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 | 397B |
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 | 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. | — | $0.60 |
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. | — | $3.60 |
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. | — | $0.065Alibaba |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $0.15DeepInfra |
| — | $26.00Alibaba | |
| 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% | 76.4% |
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% | 69.3% |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | 78% | 69% |
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% | 88.4% |
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. | — | 78% |
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% | — |
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% | — |
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. | — | 52.5% |
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. | — | 28.7% |
IFBenchInstruction following — Tests whether the AI can follow detailed instructions and satisfy multiple constraints at once. Higher is better. | 70.86% | — |
OSWorld-VerifiedAgentic computer use — Can the AI actually operate a computer — clicking, typing, and using real apps — to finish tasks on its own? Higher is better. | — | 62.2% |
CharXiv ReasoningChart reasoning — Can the AI read and reason about complex charts and figures, not just text? Higher is better. | — | 80.8% |
MMMU-ProMultimodal reasoning — A tougher version of MMMU — college-level questions that mix images, diagrams, and text together. Higher is better. | — | 79% |
MMMUMultimodal — Tests the AI on understanding images and text together across many college subjects. Higher is better. | — | 85% |
| Overview | ||
| Company | NVIDIA | Qwen |
| Release date | Oct 15 2026 | Feb 16 2026 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
Nemotron 3.5 Super VLvsClaude Haiku 5.5Qwen3.5vsClaude Haiku 5.5Nemotron 3.5 Super VLvsGPT-6.1 SolQwen3.5vsGPT-6.1 SolNemotron 3.5 Super VLvsGemini 4 ArgonQwen3.5vsGemini 4 ArgonNemotron 3.5 Super VLvsMuse Spark OpenQwen3.5vsMuse Spark OpenNemotron 3.5 Super VLvsGrok 4.7Qwen3.5vsGrok 4.7Nemotron 3.5 Super VLvsDeepSeek-V4.1-FlashQwen3.5vsDeepSeek-V4.1-FlashFrequently asked questions
Nemotron 3.5 Super VL and Qwen3.5 are evenly matched across the 4 benchmarks they both report (SWE-Bench Verified, SWE-Bench Multilingual, BrowseComp, GPQA Diamond). Only Qwen3.5 has a verified first-party API price: $0.60 per million input tokens and $3.60 per million output tokens. No pay-as-you-go API rate is tracked for Nemotron 3.5 Super VL. Qwen3.5 shipped 241 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 Qwen3.5 has 397B. Context windows are 1M (Nemotron 3.5 Super VL) vs 1M (Qwen3.5).
On SWE-Bench Verified, Qwen3.5 leads at 76.4% vs Nemotron 3.5 Super VL at 71.1%. On SWE-Bench Multilingual, Qwen3.5 leads at 69.3% vs Nemotron 3.5 Super VL at 67.72%. On BrowseComp, Nemotron 3.5 Super VL leads at 78% vs Qwen3.5 at 69%. On GPQA Diamond, Nemotron 3.5 Super VL leads at 89.52% vs Qwen3.5 at 88.4%.