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. | $0.045OpenInference | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.1652StreamLake | — |
| $24.78StreamLake · fp8 | — | |
| Benchmarks | ||
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. | 91.6% | 88.22% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 88.1% | 89.52% |
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. | 18% | — |
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% |
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% |
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 | Apr 24 2026 | Oct 15 2026 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek-V4-FlashvsClaude Haiku 5.5Nemotron 3.5 Super VLvsClaude Haiku 5.5DeepSeek-V4-FlashvsGPT-6.1 SolNemotron 3.5 Super VLvsGPT-6.1 SolDeepSeek-V4-FlashvsGemini 4 ArgonNemotron 3.5 Super VLvsGemini 4 ArgonDeepSeek-V4-FlashvsMuse Spark OpenNemotron 3.5 Super VLvsMuse Spark OpenDeepSeek-V4-FlashvsGrok 4.7Nemotron 3.5 Super VLvsGrok 4.7DeepSeek-V4-FlashvsMistral Large 4Nemotron 3.5 Super VLvsMistral Large 4Frequently asked questions
DeepSeek-V4-Flash and Nemotron 3.5 Super VL are evenly matched across the 2 benchmarks they both report (LiveCodeBench, GPQA Diamond). DeepSeek-V4-Flash shipped 174 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 LiveCodeBench, DeepSeek-V4-Flash leads at 91.6% vs Nemotron 3.5 Super VL at 88.22%. On GPQA Diamond, Nemotron 3.5 Super VL leads at 89.52% vs DeepSeek-V4-Flash at 88.1%.