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. | 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. | 1M | 400k |
| 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. | $0.06Nebius | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.24Nebius | — |
| $24.00Nebius · fp8 | — | |
| 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% | — |
Humanity's Last Exam · with toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. | 10.6% | — |
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 | NVIDIA | OpenAI |
| Release date | Dec 15 2025 | Nov 13 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Nemotron 3 NanovsClaude Fable 5.2GPT-5.1-Codex-MinivsClaude Fable 5.2Nemotron 3 NanovsGemini 4 CarbonGPT-5.1-Codex-MinivsGemini 4 CarbonNemotron 3 NanovsMuse EmberGPT-5.1-Codex-MinivsMuse EmberNemotron 3 NanovsGrok 4.7GPT-5.1-Codex-MinivsGrok 4.7Nemotron 3 NanovsDeepSeek-V4.1-Flash-1017GPT-5.1-Codex-MinivsDeepSeek-V4.1-Flash-1017Nemotron 3 NanovsMistral Large 4GPT-5.1-Codex-MinivsMistral Large 4Frequently asked questions
Nemotron 3 Nano and GPT-5.1-Codex-Mini don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. GPT-5.1-Codex-Mini shipped 32 days before Nemotron 3 Nano, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Nemotron 3 Nano) vs 400k (GPT-5.1-Codex-Mini). Nemotron 3 Nano is open weight, while GPT-5.1-Codex-Mini is closed.
Direct benchmark comparisons are unavailable — Nemotron 3 Nano and GPT-5.1-Codex-Mini don't publish scores on any of the same benchmarks.