Compare AI models
| 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 | — |
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 | — |
| 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 | $2.50 |
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 | $7.50 |
Cheapest inputLowest input rate across third-party providers, excluding the lab itself. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | $1.50Mistral | $1.475Alibaba |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $7.50Mistral | $4.425Alibaba |
| Benchmarks | ||
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% | 80.4% |
BullshitBench v2Nonsense detection — Given a confidently-worded but nonsensical prompt, does the AI spot that it makes no sense and push back — instead of playing along and inventing an answer? The score is how often it clearly called out the nonsense. Higher is better. | — | 72% |
SWE-Bench ProAgentic coding — Can the AI fix real bugs in real software? It's handed actual problems from open-source projects and has to write code that genuinely solves them. Higher is better. | — | 60.6% |
LiveCodeBenchCompetitive coding — Coding problems published so recently the AI can't have seen them in training — a contamination-free test of raw programming skill. Higher is better. | — | 91.6% |
Terminal-Bench 2.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? (Version 2.0 of the test.) Higher is better. | — | 69.7% |
MCP AtlasMulti-step tool use — Can the AI chain together many tools and steps to complete one bigger task, rather than doing just a single thing? Higher is better. | — | 76.4% |
Humanity's Last Exam · with toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. | — | 41.4% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 92.4% |
| Overview | ||
| Company | Mistral | Qwen |
| Release date | Apr 29 2026 | May 20 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Mistral Medium 3.5vsClaude Haiku 5.5Qwen3.7-MaxvsClaude Haiku 5.5Mistral Medium 3.5vsGPT-6.1 SolQwen3.7-MaxvsGPT-6.1 SolMistral Medium 3.5vsGemini 4 ArgonQwen3.7-MaxvsGemini 4 ArgonMistral Medium 3.5vsMuse Spark 1.3Qwen3.7-MaxvsMuse Spark 1.3Mistral Medium 3.5vsGrok 4.7Qwen3.7-MaxvsGrok 4.7Mistral Medium 3.5vsDeepSeek-V4.1-FlashQwen3.7-MaxvsDeepSeek-V4.1-FlashFrequently asked questions
Qwen3.7-Max leads Mistral Medium 3.5 on 1 of the 1 benchmark they both report (SWE-Bench Verified). Mistral Medium 3.5 is cheaper on input: $1.50 vs $2.50 per million tokens. Both charge $7.50 per million output tokens. Mistral Medium 3.5 shipped 21 days before Qwen3.7-Max, so benchmark comparisons should account for the intervening progress.
Mistral Medium 3.5 is open weight, while Qwen3.7-Max is closed.
On SWE-Bench Verified, Qwen3.7-Max leads at 80.4% vs Mistral Medium 3.5 at 77.6%.