Datasets:

llmlatency
/

llm-latency-tracker

date stringdate 2026-07-23 00:00:00 2026-08-17 00:00:00	provider stringclasses 45 values	region stringclasses 4 values	probe_type stringclasses 2 values	metric stringclasses 2 values	p50_ms float64 11.9 2.8k	p95_ms float64 44.4 9.06k	samples int64 8 292	success_pct float64 69 100
2026-07-23	ai21	ap-tokyo	network	ttfb	304.5	400.6	112	100
2026-07-23	ai21	eu-hetzner	network	ttfb	303.7	501.8	114	100
2026-07-23	ai21	sa-east	network	ttfb	308.9	408.9	112	100
2026-07-23	ai21	us-central	network	ttfb	167.9	276.4	112	100
2026-07-23	aleph-alpha	ap-tokyo	network	ttfb	572.2	638.2	112	100
2026-07-23	aleph-alpha	eu-hetzner	network	ttfb	101.3	293.8	114	100
2026-07-23	aleph-alpha	sa-east	network	ttfb	482.1	564.4	112	100
2026-07-23	aleph-alpha	us-central	network	ttfb	301.9	423.1	112	100
2026-07-23	anthropic	ap-tokyo	network	ttfb	247.2	414.8	135	100
2026-07-23	anthropic	eu-hetzner	network	ttfb	199.1	401.9	149	100
2026-07-23	anthropic	sa-east	network	ttfb	211.8	608.1	135	100
2026-07-23	anthropic	us-central	network	ttfb	107.9	237.3	135	100
2026-07-23	baichuan	ap-tokyo	network	ttfb	178.4	201	112	99.11
2026-07-23	baichuan	eu-hetzner	network	ttfb	178	244.1	114	100
2026-07-23	baichuan	sa-east	network	ttfb	337.8	356.2	112	100
2026-07-23	baichuan	us-central	network	ttfb	233.9	254.6	112	100
2026-07-23	baseten	ap-tokyo	network	ttfb	155.8	168.3	112	100
2026-07-23	baseten	eu-hetzner	network	ttfb	196.8	289	114	100
2026-07-23	baseten	sa-east	network	ttfb	150.2	158	112	100
2026-07-23	baseten	us-central	network	ttfb	54.9	67.2	112	100
2026-07-23	cerebras	ap-tokyo	network	ttfb	201.3	252.8	135	100
2026-07-23	cerebras	eu-hetzner	network	ttfb	198.3	397.3	149	100
2026-07-23	cerebras	sa-east	network	ttfb	186.7	236.6	135	100
2026-07-23	cerebras	us-central	network	ttfb	63.7	130.3	135	100
2026-07-23	cohere	ap-tokyo	network	ttfb	166.9	202.4	135	100
2026-07-23	cohere	eu-hetzner	network	ttfb	199.1	302.9	149	100
2026-07-23	cohere	sa-east	network	ttfb	158	175.5	135	100
2026-07-23	cohere	us-central	network	ttfb	60.5	110.3	135	100
2026-07-23	deepinfra	ap-tokyo	network	ttfb	502.2	684.4	112	100
2026-07-23	deepinfra	eu-hetzner	network	ttfb	597.1	1,092.9	114	100
2026-07-23	deepinfra	sa-east	network	ttfb	589.1	715	112	100
2026-07-23	deepinfra	us-central	network	ttfb	229.4	684.1	112	100
2026-07-23	deepseek	ap-tokyo	network	ttfb	169.7	237.8	135	100
2026-07-23	deepseek	eu-hetzner	network	ttfb	298.8	501.4	149	99.33
2026-07-23	deepseek	sa-east	network	ttfb	425.5	453.7	135	100
2026-07-23	deepseek	us-central	network	ttfb	307.1	371.8	135	100
2026-07-23	doubao	ap-tokyo	network	ttfb	255.4	844.8	112	100
2026-07-23	doubao	eu-hetzner	network	ttfb	354.3	805.1	114	100
2026-07-23	doubao	sa-east	network	ttfb	680.9	1,573.2	112	100
2026-07-23	doubao	us-central	network	ttfb	478.3	1,121.2	112	100
2026-07-23	ernie	ap-tokyo	network	ttfb	137.8	143.7	112	100
2026-07-23	ernie	eu-hetzner	network	ttfb	279.9	284.9	114	100
2026-07-23	ernie	sa-east	network	ttfb	410.1	421.5	112	100
2026-07-23	ernie	us-central	network	ttfb	271	278.2	112	100
2026-07-23	featherless	ap-tokyo	network	ttfb	505.4	2,134.9	112	100
2026-07-23	featherless	eu-hetzner	network	ttfb	700.1	1,102.6	114	100
2026-07-23	featherless	sa-east	network	ttfb	744.9	3,520	112	99.11
2026-07-23	featherless	us-central	network	ttfb	351.5	1,356.5	112	100
2026-07-23	fireworks	ap-tokyo	network	ttfb	11.9	44.4	135	100
2026-07-23	fireworks	eu-hetzner	network	ttfb	98	202	149	100
2026-07-23	fireworks	sa-east	network	ttfb	263.8	305.7	135	100
2026-07-23	fireworks	us-central	network	ttfb	25.6	66.5	135	100
2026-07-23	glm	ap-tokyo	network	ttfb	355.6	387.7	117	100
2026-07-23	glm	eu-hetzner	network	ttfb	801.3	1,494.8	119	100
2026-07-23	glm	sa-east	network	ttfb	648.9	656.5	117	100
2026-07-23	glm	us-central	network	ttfb	386.7	423.8	117	100
2026-07-23	google	ap-tokyo	network	ttfb	58	97.4	135	100
2026-07-23	google	eu-hetzner	network	ttfb	98.3	199	149	100
2026-07-23	google	sa-east	network	ttfb	151.8	303.2	135	100
2026-07-23	google	us-central	network	ttfb	37.6	92.8	135	100
2026-07-23	groq	ap-tokyo	network	ttfb	146.7	187.9	135	100
2026-07-23	groq	eu-hetzner	network	ttfb	296.6	400.7	149	100
2026-07-23	groq	sa-east	network	ttfb	226.8	262.9	135	100
2026-07-23	groq	us-central	network	ttfb	114.8	183.8	135	100
2026-07-23	hunyuan	ap-tokyo	network	ttfb	1,118.1	1,148.8	112	100
2026-07-23	hunyuan	eu-hetzner	network	ttfb	1,539.7	1,577.4	114	100
2026-07-23	hunyuan	sa-east	network	ttfb	1,653.5	1,671.2	112	100
2026-07-23	hunyuan	us-central	network	ttfb	1,378	1,388.2	112	100
2026-07-23	hyperbolic	ap-tokyo	network	ttfb	493.1	550.4	112	100
2026-07-23	hyperbolic	eu-hetzner	network	ttfb	300.9	798	114	100
2026-07-23	hyperbolic	sa-east	network	ttfb	800.4	887	112	100
2026-07-23	hyperbolic	us-central	network	ttfb	229.8	282.2	112	100
2026-07-23	iflytek	ap-tokyo	network	ttfb	224.4	842.7	112	100
2026-07-23	iflytek	eu-hetzner	network	ttfb	299.4	727.7	114	100
2026-07-23	iflytek	sa-east	network	ttfb	638.2	1,533.2	112	100
2026-07-23	iflytek	us-central	network	ttfb	427.1	1,532.5	112	99.11
2026-07-23	inference-net	ap-tokyo	network	ttfb	529	970	112	100
2026-07-23	inference-net	eu-hetzner	network	ttfb	197.1	899.5	114	100
2026-07-23	inference-net	sa-east	network	ttfb	322.4	1,251.3	112	100
2026-07-23	inference-net	us-central	network	ttfb	160.7	205.2	112	100
2026-07-23	kimi	ap-tokyo	network	ttfb	125.7	158.1	117	100
2026-07-23	kimi	eu-hetzner	network	ttfb	299.6	401.1	119	100
2026-07-23	kimi	sa-east	network	ttfb	338.2	374.9	117	100
2026-07-23	kimi	us-central	network	ttfb	264.9	332.8	117	100
2026-07-23	meta-llama	ap-tokyo	network	ttfb	128.2	349.3	112	100
2026-07-23	meta-llama	eu-hetzner	network	ttfb	100.8	290	114	100
2026-07-23	meta-llama	sa-east	network	ttfb	221.8	245	112	100
2026-07-23	meta-llama	us-central	network	ttfb	47.5	110.3	112	100
2026-07-23	mistral	ap-tokyo	network	ttfb	310.5	366.5	135	100
2026-07-23	mistral	eu-hetzner	network	ttfb	99.3	201.6	149	100
2026-07-23	mistral	sa-east	network	ttfb	251.8	290.6	135	100
2026-07-23	mistral	us-central	network	ttfb	182.4	252.1	135	100
2026-07-23	nebius	ap-tokyo	network	ttfb	547	598.4	112	100
2026-07-23	nebius	eu-hetzner	network	ttfb	99.7	296.4	114	100
2026-07-23	nebius	sa-east	network	ttfb	459.5	484.3	112	100
2026-07-23	nebius	us-central	network	ttfb	218	258.4	112	100
2026-07-23	novita	ap-tokyo	network	ttfb	145.4	173.5	112	100
2026-07-23	novita	eu-hetzner	network	ttfb	298.1	496.6	114	100
2026-07-23	novita	sa-east	network	ttfb	236.2	272.7	112	100
2026-07-23	novita	us-central	network	ttfb	67.7	137.9	112	100

