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. | 1.8B/3.25B | 123B |
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. | 32k | 128k |
| API pricingUSD per 1M tokens · lower wins | ||
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. | — | $2.00Mistral |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $6.00Mistral |
| — | $700Mistral | |
| Overview | ||
| Company | Mistral | |
| Release date | Dec 6 2023 | Jul 24 2024 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
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Gemini 1.0 Nano and Mistral Large 2 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Gemini 1.0 Nano shipped 231 days before Mistral Large 2, so benchmark comparisons should account for the intervening progress.
Gemini 1.0 Nano has 1.8B/3.25B parameters, while Mistral Large 2 has 123B. Context windows are 32k (Gemini 1.0 Nano) vs 128k (Mistral Large 2). Gemini 1.0 Nano is closed, while Mistral Large 2 is open weight.
Direct benchmark comparisons are unavailable — Gemini 1.0 Nano and Mistral Large 2 don't publish scores on any of the same benchmarks.