deepseek-v4-flash vs kimi-for-coding speed comparison
Based on 130 anonymous user runs.
Post to social channels, or use Markdown and badges for GitHub/README.
[](https://tokrace.com/en/compare/deepseek-v4-flash-vs-kimi-for-coding)
· 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.
FAQ
Which model outputs faster, deepseek-v4-flash or kimi-for-coding?
deepseek-v4-flash has faster output (median 142 vs 104 tok/s); deepseek-v4-flash has faster TTFT (0.71s vs 1.41s).
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://tokrace.com/api/badge/compare/deepseek-v4-flash-vs-kimi-for-coding?locale=en.