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. | 236B | 550B |
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. | — | $0.40DeepInfra |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $1.76DeepInfra |
| — | $210DeepInfra · 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. | — | 49% |
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. | — | 71.9% |
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. | — | 56.4% |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | — | 44.4% |
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. | — | 26.7% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 86.7% |
AA Intelligence IndexOverall intelligence — Artificial Analysis composite intelligence index across evals | — | 48 |
| Overview | ||
| Company | DeepSeek | NVIDIA |
| Release date | Dec 10 2024 | Jun 4 2026 |
| Access | Open Weight | Open Source |
| Model details | View model | View model |
Other comparisons
DeepSeek V2.5 (Revised)vsClaude Haiku 5.5Nemotron 3 UltravsClaude Haiku 5.5DeepSeek V2.5 (Revised)vsGPT-6.1 SolNemotron 3 UltravsGPT-6.1 SolDeepSeek V2.5 (Revised)vsGemini 4 ArgonNemotron 3 UltravsGemini 4 ArgonDeepSeek V2.5 (Revised)vsMuse Spark OpenNemotron 3 UltravsMuse Spark OpenDeepSeek V2.5 (Revised)vsGrok 4.7Nemotron 3 UltravsGrok 4.7DeepSeek V2.5 (Revised)vsMistral Large 4Nemotron 3 UltravsMistral Large 4Frequently asked questions
DeepSeek V2.5 (Revised) and Nemotron 3 Ultra don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. DeepSeek V2.5 (Revised) shipped 541 days before Nemotron 3 Ultra, so benchmark comparisons should account for the intervening progress.
DeepSeek V2.5 (Revised) has 236B parameters, while Nemotron 3 Ultra has 550B. Context windows are 128k (DeepSeek V2.5 (Revised)) vs 1M (Nemotron 3 Ultra). DeepSeek V2.5 (Revised) is open weight, while Nemotron 3 Ultra is open source.
Direct benchmark comparisons are unavailable — DeepSeek V2.5 (Revised) and Nemotron 3 Ultra don't publish scores on any of the same benchmarks.