Compare AI models
| Specifications | ||
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 | 400k |
| 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 | — | |
| 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. | — | 39% |
| Overview | ||
| Company | Mistral | OpenAI |
| Release date | Feb 26 2024 | Sep 15 2025 |
| Access | Closed | Closed |
| Model details | View model | View model |
Other comparisons
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Mistral Large and GPT-5-Codex don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Mistral Large shipped 567 days before GPT-5-Codex, so benchmark comparisons should account for the intervening progress.
Context windows are 32k (Mistral Large) vs 400k (GPT-5-Codex).
Direct benchmark comparisons are unavailable — Mistral Large and GPT-5-Codex don't publish scores on any of the same benchmarks.