Compare AI models
| Specifications | ||
ParametersThe learned values that shape how a model responds. B means billion and T means trillion. For mixture-of-experts models, the total count is larger than the number active for each token. Parameter count alone does not measure quality. | 128B | — |
Context windowHow much information the model can work with in one request, measured in tokens — pieces of words or other data. This includes your question, attachments, conversation and reply. Limits can vary by model variant and provider. | 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 | $15.00 |
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 | $60.00 |
Cached input priceA reduced rate for text you send over and over. If every request starts with the same instructions or the same document, the provider keeps a copy ready and charges less to read it again. | $0.15 | $7.50 |
Cheapest inputLowest input rate across every provider on OpenRouter, the lab included. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | $1.50Mistral | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $7.50Mistral | — |
| $675Mistral list price | $6,000OpenAI list price | |
| These models have no shared benchmark scores. | ||
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% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 73.3% |
| Overview | ||
| Company | Mistral | OpenAI |
| Release date | Apr 29 2026 | Sep 12 2024 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Mistral Medium 3.5vsClaude Haiku 5.5o1-previewvsClaude Haiku 5.5Mistral Medium 3.5vsGemini 4 Carbono1-previewvsGemini 4 CarbonMistral Medium 3.5vsMuse Spark Openo1-previewvsMuse Spark OpenMistral Medium 3.5vsGrok 4.7o1-previewvsGrok 4.7Mistral Medium 3.5vsDeepSeek-V4.1-Flash-1017o1-previewvsDeepSeek-V4.1-Flash-1017Mistral Medium 3.5vsKimi K3.1 Proo1-previewvsKimi K3.1 ProFrequently asked questions
Mistral Medium 3.5 and o1-preview don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Mistral Medium 3.5 is cheaper on both input and output: $1.50 vs $15.00 per million input tokens, and $7.50 vs $60.00 per million output tokens. o1-preview shipped 594 days before Mistral Medium 3.5, so benchmark comparisons should account for the intervening progress.
Context windows are 256k (Mistral Medium 3.5) vs 128k (o1-preview). Mistral Medium 3.5 is open weight, while o1-preview is closed.
Direct benchmark comparisons are unavailable — Mistral Medium 3.5 and o1-preview don't publish scores on any of the same benchmarks.