Compare AI models
| Specifications | ||
ParametersA rough measure of how big the model is. More parameters usually means more capable and more expensive to run, though it is a poor guide on its own — a smaller, newer model often beats a larger, older one. | 550B | 117B |
Context windowHow much text the model can hold in mind at once — your question, any documents you attach, the conversation so far, and its own reply. Go past it and the earliest part falls out of view. | — | 128k |
| API pricingUSD per 1M tokens · lower wins | ||
Cheapest inputLowest input rate across third-party providers, excluding the lab itself. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | $0.50DeepInfra | $0.03AkashML |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $2.20DeepInfra | $0.15Venice |
| Benchmarks | ||
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. | 49% | 12% |
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. | 71.9% | 62.4% |
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. | 26.7% | 19% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 86.7% | 80.1% |
Terminal-Bench 2.1Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Higher is better. | 56.4% | — |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | 44.4% | — |
Humanity's Last Exam · no toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects, written so you can't just look up the answer. “No tools” means the AI answers on its own. Higher is better. | — | 14.9% |
MMLUGeneral knowledge — A 57-subject multiple-choice exam — history, law, medicine, maths — that was the standard measure of how much a model knows from 2020 until roughly 2024, when frontier scores crowded into the high 80s and labs moved on to harder tests. The scores here were published years apart under different testing setups, so read them as a historical record rather than a like-for-like ranking. Higher is better. | — | 90% |
AA Intelligence IndexOverall intelligence — Artificial Analysis composite intelligence index across evals | 48 | — |
| Overview | ||
| Company | NVIDIA | OpenAI |
| Release date | Jun 4 2026 | Aug 5 2025 |
| Access | Open Source | Open Weight |
| Model details | View model | View model |
Other comparisons
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Nemotron 3 Ultra leads gpt-oss-120b on 4 of the 4 benchmarks they both report (BullshitBench v2, SWE-Bench Verified, Humanity's Last Exam, GPQA Diamond). gpt-oss-120b shipped 303 days before Nemotron 3 Ultra, so benchmark comparisons should account for the intervening progress.
Nemotron 3 Ultra has 550B parameters, while gpt-oss-120b has 117B. Nemotron 3 Ultra is open source, while gpt-oss-120b is open weight.
On BullshitBench v2, Nemotron 3 Ultra leads at 49% vs gpt-oss-120b at 12%. On SWE-Bench Verified, Nemotron 3 Ultra leads at 71.9% vs gpt-oss-120b at 62.4%. On Humanity's Last Exam · with tools, Nemotron 3 Ultra leads at 26.7% vs gpt-oss-120b at 19%. On GPQA Diamond, Nemotron 3 Ultra leads at 86.7% vs gpt-oss-120b at 80.1%.