deepseek-chat vs z-ai/glm-5.3-flash-free speed comparison

Based on 3 anonymous user runs.

Verdict: z-ai/glm-5.3-flash-free has faster output (median 142 vs 105 tok/s); deepseek-chat has faster TTFT (0.57s vs 1.51s).
Share and embed

Post to social channels, or use Markdown and badges for GitHub/README.

XFacebook微博LinkedIn
[![z-ai/glm-5.3-flash-free is faster than deepseek-chat: 142 tok/s on TOKRACE](https://www.tokrace.com/api/badge/compare/deepseek-chat-vs-z-ai-glm-5-3-flash-free?locale=en)](https://www.tokrace.com/en/compare/deepseek-chat-vs-z-ai-glm-5-3-flash-free)
Median output tok/s105142
Average output tok/s105142
TTFT0.57s1.51s
Peak tok/s113856
Samples12

· 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.

Run with current dataView full leaderboard

FAQ

Which model outputs faster, deepseek-chat or z-ai/glm-5.3-flash-free?

z-ai/glm-5.3-flash-free has faster output (median 142 vs 105 tok/s); deepseek-chat has faster TTFT (0.57s vs 1.51s).

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/deepseek-chat-vs-z-ai-glm-5-3-flash-free?locale=en.