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Foundation Model Benchmark Comparator
Compare speed, memory requirements, and accuracy across state-of-the-art open-weights architectures.
| Metric / Capability | DeepSeek-R1 | Meta-Llama-3.1-70B | Advantage |
|---|---|---|---|
| Parameter Count | 671B MoE (37B active) | 70B Dense | — |
| NVIDIA H100 Throughput | 142.6 tok/s | 165.2 tok/s | Meta-Llama-3.1-70B (+16%) |
| Min VRAM (FP8) | 160 GB | 41.2 GB | Meta-Llama-3.1-70B (Lighter) |
| MMLU General Knowledge | 90.8% | 86% | DeepSeek-R1 (+4.8%) |
| HumanEval Python Coding | 92.4% | 80.5% | DeepSeek-R1 (+11.9%) |
| GSM8K Math Reasoning | 95.8% | 93.2% | DeepSeek-R1 |
| Cost / 1M Tokens (TensorRT-LLM) | $0.55 | $0.42 | Meta-Llama-3.1-70B (Cheaper) |