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 |
| Benchmarks | ||
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. | 76.2% | 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. | 91.9% | 89.52% |
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. | 48% | — |
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 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% |
Next.js EvalsNext.js coding — Vercel's open eval of how well AI coding agents build and migrate real Next.js apps — measured as the share of tasks the agent completes successfully. Higher is better. | 52% | — |
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% |
ARC-AGI-2Abstract reasoning — Puzzle-style tests of abstract reasoning and pattern-finding — the kind of thing people find easy but AIs often struggle with. Higher is better. | 31.1% | — |
IFBenchInstruction following — Tests whether the AI can follow detailed instructions and satisfy multiple constraints at once. Higher is better. | — | 70.86% |
MMMUMultimodal — Tests the AI on understanding images and text together across many college subjects. Higher is better. | 81% | — |
| Overview | ||
| Company | NVIDIA | |
| Release date | Nov 18 2025 | Oct 15 2026 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Gemini 3.0 ProvsClaude Haiku 5.5Nemotron 3.5 Super VLvsClaude Haiku 5.5Gemini 3.0 ProvsGPT-6.1 SolNemotron 3.5 Super VLvsGPT-6.1 SolGemini 3.0 ProvsMuse Spark OpenNemotron 3.5 Super VLvsMuse Spark OpenGemini 3.0 ProvsGrok 4.7Nemotron 3.5 Super VLvsGrok 4.7Gemini 3.0 ProvsDeepSeek-V4.1-FlashNemotron 3.5 Super VLvsDeepSeek-V4.1-FlashGemini 3.0 ProvsMistral Large 4Nemotron 3.5 Super VLvsMistral Large 4Frequently asked questions
Gemini 3.0 Pro leads Nemotron 3.5 Super VL on 2 of the 2 benchmarks they both report (SWE-Bench Verified, GPQA Diamond). Gemini 3.0 Pro shipped 331 days before Nemotron 3.5 Super VL, so benchmark comparisons should account for the intervening progress.
Gemini 3.0 Pro is closed, while Nemotron 3.5 Super VL is open weight.
On SWE-Bench Verified, Gemini 3.0 Pro leads at 76.2% vs Nemotron 3.5 Super VL at 71.1%. On GPQA Diamond, Gemini 3.0 Pro leads at 91.9% vs Nemotron 3.5 Super VL at 89.52%.