Compare AI models
| Specifications | ||
ParametersThe learned values that shape how a model responds. B means billion and T means trillion. For mixture-of-experts models, the total count is larger than the number active for each token. Parameter count alone does not measure quality. | 120B | — |
Context windowHow much information the model can work with in one request, measured in tokens — pieces of words or other data. This includes your question, attachments, conversation and reply. Limits can vary by model variant and provider. | 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. | $0.10Crusoe | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.40Crusoe | — |
| $40.00Crusoe · bf16 | — | |
| These models have no shared benchmark scores. | ||
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 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% | — |
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% | — |
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% | — |
IFBenchInstruction following — Tests whether the AI can follow detailed instructions and satisfy multiple constraints at once. Higher is better. | 70.86% | — |
| Overview | ||
| Company | NVIDIA | OpenAI |
| Release date | Oct 15 2026 | Feb 27 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Nemotron 3.5 Super VLvsClaude Haiku 5.5GPT-4.5vsClaude Haiku 5.5Nemotron 3.5 Super VLvsGemini 4 ArgonGPT-4.5vsGemini 4 ArgonNemotron 3.5 Super VLvsMuse Spark OpenGPT-4.5vsMuse Spark OpenNemotron 3.5 Super VLvsGrok 4.7GPT-4.5vsGrok 4.7Nemotron 3.5 Super VLvsDeepSeek-V4.1-Flash-1017GPT-4.5vsDeepSeek-V4.1-Flash-1017Nemotron 3.5 Super VLvsMistral Large 4GPT-4.5vsMistral Large 4Frequently asked questions
Nemotron 3.5 Super VL and GPT-4.5 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. GPT-4.5 shipped 595 days before Nemotron 3.5 Super VL, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Nemotron 3.5 Super VL) vs 128k (GPT-4.5). Nemotron 3.5 Super VL is open weight, while GPT-4.5 is closed.
Direct benchmark comparisons are unavailable — Nemotron 3.5 Super VL and GPT-4.5 don't publish scores on any of the same benchmarks.