Compare AI models
| 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 | 120B |
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 | 1M |
| 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 | — |
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 | — |
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. | $1.50Mistral | $0.08DekaLLM |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $7.50Mistral | $0.40DeepInfra |
| Benchmarks | ||
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% | 60.5% |
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. | — | 54% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 79.2% |
AA Intelligence IndexOverall intelligence — Artificial Analysis composite intelligence index across evals | — | 36 |
| Overview | ||
| Company | Mistral | NVIDIA |
| Release date | Apr 29 2026 | Mar 11 2026 |
| Access | Open Weight | Open Source |
| Model details | View model | View model |
Other comparisons
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Mistral Medium 3.5 leads Nemotron 3 Super on 1 of the 1 benchmark they both report (SWE-Bench Verified). Only Mistral Medium 3.5 has a verified first-party API price: $1.50 per million input tokens and $7.50 per million output tokens. No pay-as-you-go API rate is tracked for Nemotron 3 Super. Nemotron 3 Super shipped 49 days before Mistral Medium 3.5, so benchmark comparisons should account for the intervening progress.
Mistral Medium 3.5 has 128B parameters, while Nemotron 3 Super has 120B. Context windows are 256k (Mistral Medium 3.5) vs 1M (Nemotron 3 Super). Mistral Medium 3.5 is open weight, while Nemotron 3 Super is open source.
On SWE-Bench Verified, Mistral Medium 3.5 leads at 77.6% vs Nemotron 3 Super at 60.5%.