Llama Nemotron Ultra 253BvsQwen3.7-Max
Llama Nemotron Ultra 253B | 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. | 253B | — |
| 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 | ||
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. | 66.3% | 91.6% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 76% | 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% |
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% |
| Overview | ||
| Company | NVIDIA | Qwen |
| Release date | Apr 8 2025 | May 20 2026 |
| Access | Open Weight | Proprietary |
Other comparisons
Frequently asked questions
Qwen3.7-Max leads Llama Nemotron Ultra 253B on 2 of the 2 benchmarks they both report (LiveCodeBench, 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 Llama Nemotron Ultra 253B. Llama Nemotron Ultra 253B shipped 407 days before Qwen3.7-Max, so benchmark comparisons should account for the intervening progress.
Llama Nemotron Ultra 253B is open weight, while Qwen3.7-Max is proprietary.
On LiveCodeBench, Qwen3.7-Max leads at 91.6% vs Llama Nemotron Ultra 253B at 66.3%. On GPQA Diamond, Qwen3.7-Max leads at 92.4% vs Llama Nemotron Ultra 253B at 76%.
Llama Nemotron Ultra 253B was released by NVIDIA on Apr 8 2025.
Qwen3.7-Max was released by Qwen on May 20 2026.
Qwen3.7-Max leads on LiveCodeBench — Llama Nemotron Ultra 253B 66.3% vs Qwen3.7-Max 91.6%.
Qwen3.7-Max leads on GPQA Diamond — Llama Nemotron Ultra 253B 76% 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 Llama Nemotron Ultra 253B. Rates are pay-as-you-go API prices verified on August 18, 2026.
Llama Nemotron Ultra 253B is an open weight model released by NVIDIA. Qwen3.7-Max is a proprietary model released by Qwen.