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. | 3B | 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. | 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. | $0.10 | — |
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. | $0.10 | — |
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. | $0.10Mistral | $0.05Novita |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.10Mistral | $0.20Novita |
| $25.00Mistral list price | $20.00Novita · fp4 | |
| 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. | — | 28% |
LiveCodeBenchCompetitive coding — Coding problems published so recently the AI can't have seen them in training — a contamination-free test of raw programming skill. Higher is better. | — | 68.3% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 73% |
| Overview | ||
| Company | Mistral | NVIDIA |
| Release date | Dec 2 2025 | Dec 15 2025 |
| Access | Open Weight | Open Source |
| Model details | View model | View model |
Other comparisons
Ministral 3 3B-25-12vsClaude Haiku 5.5Nemotron 3 NanovsClaude Haiku 5.5Ministral 3 3B-25-12vsGPT-6.1 SolNemotron 3 NanovsGPT-6.1 SolMinistral 3 3B-25-12vsGemini 4 ArgonNemotron 3 NanovsGemini 4 ArgonMinistral 3 3B-25-12vsMuse Spark OpenNemotron 3 NanovsMuse Spark OpenMinistral 3 3B-25-12vsGrok 4.7Nemotron 3 NanovsGrok 4.7Ministral 3 3B-25-12vsDeepSeek-V4.1-Flash-1017Nemotron 3 NanovsDeepSeek-V4.1-Flash-1017Frequently asked questions
Ministral 3 3B-25-12 and Nemotron 3 Nano don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Ministral 3 3B-25-12 has a verified first-party API price: $0.10 per million input tokens and $0.10 per million output tokens. No pay-as-you-go API rate is tracked for Nemotron 3 Nano. Ministral 3 3B-25-12 shipped 13 days before Nemotron 3 Nano, so benchmark comparisons should account for the intervening progress.
Ministral 3 3B-25-12 has 3B parameters, while Nemotron 3 Nano has 30B. Context windows are 256k (Ministral 3 3B-25-12) vs 1M (Nemotron 3 Nano). Ministral 3 3B-25-12 is open weight, while Nemotron 3 Nano is open source.
Direct benchmark comparisons are unavailable — Ministral 3 3B-25-12 and Nemotron 3 Nano don't publish scores on any of the same benchmarks.