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Fastest AI API from US (Central)

· edge + inference latency

As of October 22, 2026, measured from US (Central), the fastest AI inference API by edge latency (time-to-first-byte) is fireworks at 28 ms p50 (n=289).

Which AI API is fastest from US (Central)?

Endpoint response (TTFB) · last 24 hours · milliseconds (ms) · lower is faster.

How to read these metrics
Endpoint response (TTFB)Network connection and the first response byte, without model inference.
Typical · p5050% of successful timed samples finish within this time.
Slower · p9595% of successful timed samples finish within this time.
#ProviderTypical (p50 TTFB)Slower (p95)UptimeSamples
1fireworks28 ms72 ms100%289
2baseten46 ms61 ms100%289
3meta-llama54 ms114 ms100%289
4google58 ms160 ms100%289
5cohere61 ms127 ms100%289
6openrouter63 ms142 ms100%289
7cerebras76 ms151 ms100%289
8typesafe76 ms97 ms100%289
9siliconflow78 ms104 ms100%289
10novita84 ms162 ms100%289
11perplexity88 ms143 ms100%289
12targon90 ms102 ms100%289
13replicate92 ms152 ms100%289
14anthropic94 ms160 ms100%289
15reka103 ms191 ms99.7%289
16xai111 ms175 ms100%289
17groq116 ms190 ms100%289
18together125 ms209 ms100%289
19hyperbolic126 ms275 ms100%289
20openai142 ms232 ms100%289
21writer156 ms215 ms100%289
22deepinfra164 ms726 ms100%289
23inference-net164 ms435 ms100%289
24friendli166 ms246 ms100%289
25sambanova173 ms222 ms100%289
26nebius178 ms213 ms100%289
27mistral197 ms269 ms100%289
28yi-01ai223 ms877 ms100%289
29nscale236 ms242 ms100%289
30ai21237 ms373 ms99.7%289
31baichuan238 ms868 ms100%289
32glm255 ms291 ms100%289
33aleph-alpha256 ms322 ms100%289
34kimi269 ms350 ms100%289
35ernie269 ms281 ms100%289
36deepseek318 ms384 ms100%289
37minimax338 ms362 ms100%289
38upstage353 ms373 ms100%289
39featherless374 ms2851 ms99.7%289
40stepfun401 ms1060 ms100%289
41qwen406 ms454 ms100%289
42iflytek412 ms1438 ms100%289
43sensenova414 ms1086 ms99.7%289
44doubao463 ms1122 ms100%289
45sarvam499 ms520 ms100%289
46hunyuan1382 ms1398 ms100%289

fireworks leads by 18 ms over baseten here. The median provider measured from US (Central) answers in 166 ms, so the spread between the fastest and a typical option is 138 ms on every single request — before the model has produced anything.

Inference latency (time-to-first-token)

Each provider is measured on the model named below — model size affects TTFT far more than the network does, so these numbers are not a like-for-like ranking of providers. Use the edge-latency table above for that. Coverage is limited to providers we hold an API key for.

First model token (TTFT) · last 24 hours · milliseconds (ms) · lower is faster.

How to read these metrics
First model token (TTFT)Wait until a streamed model response emits its first token.
Typical · p5050% of successful timed samples finish within this time.
Slower · p9595% of successful timed samples finish within this time.

Models differ between providers; compare each named model over time.

#ProviderModelTypical (p50 TTFT)Slower (p95)Samples
1typesafejev-1.13.0522 ms697 ms72
2groqopenai/gpt-oss-120b1345 ms1746 ms72
3googlegemini-flash-lite-latest1591 ms6437 ms72

Why measure from US (Central) separately?

Latency is a property of a route, not of a company. The same provider can lead in one region and trail in another, so a ranking produced from a single location tells you very little about what your users will experience somewhere else. This page reports only what was measured from US (Central); the other regions are ranked independently and often disagree — see the cross-region summary for how far apart they get.

How should these numbers be read?

p50 is the typical request and p95 is the slow tail: if p95 is far above p50, that provider is inconsistent from here, which usually hurts more than a slightly higher median. Uptime counts a probe as failed only on a genuine service failure — an authentication error means the endpoint answered correctly and counts as up. All figures cover the last 24 hours and are recomputed continuously; the methodology page states the limits of this data plainly.