Mistral Small vs Qwen3-235B-A22B-Thinking-2507
Comparing Mistral Small and Qwen3-235B-A22B-Thinking-2507 across benchmarks, pricing, and capabilities.
Mistral AI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Mistral Small and Qwen3-235B-A22B-Thinking-2507 trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.
On price, Mistral Small is roughly 3.2x 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.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose Mistral Small
- cost matters — it's about 3.2x cheaper per token
Choose Qwen3-235B-A22B-Thinking-2507
- you process long inputs — it offers a 262,144 token context window
- you want the most recent training data — it shipped Jul 2025
At a glance
The differences that matter most.
Individual benchmarks
0 reported for Mistral Small · 25 for Qwen3-235B-A22B-Thinking-2507
Mistral Small and Qwen3-235B-A22B-Thinking-2507don'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
Pricing Analysis
Price comparison per million tokens
For input processing, Mistral Small ($0.20/1M tokens) is 1.5x cheaper than Qwen3-235B-A22B-Thinking-2507 ($0.30/1M tokens).
For output processing, Mistral Small ($0.60/1M tokens) is 5.0x cheaper than Qwen3-235B-A22B-Thinking-2507 ($3.00/1M tokens).
In conclusion, Qwen3-235B-A22B-Thinking-2507 is more expensive than Mistral Small.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Qwen3-235B-A22B-Thinking-2507 has 213.0B more parameters than Mistral Small, making it 968.2% larger.
Context Window
Maximum input and output token capacity
Qwen3-235B-A22B-Thinking-2507 accepts 262,144 input tokens compared to Mistral Small's 32,768 tokens. Qwen3-235B-A22B-Thinking-2507 can generate longer responses up to 131,072 tokens, while Mistral Small is limited to 32,768 tokens.
License
Usage and distribution terms
Mistral Small is licensed under Mistral Research 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.
Mistral Research License
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
Mistral Small was released on 2024-09-17, while Qwen3-235B-A22B-Thinking-2507 was released on 2025-07-25.
Qwen3-235B-A22B-Thinking-2507 is 10 months newer than Mistral Small.
Sep 17, 2024
2.0 years ago
Jul 25, 2025
1.1 years ago
10mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
Mistral Small is available from Mistral AI. Qwen3-235B-A22B-Thinking-2507 is available from Fireworks, Novita.
Mistral Small
Qwen3-235B-A22B-Thinking-2507
Outputs Comparison
Judge for yourself.
Run your own prompts against Mistral Small and Qwen3-235B-A22B-Thinking-2507 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Mistral Small vs Qwen3-235B-A22B-Thinking-2507.