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 |
| 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. | $0.30 | — |
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. | $2.50 | — |
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. | $0.03 | — |
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. | $0.15Google AI Studio | $0.08DekaLLM |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $1.25Google AI Studio | $0.40DeepInfra |
| 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. | 19% | 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% |
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 | |
| Release date | Apr 17 2025 | Mar 11 2026 |
| Access | Closed | Open Source |
| Model details | View model | View model |
Other comparisons
Gemini 2.5 FlashvsClaude Haiku 5.5Nemotron 3 SupervsClaude Haiku 5.5Gemini 2.5 FlashvsGPT-6.1 SolNemotron 3 SupervsGPT-6.1 SolGemini 2.5 FlashvsMuse Spark 1.3Nemotron 3 SupervsMuse Spark 1.3Gemini 2.5 FlashvsGrok 4.7Nemotron 3 SupervsGrok 4.7Gemini 2.5 FlashvsDeepSeek-V4.1-FlashNemotron 3 SupervsDeepSeek-V4.1-FlashGemini 2.5 FlashvsMistral Large 4Nemotron 3 SupervsMistral Large 4Frequently asked questions
Nemotron 3 Super leads Gemini 2.5 Flash on 1 of the 1 benchmark they both report (BullshitBench v2). Only Gemini 2.5 Flash has a verified first-party API price: $0.30 per million input tokens and $2.50 per million output tokens. No pay-as-you-go API rate is tracked for Nemotron 3 Super. Gemini 2.5 Flash shipped 328 days before Nemotron 3 Super, so benchmark comparisons should account for the intervening progress.
Gemini 2.5 Flash is closed, while Nemotron 3 Super is open source.
On BullshitBench v2, Nemotron 3 Super leads at 54% vs Gemini 2.5 Flash at 19%.