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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 | Qwen3.8-Flash-Next180BQwen (Alibaba) Performance benchmarkTimebench 3 - Kombi (Prefill + Generation) | 25,97 tok/s TG Prefill 204 · TTFT 110.341 ms | 10× | 2x NVIDIA RTX A6000AMD EPYC 7203P 8-Core Processor | llama.cppgodclawQ4_K_XL | Details → | |
| 2 | Qwen3.8-Flash-Next180BQwen (Alibaba) Performance benchmarkTimebench 3 - Kombi (Prefill + Generation) | 19,36 tok/s TG Prefill 158 · TTFT 64.302 ms | 5× | 2x NVIDIA RTX A6000AMD EPYC 7203P 8-Core Processor | llama.cppgodclawQ4_K_XL | Details → | |
| 3 | Qwen3.8-Flash-Next180BQwen (Alibaba) Performance benchmarkTimebench 3 - Kombi (Prefill + Generation) | 8,95 tok/s TG Prefill 94 · TTFT 24.951 ms | 1× | 2x NVIDIA RTX A6000AMD EPYC 7203P 8-Core Processor | llama.cppgodclawQ4_K_XL | Details → |