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 | 253B |
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 |
| These models have no shared benchmark scores. | ||
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. | — | 66.3% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 76% |
| Overview | ||
| Company | DeepSeek | NVIDIA |
| Release date | Feb 5 2024 | Apr 8 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
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DeepSeek-Math and Llama Nemotron Ultra 253B don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. DeepSeek-Math shipped 428 days before Llama Nemotron Ultra 253B, so benchmark comparisons should account for the intervening progress.
DeepSeek-Math has 7B parameters, while Llama Nemotron Ultra 253B has 253B. Context windows are 4k (DeepSeek-Math) vs 128k (Llama Nemotron Ultra 253B).
Direct benchmark comparisons are unavailable — DeepSeek-Math and Llama Nemotron Ultra 253B don't publish scores on any of the same benchmarks.