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. | 128B | — |
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. | $1.50 | $0.25 |
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. | $7.50 | $2.00 |
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. | — | $0.025 |
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. | $1.50Mistral | $0.125OpenAI |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $7.50Mistral | $1.00OpenAI |
| $675Mistral list price | $150OpenAI list price | |
| These models have no shared benchmark scores. | ||
ProgramBenchProgram reconstruction — The AI receives a working program and its documentation, then builds a replacement from scratch without the original source code, internet access or decompilation. The score is the percentage of 200 programs that pass every behavioral test. We record each model's best published mini-SWE-agent result, including higher reasoning efforts where available. Partial test-pass rates and almost-solved programs do not count toward this score. Equal scores share a rank here; the official board also uses partial progress to break ties. Higher is better. | — | 0% |
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. | 77.6% | — |
| Overview | ||
| Company | Mistral | OpenAI |
| Release date | Apr 29 2026 | Aug 7 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
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Mistral Medium 3.5 and GPT-5 mini don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. GPT-5 mini is cheaper on both input and output: $0.25 vs $1.50 per million input tokens, and $2.00 vs $7.50 per million output tokens. GPT-5 mini shipped 265 days before Mistral Medium 3.5, so benchmark comparisons should account for the intervening progress.
Context windows are 256k (Mistral Medium 3.5) vs 400k (GPT-5 mini). Mistral Medium 3.5 is open weight, while GPT-5 mini is closed.
Direct benchmark comparisons are unavailable — Mistral Medium 3.5 and GPT-5 mini don't publish scores on any of the same benchmarks.