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 | 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. | — | $10.00 |
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. | — | $30.00 |
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. | — | $10.00OpenAI |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $30.00OpenAI |
| — | $3,500OpenAI list price | |
| Benchmarks | ||
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% | 42.5% |
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. | 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% | — |
IFBenchInstruction following — Tests whether the AI can follow detailed instructions and satisfy multiple constraints at once. Higher is better. | 70.86% | — |
| Overview | ||
| Company | NVIDIA | OpenAI |
| Release date | Oct 15 2026 | Nov 6 2023 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Nemotron 3.5 Super VLvsClaude Haiku 5.5GPT-4 TurbovsClaude Haiku 5.5Nemotron 3.5 Super VLvsGemini 4 ArgonGPT-4 TurbovsGemini 4 ArgonNemotron 3.5 Super VLvsMuse Spark OpenGPT-4 TurbovsMuse Spark OpenNemotron 3.5 Super VLvsGrok 4.7GPT-4 TurbovsGrok 4.7Nemotron 3.5 Super VLvsDeepSeek-V4.1-FlashGPT-4 TurbovsDeepSeek-V4.1-FlashNemotron 3.5 Super VLvsMistral Large 4GPT-4 TurbovsMistral Large 4Frequently asked questions
Nemotron 3.5 Super VL leads GPT-4 Turbo on 1 of the 1 benchmark they both report (GPQA Diamond). Only GPT-4 Turbo has a verified first-party API price: $10.00 per million input tokens and $30.00 per million output tokens. No pay-as-you-go API rate is tracked for Nemotron 3.5 Super VL. GPT-4 Turbo shipped 1074 days before Nemotron 3.5 Super VL, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Nemotron 3.5 Super VL) vs 128k (GPT-4 Turbo). Nemotron 3.5 Super VL is open weight, while GPT-4 Turbo is closed.
On GPQA Diamond, Nemotron 3.5 Super VL leads at 89.52% vs GPT-4 Turbo at 42.5%.