Mistral Medium 3.5vsQwen2
Mistral Medium 3.5 | Qwen2 | |
|---|---|---|
| Specifications | ||
ParametersA rough measure of how big the model is. More parameters usually means more capable and more expensive to run, though it is a poor guide on its own — a smaller, newer model often beats a larger, older one. | 128B | 72B |
Context windowHow much text the model can hold in mind at once — your question, any documents you attach, the conversation so far, and its own reply. Go past it and the earliest part falls out of view. | 256k | 128k |
| API pricingUSD per 1M tokens · lower wins | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | $1.50 | — |
Output priceWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one. | $7.50 | — |
| BenchmarksPublished by one model only | ||
SWE-Bench VerifiedCoding — Real coding tasks pulled from open-source projects — the AI has to find and fix actual bugs. A human-checked version of the original SWE-Bench. Higher is better. | 77.6% | — |
| Overview | ||
| Company | Mistral | Qwen |
| Release date | Apr 29 2026 | Jun 6 2024 |
| Access | Open Weight | Open Weight |
Other comparisons
Mistral Medium 3.5vsClaude Fable 5.1Qwen2vsClaude Fable 5.1Mistral Medium 3.5vsGPT-6 AstraQwen2vsGPT-6 AstraMistral Medium 3.5vsGemini 3.8 FlashQwen2vsGemini 3.8 FlashMistral Medium 3.5vsMuse Spark 1.3Qwen2vsMuse Spark 1.3Mistral Medium 3.5vsGrok 4.6Qwen2vsGrok 4.6Mistral Medium 3.5vsDeepSeek-V4.1-FlashQwen2vsDeepSeek-V4.1-Flash
Frequently asked questions
Mistral Medium 3.5 and Qwen2 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Mistral Medium 3.5 has a verified first-party API price: $1.50 per million input tokens and $7.50 per million output tokens. No pay-as-you-go API rate is tracked for Qwen2. Qwen2 shipped 692 days before Mistral Medium 3.5, so benchmark comparisons should account for the intervening progress.
Mistral Medium 3.5 has 128B parameters, while Qwen2 has 72B. Context windows are 256k (Mistral Medium 3.5) vs 128k (Qwen2).
Direct benchmark comparisons are unavailable — Mistral Medium 3.5 and Qwen2 don't publish scores on any of the same benchmarks.