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 | 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. | 128k | 128k |
| Overview | ||
| Company | DeepSeek | NVIDIA |
| Release date | May 6 2024 | Mar 18 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek V2vsClaude Haiku 5.5Llama Nemotron (Nano/Super)vsClaude Haiku 5.5DeepSeek V2vsGPT-6.1 SolLlama Nemotron (Nano/Super)vsGPT-6.1 SolDeepSeek V2vsGemini 4 ArgonLlama Nemotron (Nano/Super)vsGemini 4 ArgonDeepSeek V2vsMuse Spark OpenLlama Nemotron (Nano/Super)vsMuse Spark OpenDeepSeek V2vsGrok 4.7Llama Nemotron (Nano/Super)vsGrok 4.7DeepSeek V2vsMistral Large 4Llama Nemotron (Nano/Super)vsMistral Large 4Frequently asked questions
DeepSeek V2 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 V2 shipped 316 days before Llama Nemotron (Nano/Super), so benchmark comparisons should account for the intervening progress.
DeepSeek V2 has 236B parameters, while Llama Nemotron (Nano/Super) has 8B/49B. Context windows are 128k (DeepSeek V2) vs 128k (Llama Nemotron (Nano/Super)).
Direct benchmark comparisons are unavailable — DeepSeek V2 and Llama Nemotron (Nano/Super) don't publish scores on any of the same benchmarks.