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. | 671B | 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. | 128k | 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. | $0.259SiliconFlow | $0.10DeepInfra |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.38AtlasCloud | $0.40DeepInfra |
| $71.00DeepInfra · fp4 | $40.00DeepInfra · fp4 | |
| 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. | 13% | — |
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 | DeepSeek | NVIDIA |
| Release date | Dec 1 2025 | Oct 15 2026 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek-V3.2vsClaude Haiku 5.5Nemotron 3.5 Super VLvsClaude Haiku 5.5DeepSeek-V3.2vsGPT-6.1 SolNemotron 3.5 Super VLvsGPT-6.1 SolDeepSeek-V3.2vsGemini 4 ArgonNemotron 3.5 Super VLvsGemini 4 ArgonDeepSeek-V3.2vsMuse Spark OpenNemotron 3.5 Super VLvsMuse Spark OpenDeepSeek-V3.2vsGrok 4.7Nemotron 3.5 Super VLvsGrok 4.7DeepSeek-V3.2vsMistral Large 4Nemotron 3.5 Super VLvsMistral Large 4Frequently asked questions
DeepSeek-V3.2 and Nemotron 3.5 Super VL don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. DeepSeek-V3.2 shipped 318 days before Nemotron 3.5 Super VL, so benchmark comparisons should account for the intervening progress.
DeepSeek-V3.2 has 671B parameters, while Nemotron 3.5 Super VL has 120B. Context windows are 128k (DeepSeek-V3.2) vs 1M (Nemotron 3.5 Super VL).
Direct benchmark comparisons are unavailable — DeepSeek-V3.2 and Nemotron 3.5 Super VL don't publish scores on any of the same benchmarks.