What Affects LLM API Latency? 6 Factors (2026)

LLM API latency is driven by six measurable factors: model class (reasoning overhead), routing hops, decode speed, output length, network region, and server load. In our 2026-07-26 streaming benchmark these swung the wait for first visible text from 0.71s to 13.9s.

Latency is not one number. When you call a chat model, part of the delay happens before the first token appears, and the rest depends on how fast tokens stream after that. This page explains the causes and trade-offs behind those delays, anchored where possible in first-hand data. It is not a tuning tutorial. All figures below come from a single-prompt, single-region, three-run median snapshot taken on 2026-07-26. DeepSeek V4-Flash was measured natively on api.deepseek.com; every other endpoint was OpenRouter-routed, so those numbers carry a routing hop and need native re-verification.

Time to first token (TTFT) is the latency metric that measures the gap between sending a request and receiving the very first streamed token of any kind, including hidden reasoning tokens. It is distinct from time to first content token (TTFCT), the moment a user actually sees usable text, which matters far more for chat interfaces.

What affects LLM API latency (verified 2026-07)

Read this as a snapshot, not a load test. Every routed figure includes an aggregator hop; only the DeepSeek native row reflects a vendor-direct call. Full run-by-run data lives in the cluster evidence pack.

First-hand: what we streamed on 2026-07-26

We measured seven endpoints with three SSE-streaming runs each, one fixed prompt asking for ten numbered HTTP facts, temperature 0, max_tokens 4000, and reported the medians. All 21 runs succeeded. DeepSeek V4-Flash returned its first token in a median 0.71s natively; the same DeepSeek family routed through an aggregator returned in 2.54s. GPT-4o led decode throughput at 174 tokens per second, while Kimi K2 trailed at 39.4. GLM-5 streamed a first token in 2.5s but did not surface usable content until 13.89s because it emitted a median 807 reasoning tokens first. These are first-hand numbers from one prompt, one region, one moment; treat routed values as needing native re-verification.

Factor 1: Model class and reasoning overhead

The single biggest swing in our data came from whether a model thinks before it writes. GLM-5 and MiniMax-M2.5 behaved as reasoning models on this prompt: their raw TTFT was fast (2.5s and 1.5s), but the first token a user could read arrived at 13.89s and 10.68s respectively, after 807 and 449 median reasoning tokens streamed first.

This is a fit question, not a quality knock. Reasoning overhead is a latency tax on simple generation like list formatting, and it can be entirely worth paying on hard multi-step problems where the extra deliberation changes the outcome. The same model that adds a 13-second delay to a trivial task may be the right tool for a planning-heavy one. Note also that trace depth varies with the prompt and configuration; a shorter or non-streaming task can look very different.

Factor 2: Routing hops (native vs aggregator)

An aggregator gateway is infrastructure that sits between your app and the model provider, adding a network hop and sometimes a different physical host. In our runs that hop was expensive: DeepSeek native returned first tokens in 0.71s at 95.8 tok/s, while the routed DeepSeek path returned in 2.54s at 39.5 tok/s. Routing added roughly 1.8s to first token and about halved decode throughput.

One caveat matters here. The routed model may alias a different DeepSeek build than the native V4-Flash endpoint, and it may land on a different host, so treat this as a routing-overhead signal rather than a perfect A/B of identical builds. Even with that caveat, the direction is clear: a vendor-direct call removed a measurable slice of latency in every run.

Factor 3: Decode speed

Once content starts flowing, decode rate governs how fast the rest arrives. Our measured spread ran from 39.4 tokens per second (Kimi K2, routed) to 174.0 (GPT-4o, the Western baseline). GPT-4o won decode throughput outright; no Chinese endpoint matched it on tokens per second in this run. Among Chinese endpoints, DeepSeek V4-Flash native was fastest at 95.8 tok/s.

According to OpenAI's API reference, responses can be streamed token by token as server-sent events, which is exactly the mechanism our benchmark timed. Decode speed depends on the serving stack, hardware, and batch pressure, so it is not a fixed property of a model name; it is a property of how and where that model is hosted at request time.

Factor 4: Output length

Total wall time follows a simple relationship: total is roughly TTFT plus (output tokens divided by decode rate). A short reply hides a slow decode rate, while a long reply amplifies it. Qwen3-235B and DeepSeek routed produced similar total times (9.73s and 9.68s) despite different first-token latencies, because output length and decode rate combined to a similar sum.

This is why a single latency headline misleads. According to DeepSeek's API pricing documentation, output tokens are billed separately from input, which means longer generations cost more on both latency and price axes at once. If your workload caps output length, decode-rate differences shrink; if it streams long answers, they dominate.

Factor 5: Network and region

We measured from one network location, so region effects are framed generally rather than quantified here. Physical distance between your client and the serving datacenter adds round-trip time to every request, and cross-border routes to China-hosted endpoints can add tens to hundreds of milliseconds that our single-region snapshot did not isolate. According to MiniMax's open platform documentation, the API is served from provider infrastructure whose geography you do not control from the client side, which is precisely why region belongs on this list even though we did not vary it.

Factor 6: Server load and time of day

Load and time-of-day effects are widely reported but we did not measure them, so this is a hedge, not a finding. Shared inference endpoints batch concurrent requests, and heavier batch pressure at peak hours can raise both TTFT and decode variance. Our three-run medians smoothed some noise, but a genuine load test across hours would be needed to quantify it. Qwen3-235B illustrates the caution: it posted the slowest TTFT in our set at 3.16s despite being one of the cheapest options in a separate task benchmark, a reminder that cheap and fast are different axes.

For the full ranked leaderboard and per-model verdicts, see the hub: the fastest Chinese LLM API benchmark. For a head-to-head on the Western baseline, see Chinese vs Western LLM latency.

FAQ

What is the difference between TTFT and total latency? TTFT is the wait for the first streamed token; total latency is TTFT plus the time to decode every remaining token. A model can win TTFT and lose total wall time if its decode rate is slow or its output is long.

Why does a reasoning model feel slow even with a fast TTFT? Reasoning models stream hidden thinking tokens first. In our 2026-07-26 runs GLM-5 hit a first token at 2.5s but did not show usable content until 13.89s, because 807 reasoning tokens streamed before any user-visible text.

Does using an aggregator always add latency? In our single-region snapshot, routing DeepSeek through an aggregator added roughly 1.8s to first token and halved decode throughput versus the native api.deepseek.com call. The routed path may hit a different build or host, so treat it as a routing-overhead signal, not a fixed penalty.

Which model had the fastest first token? DeepSeek V4-Flash measured natively on api.deepseek.com was fastest overall at a median 0.71s TTFT. Among routed Chinese endpoints, Kimi K2 was fastest to first token at 2.27s in the same run.

Did you measure server load or time-of-day effects? No. Those effects are commonly reported but our benchmark was a three-run median from one moment and one region, not a load test. We flag them as plausible causes without quantifying them.

This page is one micro in the speed cluster; the fastest Chinese LLM API hub collects the full benchmark and every sibling verdict.

Author: Kevin Fan, Customer Success Manager