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 | $1.25 |
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 | $10.00 |
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. | $0.15 | $0.125 |
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 | $1.25Azure |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $7.50Mistral | $10.00Azure |
| $675Mistral list price | $750OpenAI list price | |
| 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. | — | 21% |
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
Mistral Medium 3.5vsClaude Haiku 5.5GPT-5vsClaude Haiku 5.5Mistral Medium 3.5vsGemini 4 CarbonGPT-5vsGemini 4 CarbonMistral Medium 3.5vsMuse Spark OpenGPT-5vsMuse Spark OpenMistral Medium 3.5vsGrok 4.7GPT-5vsGrok 4.7Mistral Medium 3.5vsDeepSeek-V4.1-Flash-1017GPT-5vsDeepSeek-V4.1-Flash-1017Mistral Medium 3.5vsKimi K3.1 ProGPT-5vsKimi K3.1 ProFrequently asked questions
Mistral Medium 3.5 and GPT-5 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. GPT-5 is cheaper on input: $1.25 vs $1.50 per million tokens. Mistral Medium 3.5 is cheaper on output: $7.50 vs $10.00 per million tokens. GPT-5 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). Mistral Medium 3.5 is open weight, while GPT-5 is closed.
Direct benchmark comparisons are unavailable — Mistral Medium 3.5 and GPT-5 don't publish scores on any of the same benchmarks.