Nemotron Nano 2vsGPT-4o
Nemotron Nano 2 | GPT-4o | |
|---|---|---|
| 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. | 9B | — |
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 | ||
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. | — | $2.50Azure |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | — | $10.00Azure |
| Benchmarks | ||
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 64% | 49.9% |
| BenchmarksPublished by one model only | ||
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. | — | 12% |
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. | 71.1% | — |
| Overview | ||
| Company | NVIDIA | OpenAI |
| Release date | Aug 18 2025 | May 13 2024 |
| Access | Open Weight | Proprietary |
Other comparisons
Frequently asked questions
Nemotron Nano 2 leads GPT-4o on 1 of the 1 benchmark they both report (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 Nano 2. GPT-4o shipped 462 days before Nemotron Nano 2, so benchmark comparisons should account for the intervening progress.
Nemotron Nano 2 is open weight, while GPT-4o is proprietary.
On GPQA Diamond, Nemotron Nano 2 leads at 64% vs GPT-4o at 49.9%.
Nemotron Nano 2 was released by NVIDIA on Aug 18 2025.
GPT-4o was released by OpenAI on May 13 2024.
Nemotron Nano 2 leads on GPQA Diamond — Nemotron Nano 2 64% vs GPT-4o 49.9%.
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 Nano 2. Rates are pay-as-you-go API prices verified on August 18, 2026.
Nemotron Nano 2 is an open weight model released by NVIDIA. GPT-4o is a proprietary model released by OpenAI.