Nemotron 3 SupervsQwen3.8-Flash-Next
Nemotron 3 Super | Qwen3.8-Flash-Next | |
|---|---|---|
| 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 | 125B |
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 | 262k |
| Benchmarks | ||
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 79.2% | 91.7% |
| BenchmarksPublished by one model only | ||
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. | — | 62.5% |
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. | 60.5% | — |
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. | — | 81% |
DeepSWE 1.1Agentic coding — Artificial Analysis' independent test of deep, agentic software-engineering work — the AI has to plan and carry out substantial coding tasks end to end. (Version 1.1 of the test.) Higher is better. | — | 58.7% |
NL2Repo-BenchRepo-level code generation — Tests whether the AI can turn a natural-language requirement into working code across an entire repository, not just produce a single function or patch. Higher is better. | — | 48.1% |
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. | — | 91.9% |
JobBenchProfessional tool use — Tests the AI on professional workplace tasks that require using real work tools — the kind of multi-step jobs an office worker handles. Higher is better. | — | 55.7% |
CoWorkBenchLong-horizon office work — Tests long-running office tasks across fields including computer science, finance, law, medicine, and other productivity work. Higher is better. | — | 73.9% |
Toolathlon-VerifiedPersonal tool use — Tests how well the AI uses everyday personal tools and apps to get things done — a human-checked version of Toolathlon. Higher is better. | — | 73.5% |
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. | — | 35.9% |
IFBenchInstruction following — Tests whether the AI can follow detailed instructions and satisfy multiple constraints at once. Higher is better. | — | 81.3% |
OSWorld 2.0Agentic computer use — Can the AI actually operate a computer — clicking, typing, and using real apps — to finish tasks on its own? Version 2.0 is a harder, refreshed task set. Higher is better. | — | 19.4% |
Agent's Last Exam · pass@1Agentic computer use — A hard set of desktop and operating-system tasks an AI agent has to finish by looking at the screen and working the machine itself. The score is the share it passes outright — partial credit does not count. Higher is better. | — | 24.3% |
Agent's Last Exam · scoreAgentic computer use — The graded score on the same desktop and operating-system tasks in Agent's Last Exam, giving partial credit for progress beyond the strict pass-or-fail result. Higher is better. | — | 51.2% |
AA Intelligence IndexOverall intelligence — Artificial Analysis composite intelligence index across evals | 36 | — |
CharXiv ReasoningChart reasoning — Can the AI read and reason about complex charts and figures, not just text? Higher is better. | — | 84.6% |
LVBenchVideo understanding — Can the AI follow a very long video — up to an hour — and answer questions that need details from far apart in it? Higher is better. | — | 76.6% |
| Overview | ||
| Company | NVIDIA | Qwen |
| Release date | Mar 11 2026 | Aug 26 2026 |
| Access | Open Source | Open Weight |
Other comparisons
Nemotron 3 SupervsClaude Opus 5Qwen3.8-Flash-NextvsClaude Opus 5Nemotron 3 SupervsGPT-5.6 SolQwen3.8-Flash-NextvsGPT-5.6 SolNemotron 3 SupervsGemini 3.7 FlashQwen3.8-Flash-NextvsGemini 3.7 FlashNemotron 3 SupervsMuse GlimmerQwen3.8-Flash-NextvsMuse GlimmerNemotron 3 SupervsGrok 4.6Qwen3.8-Flash-NextvsGrok 4.6Nemotron 3 SupervsDeepSeek-V4-Pro-0813Qwen3.8-Flash-NextvsDeepSeek-V4-Pro-0813
Frequently asked questions
Qwen3.8-Flash-Next leads Nemotron 3 Super on 1 of the 1 benchmark they both report (GPQA Diamond). Nemotron 3 Super shipped 168 days before Qwen3.8-Flash-Next, so benchmark comparisons should account for the intervening progress.
Nemotron 3 Super has 120B parameters, while Qwen3.8-Flash-Next has 125B. Context windows are 1M (Nemotron 3 Super) vs 262k (Qwen3.8-Flash-Next). Nemotron 3 Super is open source, while Qwen3.8-Flash-Next is open weight.
On GPQA Diamond, Qwen3.8-Flash-Next leads at 91.7% vs Nemotron 3 Super at 79.2%.
Nemotron 3 Super was released by NVIDIA on Mar 11 2026.
Qwen3.8-Flash-Next was released by Qwen on Aug 26 2026.
Qwen3.8-Flash-Next leads on GPQA Diamond — Nemotron 3 Super 79.2% vs Qwen3.8-Flash-Next 91.7%.
Nemotron 3 Super has a 1M context window; Qwen3.8-Flash-Next has 262k.
Nemotron 3 Super is an open source model released by NVIDIA. Qwen3.8-Flash-Next is an open weight model released by Qwen.