Llama Nemotron Ultra 253BvsGPT-4o mini
Llama Nemotron Ultra 253B | GPT-4o mini | |
|---|---|---|
| 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. | 253B | — |
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. | — | $0.15 |
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. | — | $0.60 |
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. | — | $0.075 |
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. | — | $0.15Azure |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | — | $0.60Azure |
| Benchmarks | ||
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 76% | 40.2% |
| 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. | — | 2% |
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. | 66.3% | — |
| Overview | ||
| Company | NVIDIA | OpenAI |
| Release date | Apr 8 2025 | Jul 18 2024 |
| Access | Open Weight | Proprietary |
Other comparisons
Frequently asked questions
Llama Nemotron Ultra 253B leads GPT-4o mini on 1 of the 1 benchmark they both report (GPQA Diamond). Only GPT-4o mini has a verified first-party API price: $0.15 per million input tokens and $0.60 per million output tokens. No pay-as-you-go API rate is tracked for Llama Nemotron Ultra 253B. GPT-4o mini shipped 264 days before Llama Nemotron Ultra 253B, so benchmark comparisons should account for the intervening progress.
Llama Nemotron Ultra 253B is open weight, while GPT-4o mini is proprietary.
On GPQA Diamond, Llama Nemotron Ultra 253B leads at 76% vs GPT-4o mini at 40.2%.
Llama Nemotron Ultra 253B was released by NVIDIA on Apr 8 2025.
GPT-4o mini was released by OpenAI on Jul 18 2024.
Llama Nemotron Ultra 253B leads on GPQA Diamond — Llama Nemotron Ultra 253B 76% vs GPT-4o mini 40.2%.
Only GPT-4o mini has a verified first-party API price: $0.15 per million input tokens and $0.60 per million output tokens. No pay-as-you-go API rate is tracked for Llama Nemotron Ultra 253B. Rates are pay-as-you-go API prices verified on August 18, 2026.
Llama Nemotron Ultra 253B is an open weight model released by NVIDIA. GPT-4o mini is a proprietary model released by OpenAI.