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. | 120B | — |
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 | 128k |
| 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. | $0.08DekaLLM | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.40DeepInfra | — |
| $37.00DeepInfra · bf16 | — | |
| Benchmarks | ||
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. | 81.19% | 74.8% |
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% | — |
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. | 60.5% | — |
SWE-Bench MultilingualMultilingual coding — Like SWE-Bench, but the coding problems span many programming languages, not just one. Tests how broadly the AI can code. Higher is better. | 45.78% | — |
Terminal-Bench 2.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? (Version 2.0 of the test.) Higher is better. | 31% | — |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | 31.28% | — |
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 | NVIDIA | SpaceXSI |
| Release date | Mar 11 2026 | Feb 17 2025 |
| Access | Open Source | Closed |
| Model details | View model | View model |
Other comparisons
Nemotron 3 SupervsClaude Haiku 5.5Grok 3 minivsClaude Haiku 5.5Nemotron 3 SupervsGPT-6.1 SolGrok 3 minivsGPT-6.1 SolNemotron 3 SupervsGemini 4 ArgonGrok 3 minivsGemini 4 ArgonNemotron 3 SupervsMuse Spark OpenGrok 3 minivsMuse Spark OpenNemotron 3 SupervsDeepSeek-V4.1-Flash-1017Grok 3 minivsDeepSeek-V4.1-Flash-1017Nemotron 3 SupervsMistral Large 4Grok 3 minivsMistral Large 4Frequently asked questions
Nemotron 3 Super leads Grok 3 mini on 1 of the 1 benchmark they both report (LiveCodeBench). Grok 3 mini shipped 387 days before Nemotron 3 Super, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Nemotron 3 Super) vs 128k (Grok 3 mini). Nemotron 3 Super is open source, while Grok 3 mini is closed.
On LiveCodeBench, Nemotron 3 Super leads at 81.19% vs Grok 3 mini at 74.8%.