
Devstral Small 1.1
Qwen3-235B-A22B-Thinking-2507
Outputs Comparison
Key Takeaways
Devstral Small 1.1
View detailsMistral AI
Qwen3-235B-A22B-Thinking-2507
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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FAQ
Common questions about Devstral Small 1.1 vs Qwen3-235B-A22B-Thinking-2507.