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. | — | 9B |
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. | 1M | 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. | $0.10 | — |
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. | $0.40 | — |
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. | $0.01 | — |
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. | $0.05Google AI Studio | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.20Google AI Studio | — |
| $40.00Google 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. | — | 5% |
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% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 64% |
| Overview | ||
| Company | NVIDIA | |
| Release date | Jun 17 2025 | Aug 18 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Gemini 2.5 Flash-LitevsClaude Haiku 5.5Nemotron Nano 2vsClaude Haiku 5.5Gemini 2.5 Flash-LitevsGPT-6.1 SolNemotron Nano 2vsGPT-6.1 SolGemini 2.5 Flash-LitevsMuse Spark OpenNemotron Nano 2vsMuse Spark OpenGemini 2.5 Flash-LitevsGrok 4.7Nemotron Nano 2vsGrok 4.7Gemini 2.5 Flash-LitevsDeepSeek-V4.1-Flash-1017Nemotron Nano 2vsDeepSeek-V4.1-Flash-1017Gemini 2.5 Flash-LitevsMistral Large 4Nemotron Nano 2vsMistral Large 4Frequently asked questions
Gemini 2.5 Flash-Lite and Nemotron Nano 2 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Gemini 2.5 Flash-Lite has a verified first-party API price: $0.10 per million input tokens and $0.40 per million output tokens. No pay-as-you-go API rate is tracked for Nemotron Nano 2. Gemini 2.5 Flash-Lite shipped 62 days before Nemotron Nano 2, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Gemini 2.5 Flash-Lite) vs 128k (Nemotron Nano 2). Gemini 2.5 Flash-Lite is closed, while Nemotron Nano 2 is open weight.
Direct benchmark comparisons are unavailable — Gemini 2.5 Flash-Lite and Nemotron Nano 2 don't publish scores on any of the same benchmarks.