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. | 256k | 400k |
| 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. | — | $0.05 |
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. | — | $0.40 |
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. | — | $0.005 |
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. | — | $0.025OpenAI |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $0.20OpenAI |
| — | $30.00OpenAI 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. | 68% | — |
SWE-Bench MultilingualMultilingual coding — Like SWE-Bench, but the coding problems span many programming languages, not just one. Tests how broadly the AI can code. Higher is better. | 55.7% | — |
Terminal-Bench 2.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? (Version 2.0 of the test.) Higher is better. | 22.5% | — |
| Overview | ||
| Company | Mistral | OpenAI |
| Release date | Dec 9 2025 | Aug 7 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
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Devstral Small 2 (24B) and GPT-5 nano don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only GPT-5 nano has a verified first-party API price: $0.05 per million input tokens and $0.40 per million output tokens. No pay-as-you-go API rate is tracked for Devstral Small 2 (24B). GPT-5 nano shipped 124 days before Devstral Small 2 (24B), so benchmark comparisons should account for the intervening progress.
Context windows are 256k (Devstral Small 2 (24B)) vs 400k (GPT-5 nano). Devstral Small 2 (24B) is open weight, while GPT-5 nano is closed.
Direct benchmark comparisons are unavailable — Devstral Small 2 (24B) and GPT-5 nano don't publish scores on any of the same benchmarks.