glm-5.1 vs kimi-for-coding-highspeed speed comparison

Based on 52 anonymous user runs.

Verdict: kimi-for-coding-highspeed has faster output (median 286 vs 53 tok/s); kimi-for-coding-highspeed has faster TTFT (1.28s vs 4.70s).
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[![kimi-for-coding-highspeed is faster than glm-5.1: 286 tok/s on TOKRACE](https://www.tokrace.com/api/badge/compare/glm-5-1-vs-kimi-for-coding-highspeed?locale=en)](https://www.tokrace.com/en/compare/glm-5-1-vs-kimi-for-coding-highspeed)
Median output tok/s53286
Average output tok/s61286
TTFT4.70s1.28s
Peak tok/s594467
Samples511

· Data comes from voluntary anonymous sharing; medians reduce jitter · Updates every 5 minutes

· Speed is affected by network, time of day and provider load · Methodology

How to use this comparison

Writing/long output: Prioritize median output tok/s and peak speed.

Chat/agents: TTFT usually has a bigger UX impact.

Model selection: Rerun your real Prompt and inspect output quality too.

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FAQ

Which model outputs faster, glm-5.1 or kimi-for-coding-highspeed?

kimi-for-coding-highspeed has faster output (median 286 vs 53 tok/s); kimi-for-coding-highspeed has faster TTFT (1.28s vs 4.70s).

Why can output speed and TTFT have different winners?

Output tok/s measures sustained generation speed, while TTFT measures the wait until the first token. A model can generate long text faster while still taking longer to start.

How should I rerun this comparison?

Use the arena with the same Prompt, temperature and network conditions, then repeat a few times and combine the speed data with output quality.

Can I embed this comparison in GitHub or an article?

Yes. This page provides Markdown and HTML badges. The badge image URL is https://www.tokrace.com/api/badge/compare/glm-5-1-vs-kimi-for-coding-highspeed?locale=en.