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 | 117B |
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 | ||
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. | — | $0.03AkashML |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $0.15Venice |
| — | $13.50Venice · int4 | |
| 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. | 71.1% | 62.4% |
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% | 80.1% |
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. | — | 12% |
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% | — |
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% | — |
Humanity's Last Exam · no toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects, written so you can't just look up the answer. “No tools” means the AI answers on its own. Higher is better. | — | 14.9% |
Humanity's Last Exam · with toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. | — | 19% |
IFBenchInstruction following — Tests whether the AI can follow detailed instructions and satisfy multiple constraints at once. Higher is better. | 70.86% | — |
MMLUGeneral knowledge — A 57-subject multiple-choice exam — history, law, medicine, maths — that was the standard measure of how much a model knows from 2020 until roughly 2024, when frontier scores crowded into the high 80s and labs moved on to harder tests. The scores here were published years apart under different testing setups, so read them as a historical record rather than a like-for-like ranking. Higher is better. | — | 90% |
| Overview | ||
| Company | NVIDIA | OpenAI |
| Release date | Oct 15 2026 | Aug 5 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
Nemotron 3.5 Super VLvsClaude Haiku 5.5gpt-oss-120bvsClaude Haiku 5.5Nemotron 3.5 Super VLvsGemini 4 Argongpt-oss-120bvsGemini 4 ArgonNemotron 3.5 Super VLvsMuse Spark Opengpt-oss-120bvsMuse Spark OpenNemotron 3.5 Super VLvsGrok 4.7gpt-oss-120bvsGrok 4.7Nemotron 3.5 Super VLvsDeepSeek-V4.1-Flashgpt-oss-120bvsDeepSeek-V4.1-FlashNemotron 3.5 Super VLvsMistral Large 4gpt-oss-120bvsMistral Large 4Frequently asked questions
Nemotron 3.5 Super VL leads gpt-oss-120b on 2 of the 2 benchmarks they both report (SWE-Bench Verified, GPQA Diamond). gpt-oss-120b shipped 436 days before Nemotron 3.5 Super VL, so benchmark comparisons should account for the intervening progress.
Nemotron 3.5 Super VL has 120B parameters, while gpt-oss-120b has 117B. Context windows are 1M (Nemotron 3.5 Super VL) vs 128k (gpt-oss-120b).
On SWE-Bench Verified, Nemotron 3.5 Super VL leads at 71.1% vs gpt-oss-120b at 62.4%. On GPQA Diamond, Nemotron 3.5 Super VL leads at 89.52% vs gpt-oss-120b at 80.1%.