Nemotron 3 SupervsQwen3.7-Max
Nemotron 3 Super | Qwen3.7-Max | |
|---|---|---|
| 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. | — | $2.50 |
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. | — | $7.50 |
| 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% | 92.4% |
| BenchmarksPublished by one model only | ||
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. | — | 71% |
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. | — | 60.6% |
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% | — |
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. | — | 91.6% |
Terminal-Bench 2.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? (Version 2.0 of the test.) Higher is better. | — | 69.7% |
MCP AtlasMulti-step tool use — Can the AI chain together many tools and steps to complete one bigger task, rather than doing just a single thing? Higher is better. | — | 76.4% |
AA Intelligence IndexOverall intelligence — Artificial Analysis composite intelligence index across evals | 36 | — |
| Overview | ||
| Company | NVIDIA | Qwen |
| Release date | Mar 11 2026 | May 20 2026 |
| Access | Open Source | Proprietary |
Other comparisons
Frequently asked questions
Qwen3.7-Max leads Nemotron 3 Super on 1 of the 1 benchmark they both report (GPQA Diamond). Only Qwen3.7-Max has a verified first-party API price: $2.50 per million input tokens and $7.50 per million output tokens. No pay-as-you-go API rate is tracked for Nemotron 3 Super. Nemotron 3 Super shipped 70 days before Qwen3.7-Max, so benchmark comparisons should account for the intervening progress.
Nemotron 3 Super is open source, while Qwen3.7-Max is proprietary.
On GPQA Diamond, Qwen3.7-Max leads at 92.4% vs Nemotron 3 Super at 79.2%.
Nemotron 3 Super was released by NVIDIA on Mar 11 2026.
Qwen3.7-Max was released by Qwen on May 20 2026.
Qwen3.7-Max leads on GPQA Diamond — Nemotron 3 Super 79.2% vs Qwen3.7-Max 92.4%.
Only Qwen3.7-Max has a verified first-party API price: $2.50 per million input tokens and $7.50 per million output tokens. No pay-as-you-go API rate is tracked for Nemotron 3 Super. Rates are pay-as-you-go API prices verified on August 18, 2026.
Nemotron 3 Super is an open source model released by NVIDIA. Qwen3.7-Max is a proprietary model released by Qwen.