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 | 314B |
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 | 8k |
| 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 | — | |
| 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. | 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% | — |
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% | — |
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 | Nov 3 2023 |
| Access | Open Source | Open Weight |
| Model details | View model | View model |
Other comparisons
Nemotron 3 SupervsClaude Haiku 5.5Grok‑1vsClaude Haiku 5.5Nemotron 3 SupervsGPT-6.1 SolGrok‑1vsGPT-6.1 SolNemotron 3 SupervsGemini 4 ArgonGrok‑1vsGemini 4 ArgonNemotron 3 SupervsMuse Spark OpenGrok‑1vsMuse Spark OpenNemotron 3 SupervsDeepSeek-V4.1-Flash-1017Grok‑1vsDeepSeek-V4.1-Flash-1017Nemotron 3 SupervsMistral Large 4Grok‑1vsMistral Large 4Frequently asked questions
Nemotron 3 Super and Grok‑1 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Grok‑1 shipped 859 days before Nemotron 3 Super, so benchmark comparisons should account for the intervening progress.
Nemotron 3 Super has 120B parameters, while Grok‑1 has 314B. Context windows are 1M (Nemotron 3 Super) vs 8k (Grok‑1). Nemotron 3 Super is open source, while Grok‑1 is open weight.
Direct benchmark comparisons are unavailable — Nemotron 3 Super and Grok‑1 don't publish scores on any of the same benchmarks.