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. | 7B | 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. | 4k | 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. | — | $0.08DekaLLM |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $0.40DeepInfra |
| — | $37.00DeepInfra · bf16 | |
| 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. | — | 54% |
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. | — | 60.5% |
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. | — | 45.78% |
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. | — | 81.19% |
Terminal-Bench 2.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? (Version 2.0 of the test.) Higher is better. | — | 31% |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | — | 31.28% |
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 | Feb 5 2024 | Mar 11 2026 |
| Access | Open Weight | Open Source |
| Model details | View model | View model |
Other comparisons
DeepSeek-MathvsClaude Haiku 5.5Nemotron 3 SupervsClaude Haiku 5.5DeepSeek-MathvsGPT-6.1 SolNemotron 3 SupervsGPT-6.1 SolDeepSeek-MathvsGemini 4 ArgonNemotron 3 SupervsGemini 4 ArgonDeepSeek-MathvsMuse Spark OpenNemotron 3 SupervsMuse Spark OpenDeepSeek-MathvsGrok 4.7Nemotron 3 SupervsGrok 4.7DeepSeek-MathvsMistral Large 4Nemotron 3 SupervsMistral Large 4Frequently asked questions
DeepSeek-Math and Nemotron 3 Super don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. DeepSeek-Math shipped 765 days before Nemotron 3 Super, so benchmark comparisons should account for the intervening progress.
DeepSeek-Math has 7B parameters, while Nemotron 3 Super has 120B. Context windows are 4k (DeepSeek-Math) vs 1M (Nemotron 3 Super). DeepSeek-Math is open weight, while Nemotron 3 Super is open source.
Direct benchmark comparisons are unavailable — DeepSeek-Math and Nemotron 3 Super don't publish scores on any of the same benchmarks.