High API relay latency? A guide to low-latency model API access

The short answer: High relay latency usually combines network transfer, server queues and model generation. Enable streaming (stream=true), choose a closer node and use a faster model tier to noticeably reduce time to first token. This guide covers the causes and a measurement script you can use immediately.

Introduction

Last week, a support system owner told developer Zhou: "Customers say the bot is too slow." Logs showed average total request time of only three seconds, yet many users waited eight seconds for the first word and closed the page. Animated dots gave no indication whether the system was stuck or thinking.

The difference comes from time spent in every part of the response path. With a gateway such as Yomi API , latency depends on client configuration, platform routing and model tier. All can be improved. We will cover the sources of latency, five practical fixes, the platform's role and how to measure results.

Key takeaways

Latency = network transfer + server queuing + model generation. Separate them to find the right fix.

Time to first token matters more to perceived speed than total duration: users judge the first visible word.

The first fix is free: add stream=true to the request to return the first token much sooner.

For chat, switch to faster tiers such as deepseek-v4-flash, glm-5.2 for better generation speed and cost.

Platforms can handle intelligent routing and failover. Compare their published 24-hour performance data first.

Run a script 20 times and calculate P50/P95 to evaluate latency with data.

Where does latency come from? Three parts of a request

From sending a request to receiving the result, three stages add to total duration:

Network transfer: packets travel from your server through backbone networks to the model node. Greater distance and more network segments increase round-trip time (RTT).
Queuing: after reaching the platform or model service, requests wait for GPU capacity. Peak traffic lengthens queues and raises latency.
Model generation: the model first reads your prompt during prefill, then generates tokens. Total duration grows with response length, while the first token is strongly affected by the preceding stages.

A key concept is TTFT (time to first token): the interval from sending a request to receiving the first token. In chat, this largely determines perceived speed: a response within two seconds feels smooth, while five seconds of silence invites a page refresh. TTFT shapes the experience of waiting, which is why it matters more than total duration.

Five latency improvements, ranked by value

The first improvement is free. Start there, then use this quick reference:

Method	Implementation	Benefit
Enable streaming	Add `stream=true` to receive the response incrementally	A much earlier first token and the clearest improvement in perceived speed
Choose a closer node	Use global low-latency access and the fastest nearby route	Less network transfer time
Choose a faster model tier	For chat, use `deepseek-v4-flash`, `glm-5.2`	Faster generation and lower costs
Control prompt length and max_tokens	Shorten system prompts and set `max_tokens` according to need	Shorter prefill and generation stages
Set sensible timeouts and retries	Use a 15–30-second timeout and one or two retries with backoff	Absorb occasional network fluctuations

First, enable streaming. Chat products should use it by default. Add stream: true to the request and the server sends each portion as it is generated. Users see progressive output instead of waiting for completion. With the same three-second total duration, seeing the first token at 0.5 seconds feels entirely different.

Second, choose a closer node. Geography strongly affects network cost. Global gateway nodes route requests through closer, more stable paths, reducing RTT and time to first token.

Third, choose a faster model tier. Not every task needs a premium model. For frequent support, translation and summary tasks, use deepseek-v4-flash or glm-5.2 for better speed and cost. For complex reasoning or long documents, move up to claude-sonnet-5 or a higher tier, subject to the current Model Explorer listing.

Fourth, control prompt length and max_tokens. Prefill must read the entire prompt; longer system prompts take longer. Set max_tokens to your needs rather than generating lengthy content you will not use.

Fifth, use sensible timeouts and retries. Occasional network variation is unavoidable; a 15–30-second timeout is a reasonable starting point. One or two retries with backoff can absorb many transient failures.

What can the platform do? Delegate routing and resilience

The earlier changes are under client control. A reliable gateway should continually handle three other tasks:

Intelligent routing: distribute requests across the currently fastest and most reliable routes according to live latency and availability, rather than a fixed path. Routing changes can improve latency without client intervention.
Automatic failover: switch requests to a backup route when a route degrades or fails, keeping your service running.
Multiple access nodes: use the same key at nearby nodes, close to wherever your business is deployed.

How do you evaluate this? Look at data. Yomi API aggregates 200+ models from 30+ providers and publishes each model's 24-hour performance snapshot in Model Explorer, with hourly TTFT and availability updates for clear comparison.Transparency is a key selection criterion: publishing performance data gives a platform an incentive to keep improving routes.

Also check for TLS 1.3 encryption throughout and a commitment not to store request content.

Measurement: a script for P50/P95

Measure instead of guessing. This script sends 20 requests and calculates P50/P95 time to first token in a few minutes. For the OpenAI-compatible API, set base_url to https://api.yomiapi.com/v1:

from openai import OpenAI
import time, statistics

client = OpenAI(
    api_key="sk-YOUR-YOMI-API-KEY",        # Key created in the console
    base_url="https://api.yomiapi.com/v1" # OpenAI-compatible address including /v1
)

ttfts = []
for _ in range(20):
    t0 = time.time()
    resp = client.chat.completions.create(
        model="claude-sonnet-5",          # Check the current Model Explorer listing
        messages=[{"role": "user", "content": "Hello"}],
        stream=True,                      # Stream to measure TTFT
    )
    next(resp)                            # Receive the first chunk
    ttfts.append(time.time() - t0)

ttfts.sort()
p50 = statistics.median(ttfts)
p95 = ttfts[int(len(ttfts) * 0.95) - 1]
print(f"P50: {p50:.2f}s   P95: {p95:.2f}s")

The script uses stream=True and waits only for the first chunk to measure TTFT.P50 describes the typical experience, with half of requests faster; P95 describes the tail, with 95% returning their first token within that time. Compare P50/P95 across models and times of day for more useful evidence than marketing claims.

Final thoughts

For that eight-second support bot, the order is straightforward: enable streaming, check that global low-latency routing removes network delays, then match the model tier to the task. These three steps can bring the first token into the one-to-two-second range and transform perceived speed.

Latency optimization requires continued observation. Record your script's P50/P95 and review trends weekly. Route, model and time-of-day variations appear in the data. Twenty requests are a useful starting test.

Instead of relying on someone else's screenshots, test your own requests: register, get a key, run the script and compare it with the 24-hour performance snapshot. Top up in RMB through WeChat Pay or Alipay, with token billing, separate input/output rates and itemized console records.

Get a key and measure your TTFT →