End of preview. Expand in Data Studio

LLM Latency Tracker

Independent, continuously measured latency and availability for AI inference API providers, aggregated by day. Covers 45 providers across 4 regions (ap-tokyo, eu-hetzner, sa-east, us-central), built from 1,295,385 raw probes collected between 2026-07-23 and 2026-08-17.

Live rankings and full methodology: llmlatency.dev

How the numbers are produced

Probes run every five minutes from separate network locations and are never routed through a gateway or an aggregator, so the numbers describe the providers themselves rather than a proxy in front of them.

Network probe — DNS → TCP → TLS → time to first byte (ttfb).
Inference probe — time to first token on a real completion request (ttft).

These are different quantities, an order of magnitude apart, and they are never mixed in one ranking. Percentiles are nearest-rank, identical to the ones shown on the site.

Files

file	contents
`daily_aggregates.csv`	one row per date × provider × region × probe type, with p50/p95, sample count and success rate
`rankings.json`	machine-readable snapshot published live at llmlatency.dev

Usage

from datasets import load_dataset

ds = load_dataset("llmlatency/llm-latency-tracker", split="train")
ds.filter(lambda r: r["region"] == "eu-hetzner" and r["probe_type"] == "network")

Or without this dataset at all — there is a client on PyPI:

pip install llmlatency
llmlatency fastest

Limitations, stated up front

Vantage points are cloud data centres, not consumer networks, so absolute values are lower than an end user would see — the comparison between providers is the meaningful part. Provider coverage changes over time as APIs appear and shut down.

Citation

Archived, citable version with a DOI: https://doi.org/10.5281/zenodo.21954788

@dataset{llmlatency_tracker,
  title  = {LLM Latency Tracker: measured latency and uptime for AI inference APIs},
  author = {llmlatency.dev},
  year   = {2026},
  doi    = {10.5281/zenodo.21954788},
  url    = {https://llmlatency.dev}
}