Fastest AI API from South America (São Paulo)
· edge + inference latency
As of October 22, 2026, measured from South America (São Paulo), the fastest AI inference API by edge latency (time-to-first-byte) is openrouter at 61 ms p50 (n=289).
Which AI API is fastest from South America (São Paulo)?
Endpoint response (TTFB) · last 24 hours · milliseconds (ms) · lower is faster.
How to read these metrics
| # | Provider | Typical (p50 TTFB) | Slower (p95) | Uptime | Samples |
|---|---|---|---|---|---|
| 1 | openrouter | 61 ms | 104 ms | 100% | 289 |
| 2 | 142 ms | 199 ms | 100% | 289 | |
| 3 | baseten | 154 ms | 167 ms | 100% | 289 |
| 4 | cohere | 167 ms | 203 ms | 100% | 289 |
| 5 | typesafe | 174 ms | 186 ms | 100% | 289 |
| 6 | cerebras | 192 ms | 259 ms | 100% | 289 |
| 7 | anthropic | 197 ms | 266 ms | 100% | 289 |
| 8 | perplexity | 204 ms | 425 ms | 100% | 289 |
| 9 | reka | 208 ms | 447 ms | 100% | 289 |
| 10 | meta-llama | 218 ms | 377 ms | 100% | 289 |
| 11 | inference-net | 227 ms | 560 ms | 100% | 289 |
| 12 | xai | 233 ms | 426 ms | 100% | 289 |
| 13 | groq | 234 ms | 281 ms | 100% | 289 |
| 14 | writer | 247 ms | 288 ms | 100% | 289 |
| 15 | novita | 247 ms | 316 ms | 100% | 289 |
| 16 | fireworks | 252 ms | 276 ms | 100% | 289 |
| 17 | openai | 253 ms | 606 ms | 100% | 289 |
| 18 | mistral | 253 ms | 329 ms | 100% | 289 |
| 19 | replicate | 267 ms | 556 ms | 100% | 289 |
| 20 | friendli | 271 ms | 380 ms | 100% | 289 |
| 21 | together | 273 ms | 652 ms | 100% | 289 |
| 22 | siliconflow | 285 ms | 290 ms | 100% | 289 |
| 23 | targon | 316 ms | 329 ms | 100% | 289 |
| 24 | ai21 | 326 ms | 460 ms | 100% | 289 |
| 25 | yi-01ai | 332 ms | 1196 ms | 100% | 289 |
| 26 | kimi | 354 ms | 451 ms | 100% | 289 |
| 27 | baichuan | 360 ms | 392 ms | 100% | 289 |
| 28 | nebius | 366 ms | 374 ms | 100% | 289 |
| 29 | sambanova | 376 ms | 403 ms | 100% | 289 |
| 30 | ernie | 409 ms | 451 ms | 100% | 289 |
| 31 | deepseek | 436 ms | 501 ms | 100% | 289 |
| 32 | aleph-alpha | 444 ms | 532 ms | 100% | 289 |
| 33 | nscale | 451 ms | 478 ms | 100% | 289 |
| 34 | minimax | 484 ms | 517 ms | 100% | 289 |
| 35 | deepinfra | 538 ms | 1045 ms | 99.7% | 289 |
| 36 | glm | 539 ms | 573 ms | 100% | 289 |
| 37 | upstage | 606 ms | 646 ms | 100% | 289 |
| 38 | sarvam | 625 ms | 645 ms | 99.0% | 289 |
| 39 | sensenova | 634 ms | 1532 ms | 99.3% | 289 |
| 40 | iflytek | 648 ms | 1580 ms | 99.7% | 289 |
| 41 | stepfun | 659 ms | 1654 ms | 99.7% | 289 |
| 42 | doubao | 694 ms | 1598 ms | 99.7% | 289 |
| 43 | qwen | 716 ms | 772 ms | 100% | 289 |
| 44 | featherless | 744 ms | 2095 ms | 100% | 289 |
| 45 | hyperbolic | 845 ms | 933 ms | 100% | 289 |
| 46 | hunyuan | 1657 ms | 1739 ms | 100% | 289 |
openrouter leads by 81 ms over google here. The median provider measured from South America (São Paulo) answers in 326 ms, so the spread between the fastest and a typical option is 264 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
Models differ between providers; compare each named model over time.
| # | Provider | Model | Typical (p50 TTFT) | Slower (p95) | Samples |
|---|---|---|---|---|---|
| 1 | typesafe | jev-1.13.0 | 400 ms | 496 ms | 72 |
| 2 | groq | openai/gpt-oss-120b | 776 ms | 1141 ms | 72 |
| 3 | gemini-flash-lite-latest | 1151 ms | 3599 ms | 72 |
Why measure from South America (São Paulo) 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 (São Paulo); 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.