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. | 119B | — |
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. | $0.15 | $1.10 |
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. | $0.60 | $4.40 |
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. | — | $0.55 |
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. | $0.15Mistral | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.60Mistral | — |
| $60.00Mistral list price | $440OpenAI list price | |
| These models have no shared benchmark scores. | ||
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. | 6% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 60% |
| Overview | ||
| Company | Mistral | OpenAI |
| Release date | Mar 16 2026 | Sep 12 2024 |
| Access | Closed | Closed |
| Model details | View model | View model |
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Mistral Small 4 and o1-mini don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Mistral Small 4 is cheaper on both input and output: $0.15 vs $1.10 per million input tokens, and $0.60 vs $4.40 per million output tokens. o1-mini shipped 550 days before Mistral Small 4, so benchmark comparisons should account for the intervening progress.
Context windows are 256k (Mistral Small 4) vs 128k (o1-mini).
Direct benchmark comparisons are unavailable — Mistral Small 4 and o1-mini don't publish scores on any of the same benchmarks.