Kimi K3.1 Pro Highspeed vs Pro: we measured the real speedup

Description and use

Highspeed speeds token generation 2–3×, but first‑token latency is unchanged. End‑to‑end speedup varies: 1.7× for short answers, 2× for long code answers, 1.25× for large inputs. Cost per request is about double; agent runs cost 1.5× more due to prompt caching.

Problem

Determine whether the higher‑priced Highspeed endpoint delivers a worthwhile speed‑to‑cost advantage over the standard Kimi K3.1 Pro model.

Outcome

Highspeed achieves 2–3× faster token generation but does not reduce first‑token latency. End‑to‑end speedup is 1.7× for short answers, 2× for long code answers, and 1.25× for large inputs. Cost per request is roughly double, though agent runs incur only 1.5× higher cost due to prompt caching.

Workflow

• 1. Send 48 streamed /v1/chat/completions requests (short answer, long code answer, 52K‑token log) to each model, 8 runs per type.
• 2. Measure first‑token time, total time, output tokens/s, and cost per request.
• 3. Run OpenCode 1.19.4 coding agent on six Python tasks twice per model, recording time and cost per run.
• 4. Aggregate results and compute speedup and cost ratios.
• 5. Analyze trade‑offs for interactive vs batch workloads.

Evidence

Highspeed generated text 2 to 3 times faster.

Source

Both models found the root‑cause line in all 16 long‑input runs and quoted it exactly.

Source

Highspeed cost 1.5 times as much, not 2.

Source