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 · base tier | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | $1.50 | $0.50 |
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 | $3.00 |
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 | $0.325Alibaba |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $7.50Mistral | $1.95Alibaba |
| 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% | 78.8% |
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% |
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. | — | 28.8% |
| Overview | ||
| Company | Mistral | Qwen |
| Release date | Apr 29 2026 | Apr 2 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
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Qwen3.6-Plus leads Mistral Medium 3.5 on 1 of the 1 benchmark they both report (SWE-Bench Verified). Qwen3.6-Plus is cheaper on both input and output: $0.50 vs $1.50 per million input tokens, and $3.00 vs $7.50 per million output tokens. Figures are base-tier rates. Qwen3.6-Plus shipped 27 days before Mistral Medium 3.5, so benchmark comparisons should account for the intervening progress.
Mistral Medium 3.5 is open weight, while Qwen3.6-Plus is closed.
On SWE-Bench Verified, Qwen3.6-Plus leads at 78.8% vs Mistral Medium 3.5 at 77.6%.