Mistral Medium 3.5vsGPT-4o
Mistral Medium 3.5 | GPT-4o | |
|---|---|---|
| Specifications | ||
ParametersA rough measure of how big the model is. More parameters usually means more capable and more expensive to run, though it is a poor guide on its own — a smaller, newer model often beats a larger, older one. | 128B | — |
Context windowHow much text the model can hold in mind at once — your question, any documents you attach, the conversation so far, and its own reply. Go past it and the earliest part falls out of view. | 256k | 128k |
| 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 | $2.50 |
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. | — | $1.25 |
Cheapest inputLowest input rate across third-party providers, excluding the lab itself. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | — | $2.50Azure |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | — | $10.00Azure |
| BenchmarksPublished by one model only | ||
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. | — | 12% |
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% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 49.9% |
| Overview | ||
| Company | Mistral | OpenAI |
| Release date | Apr 29 2026 | May 13 2024 |
| Access | Open Weight | Proprietary |
Other comparisons
Mistral Medium 3.5vsClaude Fable 5.1GPT-4ovsClaude Fable 5.1Mistral Medium 3.5vsGemini 3.8 FlashGPT-4ovsGemini 3.8 FlashMistral Medium 3.5vsMuse Spark 1.3GPT-4ovsMuse Spark 1.3Mistral Medium 3.5vsGrok 4.6GPT-4ovsGrok 4.6Mistral Medium 3.5vsDeepSeek-V4.1-FlashGPT-4ovsDeepSeek-V4.1-FlashMistral Medium 3.5vsKimi K3GPT-4ovsKimi K3
Frequently asked questions
Mistral Medium 3.5 and GPT-4o don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Mistral Medium 3.5 is cheaper on both input and output: $1.50 vs $2.50 per million input tokens, and $7.50 vs $10.00 per million output tokens. GPT-4o shipped 716 days before Mistral Medium 3.5, so benchmark comparisons should account for the intervening progress.
Context windows are 256k (Mistral Medium 3.5) vs 128k (GPT-4o). Mistral Medium 3.5 is open weight, while GPT-4o is proprietary.
Direct benchmark comparisons are unavailable — Mistral Medium 3.5 and GPT-4o don't publish scores on any of the same benchmarks.