Compare AI models
| 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 |
| 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. | $0.30 | — |
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. | $2.50 | — |
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. | $0.03 | — |
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. | $0.15Google | — |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $1.25Google | — |
| Benchmarks | ||
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. | 66% | 5% |
SWE-Bench ProAgentic coding — Can the AI fix real bugs in real software? It's handed actual problems from open-source projects and has to write code that genuinely solves them. Higher is better. | 54.2% | — |
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. | — | 71.1% |
Terminal-Bench 2.1Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Higher is better. | 54% | — |
BU BenchBrowser agent — Can the AI drive a real web browser to finish tasks — clicking, filling forms, and navigating sites the way a person would? Run by Browser Use on their BU Bench task set. Higher is better. | 49% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 64% |
OSWorld-VerifiedAgentic computer use — Can the AI actually operate a computer — clicking, typing, and using real apps — to finish tasks on its own? Higher is better. | 74% | — |
GDPval-AA v2Knowledge work — economically valuable knowledge work (v2, re-based Elo) | 1140 | — |
| Overview | ||
| Company | NVIDIA | |
| Release date | Jul 21 2026 | Aug 18 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Gemini 3.5 Flash-LitevsClaude Haiku 5.5Nemotron Nano 2vsClaude Haiku 5.5Gemini 3.5 Flash-LitevsGPT-6.1 SolNemotron Nano 2vsGPT-6.1 SolGemini 3.5 Flash-LitevsMuse Spark 1.3Nemotron Nano 2vsMuse Spark 1.3Gemini 3.5 Flash-LitevsGrok 4.7Nemotron Nano 2vsGrok 4.7Gemini 3.5 Flash-LitevsDeepSeek-V4.1-FlashNemotron Nano 2vsDeepSeek-V4.1-FlashGemini 3.5 Flash-LitevsMistral Large 4Nemotron Nano 2vsMistral Large 4Frequently asked questions
Gemini 3.5 Flash-Lite leads Nemotron Nano 2 on 1 of the 1 benchmark they both report (BullshitBench v2). Only Gemini 3.5 Flash-Lite has a verified first-party API price: $0.30 per million input tokens and $2.50 per million output tokens. No pay-as-you-go API rate is tracked for Nemotron Nano 2. Nemotron Nano 2 shipped 337 days before Gemini 3.5 Flash-Lite, so benchmark comparisons should account for the intervening progress.
Gemini 3.5 Flash-Lite is closed, while Nemotron Nano 2 is open weight.
On BullshitBench v2, Gemini 3.5 Flash-Lite leads at 66% vs Nemotron Nano 2 at 5%.