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 | ||
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. | $0.25DeepInfra | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.95DeepInfra | — |
| $97.50DeepInfra · fp4 | — | |
| 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. | 66% | 71.1% |
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. | 74.8% | 88.22% |
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% |
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. | 15.9% | — |
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 | DeepSeek | NVIDIA |
| Release date | Aug 21 2025 | Oct 15 2026 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek V3.1vsClaude Haiku 5.5Nemotron 3.5 Super VLvsClaude Haiku 5.5DeepSeek V3.1vsGPT-6.1 SolNemotron 3.5 Super VLvsGPT-6.1 SolDeepSeek V3.1vsGemini 4 ArgonNemotron 3.5 Super VLvsGemini 4 ArgonDeepSeek V3.1vsMuse Spark OpenNemotron 3.5 Super VLvsMuse Spark OpenDeepSeek V3.1vsGrok 4.7Nemotron 3.5 Super VLvsGrok 4.7DeepSeek V3.1vsMistral Large 4Nemotron 3.5 Super VLvsMistral Large 4Frequently asked questions
Nemotron 3.5 Super VL leads DeepSeek V3.1 on 2 of the 2 benchmarks they both report (SWE-Bench Verified, LiveCodeBench). DeepSeek V3.1 shipped 420 days before Nemotron 3.5 Super VL, so benchmark comparisons should account for the intervening progress.
Published specifications for these two models are limited — see each model page for the latest details.
On SWE-Bench Verified, Nemotron 3.5 Super VL leads at 71.1% vs DeepSeek V3.1 at 66%. On LiveCodeBench, Nemotron 3.5 Super VL leads at 88.22% vs DeepSeek V3.1 at 74.8%.