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. | — | 120B |
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. | — | 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. | $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 | $0.10Crusoe |
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 | $0.40Crusoe |
| $40.00Google list price | $40.00Crusoe · bf16 | |
| These models have no shared benchmark scores. | ||
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. | — | 51.78% |
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. | — | 71.1% |
SWE-Bench MultilingualMultilingual coding — Like SWE-Bench, but the coding problems span many programming languages, not just one. Tests how broadly the AI can code. Higher is better. | — | 67.72% |
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. | — | 88.22% |
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. | — | 66.7% |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | — | 78% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 89.52% |
IFBenchInstruction following — Tests whether the AI can follow detailed instructions and satisfy multiple constraints at once. Higher is better. | — | 70.86% |
| Overview | ||
| Company | NVIDIA | |
| Release date | Jun 17 2025 | Oct 15 2026 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Gemini 2.5 Flash-LitevsClaude Haiku 5.5Nemotron 3.5 Super VLvsClaude Haiku 5.5Gemini 2.5 Flash-LitevsGPT-6.1 SolNemotron 3.5 Super VLvsGPT-6.1 SolGemini 2.5 Flash-LitevsMuse Spark OpenNemotron 3.5 Super VLvsMuse Spark OpenGemini 2.5 Flash-LitevsGrok 4.7Nemotron 3.5 Super VLvsGrok 4.7Gemini 2.5 Flash-LitevsDeepSeek-V4.1-Flash-1017Nemotron 3.5 Super VLvsDeepSeek-V4.1-Flash-1017Gemini 2.5 Flash-LitevsMistral Large 4Nemotron 3.5 Super VLvsMistral Large 4Frequently asked questions
Gemini 2.5 Flash-Lite and Nemotron 3.5 Super VL 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 3.5 Super VL. Gemini 2.5 Flash-Lite shipped 485 days before Nemotron 3.5 Super VL, so benchmark comparisons should account for the intervening progress.
Gemini 2.5 Flash-Lite is closed, while Nemotron 3.5 Super VL is open weight.
Direct benchmark comparisons are unavailable — Gemini 2.5 Flash-Lite and Nemotron 3.5 Super VL don't publish scores on any of the same benchmarks.