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Rankings
LLM Leaderboard
Real measurements of every tested language model on real, named hardware – rated by raw speed (performance in tokens per second, prefill and time to first token) and by practical task quality in complete agent and chat runs (harness). Pick a benchmark type below or filter by model, maker and hardware to see exactly what is tested and how the results are produced.
⚡ Performance (tok/s)🤖 Harness quality👥 Concurrency🖥️ real hardware
| # | Model / Maker | Metrics | Parallel | GPU / CPU / RAM | Runtime | ||
|---|---|---|---|---|---|---|---|
| 1 | MiniMax-M2.7MiniMax PerformancebenchmarkTimebench 3 - Kombi (Prefill + Generation) | 2,62 tok/s TG Prefill 15 · TTFT 156.050 ms | 1× | Keine GPU (CPU-only)51x Intel(R) Xeon(R) CPU E5-4620 v2 @ 2.60GHz · 504 GB RAM | llama.cppgodclawUD-Q4_K_M | Details → | |
| 2 | MiniMax-M2.7MiniMax PerformancebenchmarkTimebench 3 - Kombi (Prefill + Generation) | 0,04 tok/s TG Prefill 29 · TTFT 211.132 ms | 5× | Keine GPU (CPU-only)51x Intel(R) Xeon(R) CPU E5-4620 v2 @ 2.60GHz · 504 GB RAM | llama.cppgodclawUD-Q4_K_M | Details → | |
| 3 | MiniMax-M2.7MiniMax PerformancebenchmarkTimebench 3 - Kombi (Prefill + Generation) | 0,03 tok/s TG Prefill 28 · TTFT 212.753 ms | 10× | Keine GPU (CPU-only)51x Intel(R) Xeon(R) CPU E5-4620 v2 @ 2.60GHz · 504 GB RAM | llama.cppgodclawUD-Q4_K_M | Details → |