MicroLLM lab

Generate max new

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Objective checks (regex / exact tokens), not writing quality. A 135M model is allowed to fail — that is the measurement. Pick models, then run. Estimate uses your last tok/s if we have one.

Run suite on active model Run suite on every loaded model Run sustained speed test (256 tokens)

🏎️ Highest Peak Speed — Fastest single test

⚡ Highest Sustained Speed — Continuous 256-tok decode

📊 Avg Sustained Speed — Across tested models

⏱️ Total Benchmark Score — Cumulative suite wall time

Runtime per test (ms)

Accuracy per test

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Speed (tokens/s, sustained decode, suite wall) and accuracy (pass rate on objective tests) from runs in this browser. Numbers stay on this machine. Charts use the latest suite per model.

🏎️ Highest Peak Speed — Fastest single test

⚡ Highest Sustained Speed — Continuous 256-tok decode

📊 Avg Sustained Speed — Across loaded models

⏱️ Total Benchmark Score — Total execution wall time

Accuracy (latest suite)

Speed (mean tok/s)

Suite wall (ms)

Clear saved comparison

Your Name or Handle

Device & Hardware Information

🎨 Generate Certificate

⬇️ Download Certificate Image (.png) 📋 Copy Image 𝕏 Share on X 💼 Share on LinkedIn 🔗 Copy Text Summary

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Write a benchmark in JavaScript

The editor is `eval()`’d in this origin, then each `check` runs on the model’s decoded text.

Example: object Example: function Eval and run

Prompt for a larger LLM

Copy prompt

Function form

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