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. | 24B | — |
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. | 128k | 200k |
| 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. | $0.075DeepInfra | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.20DeepInfra | — |
| $25.00DeepInfra · fp8 | — | |
| Overview | ||
| Company | Mistral | OpenAI |
| Release date | Jun 20 2025 | May 16 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
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Mistral Small 3.2 and codex-mini-latest don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. codex-mini-latest shipped 35 days before Mistral Small 3.2, so benchmark comparisons should account for the intervening progress.
Context windows are 128k (Mistral Small 3.2) vs 200k (codex-mini-latest). Mistral Small 3.2 is open weight, while codex-mini-latest is closed.
Direct benchmark comparisons are unavailable — Mistral Small 3.2 and codex-mini-latest don't publish scores on any of the same benchmarks.