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 | 400k |
| 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. | — | $0.20 |
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. | — | $1.25 |
Cached input priceA reduced rate for text you send over and over. If every request starts with the same instructions or the same document, the provider keeps a copy ready and charges less to read it again. | — | $0.02 |
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. | $0.10DeepInfra | $0.10OpenAI |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.40DeepInfra | $0.625OpenAI |
| $40.00DeepInfra · fp4 | $103OpenAI list price | |
| These models have no shared benchmark scores. | ||
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. | — | 14% |
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% | — |
Humanity's Last Exam · with toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. | — | 37.7% |
ARC-AGI-2Abstract reasoning — Puzzle-style tests of abstract reasoning and pattern-finding — the kind of thing people find easy but AIs often struggle with. Higher is better. | — | 5.7% |
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% | — |
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. | — | 39% |
| Overview | ||
| Company | NVIDIA | OpenAI |
| Release date | Oct 15 2026 | Mar 17 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Nemotron 3.5 Super VLvsClaude Haiku 5.5GPT-5.4 nanovsClaude Haiku 5.5Nemotron 3.5 Super VLvsGemini 4 ArgonGPT-5.4 nanovsGemini 4 ArgonNemotron 3.5 Super VLvsMuse Spark OpenGPT-5.4 nanovsMuse Spark OpenNemotron 3.5 Super VLvsGrok 4.7GPT-5.4 nanovsGrok 4.7Nemotron 3.5 Super VLvsDeepSeek-V4.1-Flash-1017GPT-5.4 nanovsDeepSeek-V4.1-Flash-1017Nemotron 3.5 Super VLvsMistral Large 4GPT-5.4 nanovsMistral Large 4Frequently asked questions
Nemotron 3.5 Super VL and GPT-5.4 nano don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only GPT-5.4 nano has a verified first-party API price: $0.20 per million input tokens and $1.25 per million output tokens. No pay-as-you-go API rate is tracked for Nemotron 3.5 Super VL. GPT-5.4 nano shipped 212 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 400k (GPT-5.4 nano). Nemotron 3.5 Super VL is open weight, while GPT-5.4 nano is closed.
Direct benchmark comparisons are unavailable — Nemotron 3.5 Super VL and GPT-5.4 nano don't publish scores on any of the same benchmarks.