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Fastest AI API from South America (Santiago)

· edge + inference latency

As of November 08, 2026, measured from South America (Santiago), the fastest AI inference API by edge latency (time-to-first-byte) is openrouter at 57 ms p50 (n=21).

Which AI API is fastest from South America (Santiago)?

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
1openrouter57 ms83 ms100%21
2meta-llama165 ms379 ms100%21
3typesafe174 ms179 ms100%21
4baseten176 ms198 ms100%21
5cerebras181 ms237 ms100%21
6xai187 ms220 ms100%21
7anthropic192 ms218 ms100%21
8groq196 ms300 ms100%21
9cohere202 ms226 ms100%21
10openai212 ms252 ms100%21
11inference-net213 ms265 ms100%21
12novita219 ms332 ms100%21
13perplexity234 ms481 ms100%21
14together238 ms619 ms100%21
15fireworks260 ms276 ms100%21
16reka264 ms571 ms100%21
17writer266 ms329 ms100%21
18siliconflow280 ms287 ms100%21
19friendli294 ms389 ms100%21
20mistral300 ms341 ms100%21
21kimi343 ms384 ms100%21
22sambanova379 ms384 ms100%21
23baichuan393 ms1385 ms100%21
24ai21395 ms506 ms100%21
25ernie412 ms418 ms100%21
26nebius440 ms475 ms100%21
27nscale441 ms443 ms100%21
28deepseek455 ms525 ms100%21
29aleph-alpha464 ms475 ms100%21
30minimax496 ms512 ms100%21
31deepinfra527 ms564 ms100%21
32glm533 ms545 ms100%21
33replicate556 ms708 ms100%21
34upstage593 ms685 ms100%21
35sarvam673 ms689 ms100%21
36stepfun704 ms1758 ms100%21
37sensenova729 ms1660 ms100%21
38qwencloud732 ms750 ms100%15
39qwen735 ms773 ms100%21
40iflytek741 ms1711 ms100%21
41featherless742 ms834 ms100%21
42hyperbolic752 ms783 ms100%21
43doubao763 ms1725 ms100%21
44tencent-tokenhub793 ms806 ms100%21
45google970 ms1071 ms100%21

openrouter leads by 108 ms over meta-llama here. The median provider measured from South America (Santiago) answers in 393 ms, so the spread between the fastest and a typical option is 336 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-1.13.0306 ms454 ms5
2groqopenai/gpt-oss-120b717 ms823 ms5
3googlegemini-flash-lite-latest1891 ms2003 ms5

Why measure from South America (Santiago) 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 South America (Santiago); 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.