Compare AI models
| Specifications | ||
ParametersThe learned values that shape how a model responds. B means billion and T means trillion. For mixture-of-experts models, the total count is larger than the number active for each token. Parameter count alone does not measure quality. | 253B | — |
Context windowHow much information the model can work with in one request, measured in tokens — pieces of words or other data. This includes your question, attachments, conversation and reply. Limits can vary by model variant and provider. | 128k | 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. | — | $2.00 |
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. | — | $8.00 |
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. | — | $0.50 |
Cheapest inputLowest input rate across every provider on OpenRouter, the lab included. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | — | $2.00Azure |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $8.00Azure |
| — | $800OpenAI list price | |
| These models have no shared benchmark scores. | ||
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. | — | 14% |
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. | — | 54.6% |
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% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 76% | — |
| Overview | ||
| Company | NVIDIA | OpenAI |
| Release date | Apr 8 2025 | Apr 14 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Llama Nemotron Ultra 253BvsClaude Haiku 5.5GPT-4.1vsClaude Haiku 5.5Llama Nemotron Ultra 253BvsGemini 4 ArgonGPT-4.1vsGemini 4 ArgonLlama Nemotron Ultra 253BvsMuse Spark OpenGPT-4.1vsMuse Spark OpenLlama Nemotron Ultra 253BvsGrok 4.7GPT-4.1vsGrok 4.7Llama Nemotron Ultra 253BvsDeepSeek-V4.1-Flash-1017GPT-4.1vsDeepSeek-V4.1-Flash-1017Llama Nemotron Ultra 253BvsMistral Large 4GPT-4.1vsMistral Large 4Frequently asked questions
Llama Nemotron Ultra 253B and GPT-4.1 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only GPT-4.1 has a verified first-party API price: $2.00 per million input tokens and $8.00 per million output tokens. No pay-as-you-go API rate is tracked for Llama Nemotron Ultra 253B. Llama Nemotron Ultra 253B shipped 6 days before GPT-4.1, so benchmark comparisons should account for the intervening progress.
Context windows are 128k (Llama Nemotron Ultra 253B) vs 1M (GPT-4.1). Llama Nemotron Ultra 253B is open weight, while GPT-4.1 is closed.
Direct benchmark comparisons are unavailable — Llama Nemotron Ultra 253B and GPT-4.1 don't publish scores on any of the same benchmarks.