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 | 8B/49B |
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 | 128k |
| Overview | ||
| Company | DeepSeek | NVIDIA |
| Release date | Feb 5 2024 | Mar 18 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek-MathvsClaude Haiku 5.5Llama Nemotron (Nano/Super)vsClaude Haiku 5.5DeepSeek-MathvsGPT-6.1 SolLlama Nemotron (Nano/Super)vsGPT-6.1 SolDeepSeek-MathvsGemini 4 ArgonLlama Nemotron (Nano/Super)vsGemini 4 ArgonDeepSeek-MathvsMuse Spark OpenLlama Nemotron (Nano/Super)vsMuse Spark OpenDeepSeek-MathvsGrok 4.7Llama Nemotron (Nano/Super)vsGrok 4.7DeepSeek-MathvsMistral Large 4Llama Nemotron (Nano/Super)vsMistral Large 4Frequently asked questions
DeepSeek-Math and Llama Nemotron (Nano/Super) don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. DeepSeek-Math shipped 407 days before Llama Nemotron (Nano/Super), so benchmark comparisons should account for the intervening progress.
DeepSeek-Math has 7B parameters, while Llama Nemotron (Nano/Super) has 8B/49B. Context windows are 4k (DeepSeek-Math) vs 128k (Llama Nemotron (Nano/Super)).
Direct benchmark comparisons are unavailable — DeepSeek-Math and Llama Nemotron (Nano/Super) don't publish scores on any of the same benchmarks.