Skip to content
LLM Abacus
Two probe locations · refreshed every 10 minutes913 samples in the last 24 hours · data through the hour beginning Aug 2, 08:00 UTC

LLM API Latency Benchmark

Independent probes measure time to first token (TTFT) and output speed (tokens per second) for each API endpoint on a continuous schedule. For prices, see the model pricing table.

Where it is measured from: 2 locations, reported separately

🇭🇰 Hong Kong, 🇺🇸 US Central (Chicago). The two columns are measured independently and never averaged together. The same endpoint routinely differs several-fold between the two, and an average of those would be a number nobody could use. The Hong Kong probe measures international and cross-border routes, which is not the same as a connection originating inside mainland China. Calls to Chinese models from the US probe cross the Pacific. There is no mainland probe yet; if one is added it will be labelled separately.

How it is measured

TTFT uses a streaming request with max_tokens between 8 and 16, recording when the first token arrives. Output speed caps generation at 256 tokens and divides the provider’s own completion_tokens by elapsed time. Reasoning mode is explicitly disabled for both, because chain-of-thought tokens count toward completion_tokens and ignore max_tokens (one model returned 896 tokens for a 256 token request). Leaving it on would compare thinking speed against answering speed. The cost is coverage: Gemini 3.x refuses to disable reasoning across the whole family, so this board uses Gemini 2.5 Flash instead. Newer flagships increasingly force reasoning on, so this comparable set will narrow rather than widen.

What this table does not do

It does not rank which provider is best. Latency is one dimension of a choice, and samples from two locations cannot represent worldwide experience. Sample counts are in the table, so you can judge for yourself how much weight a row carries.

Model & providerEndpoint typePrice (per 1M)
🇭🇰hk
Hong Kong
🇺🇸us-central
US Central (Chicago)
DeepSeek V4 Pro
DeepSeek Official
Direct
In: ¥3.00
Out: ¥6.00
152 ms
36.7 t/s · p95: 182ms
312 ms
37.0 t/s · p95: 318ms
DeepSeek V4 Flash
DeepSeek Official
Direct
In: ¥1.00
Out: ¥2.00
157 ms
60.9 t/s · p95: 193ms
608 ms
48.6 t/s · p95: 672ms
Qwen3.5 Flash
Alibaba Model Studio
Direct
In: ¥0.20
Out: ¥2.00
430 ms
100.5 t/s · p95: 844ms
1381 ms
74.7 t/s · p95: 1767ms
Qwen3 Max
Alibaba Model Studio
Direct
In: ¥2.50
Out: ¥10.00
585 ms
20.4 t/s · p95: 7013ms
662 ms
21.8 t/s · p95: 788ms
Qwen3.7 Max
Alibaba Model Studio
Direct
In: ¥12.00
Out: ¥36.00
628 ms
TPS not measured this hour · p95: 20934ms
610 ms
TPS not measured this hour · p95: 1214ms
Qwen3.7 Max
OpenRouter(Relay)
Relay
In: $1.48
Out: $4.42
708 ms
TPS not measured this hour · p95: 1202ms
1148 ms
TPS not measured this hour · p95: 1614ms
GLM-5.2
OpenRouter(Relay)
Relay
In: $1.12
Out: $3.52
797 ms
TPS not measured this hour · p95: 1257ms
384 ms
TPS not measured this hour · p95: 560ms
Kimi K3
OpenRouter(Relay)
Relay
In: $3.00
Out: $15.00
1009 ms
TPS not measured this hour · p95: 1009ms
11125 ms
19.2 t/s · p95: 11125ms
DeepSeek V4 Pro
OpenRouter(Relay)
Relay
In: $0.43
Out: $0.87
1116 ms
53.5 t/s · p95: 2689ms
1666 ms
32.1 t/s · p95: 2916ms
GLM-4.7
Zhipu AI GLM Official
Direct
In: ¥2.00
Out: ¥8.00
1181 ms
30.5 t/s · p95: 6142ms
2278 ms
6.0 t/s · p95: 2359ms
Kimi K2.6
Moonshot Kimi Official
Direct
In: ¥6.50
Out: ¥27.00
1456 ms
TPS not measured this hour · p95: 2259ms
2302 ms
TPS not measured this hour · p95: 2398ms
GLM-5.2
Zhipu AI GLM Official
Direct
In: ¥8.00
Out: ¥28.00
2088 ms
TPS not measured this hour · p95: 7160ms
1502 ms
TPS not measured this hour · p95: 3740ms
Kimi K3
Moonshot Kimi Official
Direct
In: ¥20.00
Out: ¥100.00
5998 ms
TPS not measured this hour · p95: 5998ms
4457 ms
15.0 t/s · p95: 4457ms
Claude Sonnet 5
OpenRouter(Relay)
Relay
In: $2.00
Out: $10.00
-
1344 ms
TPS not measured this hour · p95: 1344ms
Gemini 2.5 Flash
OpenRouter(Relay)
Relay
In: $0.30
Out: $2.50
-
261 ms
TPS not measured this hour · p95: 261ms

Citing this data

You are welcome to cite these figures in articles, documentation or a GitHub README under CC BY 4.0; please keep a link back to this page. The badge below shows that endpoint’s median TTFT from the most recent hourly aggregation:

[![DeepSeek V4 Pro latency](https://llmabacus-probe-badge.john-jones871024.workers.dev/badge/deepseek-v4-pro@official-deepseek)](https://llmabacus.com/en/live)

Swap deepseek-v4-pro@official-deepseekfor any endpoint ID in the table. The badge is served from Cloudflare’s edge with a 600 second cache, so embedding it puts no extra load on this site.