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 | 200k |
| 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. | — | 75.7% |
| Overview | ||
| Company | Mistral | OpenAI |
| Release date | Apr 29 2026 | Dec 5 2024 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Mistral Medium 3.5vsClaude Haiku 5.5o1vsClaude Haiku 5.5Mistral Medium 3.5vsGemini 4 Carbono1vsGemini 4 CarbonMistral Medium 3.5vsMuse Spark Openo1vsMuse Spark OpenMistral Medium 3.5vsGrok 4.7o1vsGrok 4.7Mistral Medium 3.5vsDeepSeek-V4.1-Flash-1017o1vsDeepSeek-V4.1-Flash-1017Mistral Medium 3.5vsKimi K3.1 Proo1vsKimi K3.1 ProFrequently asked questions
Mistral Medium 3.5 and o1 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 shipped 510 days before Mistral Medium 3.5, so benchmark comparisons should account for the intervening progress.
Context windows are 256k (Mistral Medium 3.5) vs 200k (o1). Mistral Medium 3.5 is open weight, while o1 is closed.
Direct benchmark comparisons are unavailable — Mistral Medium 3.5 and o1 don't publish scores on any of the same benchmarks.