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. | 8B/49B | — |
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 | 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 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. | — | $2.50Azure |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $10.00Azure |
| — | $1,000OpenAI 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. | — | 12% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 49.9% |
| Overview | ||
| Company | NVIDIA | OpenAI |
| Release date | Mar 18 2025 | May 13 2024 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Llama Nemotron (Nano/Super)vsClaude Haiku 5.5GPT-4ovsClaude Haiku 5.5Llama Nemotron (Nano/Super)vsGemini 4 ArgonGPT-4ovsGemini 4 ArgonLlama Nemotron (Nano/Super)vsMuse Spark OpenGPT-4ovsMuse Spark OpenLlama Nemotron (Nano/Super)vsGrok 4.7GPT-4ovsGrok 4.7Llama Nemotron (Nano/Super)vsDeepSeek-V4.1-Flash-1017GPT-4ovsDeepSeek-V4.1-Flash-1017Llama Nemotron (Nano/Super)vsMistral Large 4GPT-4ovsMistral Large 4Frequently asked questions
Llama Nemotron (Nano/Super) and GPT-4o don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. 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 Llama Nemotron (Nano/Super). GPT-4o shipped 309 days before Llama Nemotron (Nano/Super), so benchmark comparisons should account for the intervening progress.
Context windows are 128k (Llama Nemotron (Nano/Super)) vs 128k (GPT-4o). Llama Nemotron (Nano/Super) is open weight, while GPT-4o is closed.
Direct benchmark comparisons are unavailable — Llama Nemotron (Nano/Super) and GPT-4o don't publish scores on any of the same benchmarks.