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 third-party providers, excluding the lab itself. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | $0.25DeepInfra | $0.08DekaLLM |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $0.95DeepInfra | $0.40DeepInfra |
| 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% | 60.5% |
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. | — | 54% |
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% | — |
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. | — | 79.2% |
AA Intelligence IndexOverall intelligence — Artificial Analysis composite intelligence index across evals | — | 36 |
| Overview | ||
| Company | DeepSeek | NVIDIA |
| Release date | Aug 21 2025 | Mar 11 2026 |
| Access | Open Weight | Open Source |
| Model details | View model | View model |
Other comparisons
DeepSeek V3.1vsClaude Haiku 5.5Nemotron 3 SupervsClaude Haiku 5.5DeepSeek V3.1vsGPT-6.1 SolNemotron 3 SupervsGPT-6.1 SolDeepSeek V3.1vsGemini 4 ArgonNemotron 3 SupervsGemini 4 ArgonDeepSeek V3.1vsMuse Spark 1.3Nemotron 3 SupervsMuse Spark 1.3DeepSeek V3.1vsGrok 4.7Nemotron 3 SupervsGrok 4.7DeepSeek V3.1vsMistral Large 4Nemotron 3 SupervsMistral Large 4Frequently asked questions
DeepSeek V3.1 leads Nemotron 3 Super on 1 of the 1 benchmark they both report (SWE-Bench Verified). DeepSeek V3.1 shipped 202 days before Nemotron 3 Super, so benchmark comparisons should account for the intervening progress.
DeepSeek V3.1 is open weight, while Nemotron 3 Super is open source.
On SWE-Bench Verified, DeepSeek V3.1 leads at 66% vs Nemotron 3 Super at 60.5%.