Every local-LLM benchmark reports throughput: "47 tok/s on an M3," "180 tok/s on a 4090," "500 tok/s on Groq." Unless you've actually watched tokens stream at those rates, the numbers are hard to internalize. This is the rendering.
Four modes
- code — syntax-highlighted pseudo-code, the most common thing you watch stream out of an LLM.
- text — lorem ipsum prose, for the chat/answer case.
- think — dim-italic reasoning sentences alternating with code, mimicking a reasoning model thinking out loud.
- agent — alternating tool calls and code generation with processing pauses, simulating an AI coding agent.
What to try
Start at the default 30 and read along. Then hit 1 (5 tok/s — Raspberry-Pi-class local model), 5 (60 tok/s — typical hosted Claude or GPT), 7 (200 tok/s — Groq territory), 9 (800 tok/s — Cerebras-class, where the bottleneck is your eyeballs).
Now switch between c and t at the same rate. The difference is striking — and intentional.
Prompt processing
Before a model emits a single output token, it has to read your whole prompt — the prefill pass. Open p, pick a context size, and the tool makes you sit through that wait before streaming starts, the same way it makes you feel tok/s. Prefill is much faster per token than generation, but a long context still stalls you: 64k tokens at 1,000 tok/s of prefill is over a minute of nothing. That delay is time-to-first-token, and it's the half of the latency story a throughput number never shows.
What counts as a token
This approximates BPE-style tokenization, not any vendor-specific encoder
(tiktoken, Claude's tokenizer, etc. — those disagree in the
details anyway).
Short words are often one token; longer identifiers split into chunks
(processUserInput → process + User + Input);
punctuation and operators usually count too.
Code is more token-dense than prose, so the same tok/s can feel very different depending on what's streaming. The benchmark number is honest; the perceptual effect varies a lot by content type — which is the gap this tool exists to expose.
English prose averages ~1.3 tokens per word, so 30 tok/s ≈ 23 words/s.