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. | 7B | 30B |
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. | 32k | 1M |
| API pricingUSD per 1M tokens · lower wins | ||
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. | — | $0.00Nvidia |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $0.00Nvidia |
| — | $11.00Darkbloom · int4 | |
| 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. | — | 51.6% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 75.4% |
| Overview | ||
| Company | Mistral | NVIDIA |
| Release date | Jul 16 2024 | Aug 11 2026 |
| Access | Open Weight | Open Source |
| Model details | View model | View model |
Other comparisons
Mathstral 7BvsClaude Haiku 5.5Nemotron 3.5 LightningvsClaude Haiku 5.5Mathstral 7BvsGPT-6.1 SolNemotron 3.5 LightningvsGPT-6.1 SolMathstral 7BvsGemini 4 ArgonNemotron 3.5 LightningvsGemini 4 ArgonMathstral 7BvsMuse Spark OpenNemotron 3.5 LightningvsMuse Spark OpenMathstral 7BvsGrok 4.7Nemotron 3.5 LightningvsGrok 4.7Mathstral 7BvsDeepSeek-V4.1-Flash-1017Nemotron 3.5 LightningvsDeepSeek-V4.1-Flash-1017Frequently asked questions
Mathstral 7B and Nemotron 3.5 Lightning don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Mathstral 7B shipped 756 days before Nemotron 3.5 Lightning, so benchmark comparisons should account for the intervening progress.
Mathstral 7B has 7B parameters, while Nemotron 3.5 Lightning has 30B. Context windows are 32k (Mathstral 7B) vs 1M (Nemotron 3.5 Lightning). Mathstral 7B is open weight, while Nemotron 3.5 Lightning is open source.
Direct benchmark comparisons are unavailable — Mathstral 7B and Nemotron 3.5 Lightning don't publish scores on any of the same benchmarks.