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Find LLM tokens-per-second (TPS), latency, and throughput metrics. If direct API benchmarking is impossible, pivot to published third-party benchmarks and provide a manual fallback script.
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Based on SOC occupation classification
Run AI coding agents on disposable repo clones. The agent works on a clone — it can't touch your real repo. An optional container (Podman or Docker) provides build/test isolation. You review the diff and decide what (if anything) to apply.
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| name | llm-performance-benchmark |
| description | Find LLM tokens-per-second (TPS), latency, and throughput metrics. If direct API benchmarking is impossible, pivot to published third-party benchmarks and provide a manual fallback script. |
| trigger | User asks for model speed, TPS, tokens per second, throughput, latency, or performance benchmark of a specific LLM. |
If direct measurement is impossible, immediately search for third-party benchmark data.
| Site | What it tracks | URL pattern |
|---|---|---|
| Artificial Analysis | Provider-level TPS, TTFT, price | artificialanalysis.ai/models/<model>/providers |
| DeepInfra Blog | Provider shootouts (Groq, Fireworks, etc.) | deepinfra.com/blog/<model>-api-benchmarks |
| LLM-Stats | Leaderboard rankings, quality scores | llm-stats.com/models/<model> |
"<model-name>" tokens per second benchmark throughput
"<model-name>" API provider benchmark artificialanalysis
kimi-k2.6 is the current flagship; kimi-k2-0905 / kimi-k2-0711 are earlier K2 series variants. Do not conflate them.Present:
Always give the user a copy-paste script they can run with their own key.