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 | — |
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 · base tier | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | — | $1.00 |
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. | — | $2.00 |
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. | — | $0.20 |
| — | $300SpaceXSI list price | |
| 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% | 70.8% |
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 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 | SpaceXSI |
| Release date | Oct 15 2026 | Aug 28 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Nemotron 3.5 Super VLvsClaude Haiku 5.5Grok Code Fast 1vsClaude Haiku 5.5Nemotron 3.5 Super VLvsGPT-6.1 SolGrok Code Fast 1vsGPT-6.1 SolNemotron 3.5 Super VLvsGemini 4 ArgonGrok Code Fast 1vsGemini 4 ArgonNemotron 3.5 Super VLvsMuse Spark OpenGrok Code Fast 1vsMuse Spark OpenNemotron 3.5 Super VLvsDeepSeek-V4.1-FlashGrok Code Fast 1vsDeepSeek-V4.1-FlashNemotron 3.5 Super VLvsMistral Large 4Grok Code Fast 1vsMistral Large 4Frequently asked questions
Nemotron 3.5 Super VL leads Grok Code Fast 1 on 1 of the 1 benchmark they both report (SWE-Bench Verified). Only Grok Code Fast 1 has a verified first-party API price: $1.00 per million input tokens and $2.00 per million output tokens. No pay-as-you-go API rate is tracked for Nemotron 3.5 Super VL. Grok Code Fast 1 shipped 413 days before Nemotron 3.5 Super VL, so benchmark comparisons should account for the intervening progress.
Nemotron 3.5 Super VL is open weight, while Grok Code Fast 1 is closed.
On SWE-Bench Verified, Nemotron 3.5 Super VL leads at 71.1% vs Grok Code Fast 1 at 70.8%.