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. | 120B | — |
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. | 1M | 128k |
| 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. | — | $2.50 |
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. | — | $10.00 |
Cached input priceA reduced rate for text you send over and over. If every request starts with the same instructions or the same document, the provider keeps a copy ready and charges less to read it again. | — | $1.25 |
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. | $0.08DekaLLM | $2.50Azure |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $0.40DeepInfra | $10.00Azure |
| 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. | 54% | 12% |
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% | 49.9% |
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% | — |
AA Intelligence IndexOverall intelligence — Artificial Analysis composite intelligence index across evals | 36 | — |
| Overview | ||
| Company | NVIDIA | OpenAI |
| Release date | Mar 11 2026 | May 13 2024 |
| Access | Open Source | Closed |
| Model details | View model | View model |
Other comparisons
Nemotron 3 SupervsClaude Haiku 5.5GPT-4ovsClaude Haiku 5.5Nemotron 3 SupervsGemini 4 ArgonGPT-4ovsGemini 4 ArgonNemotron 3 SupervsMuse Spark 1.3GPT-4ovsMuse Spark 1.3Nemotron 3 SupervsGrok 4.7GPT-4ovsGrok 4.7Nemotron 3 SupervsDeepSeek-V4.1-FlashGPT-4ovsDeepSeek-V4.1-FlashNemotron 3 SupervsMistral Large 4GPT-4ovsMistral Large 4Frequently asked questions
Nemotron 3 Super leads GPT-4o on 2 of the 2 benchmarks they both report (BullshitBench v2, GPQA Diamond). Only GPT-4o has a verified first-party API price: $2.50 per million input tokens and $10.00 per million output tokens. No pay-as-you-go API rate is tracked for Nemotron 3 Super. GPT-4o shipped 667 days before Nemotron 3 Super, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Nemotron 3 Super) vs 128k (GPT-4o). Nemotron 3 Super is open source, while GPT-4o is closed.
On BullshitBench v2, Nemotron 3 Super leads at 54% vs GPT-4o at 12%. On GPQA Diamond, Nemotron 3 Super leads at 79.2% vs GPT-4o at 49.9%.