Fastest AI API from Europe (Germany)
· edge + inference latency
As of October 22, 2026, measured from Europe (Germany), the fastest AI inference API by edge latency (time-to-first-byte) is fireworks at 97 ms p50 (n=288).
Which AI API is fastest from Europe (Germany)?
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 | fireworks | 97 ms | 201 ms | 99.7% | 288 |
| 2 | nscale | 98 ms | 204 ms | 99.7% | 288 |
| 3 | 99 ms | 202 ms | 100% | 288 | |
| 4 | openrouter | 99 ms | 203 ms | 100% | 288 |
| 5 | nebius | 100 ms | 295 ms | 100% | 288 |
| 6 | meta-llama | 100 ms | 295 ms | 100% | 288 |
| 7 | aleph-alpha | 100 ms | 205 ms | 100% | 288 |
| 8 | mistral | 102 ms | 298 ms | 99.3% | 288 |
| 9 | inference-net | 194 ms | 503 ms | 99.3% | 288 |
| 10 | baichuan | 196 ms | 303 ms | 100% | 288 |
| 11 | cerebras | 198 ms | 302 ms | 99.3% | 288 |
| 12 | baseten | 199 ms | 304 ms | 100% | 288 |
| 13 | cohere | 199 ms | 301 ms | 100% | 288 |
| 14 | glm | 199 ms | 303 ms | 100% | 288 |
| 15 | perplexity | 200 ms | 397 ms | 100% | 288 |
| 16 | anthropic | 200 ms | 303 ms | 98.6% | 288 |
| 17 | replicate | 200 ms | 406 ms | 99.0% | 288 |
| 18 | typesafe | 200 ms | 300 ms | 99.7% | 288 |
| 19 | reka | 206 ms | 501 ms | 100% | 288 |
| 20 | yi-01ai | 230 ms | 836 ms | 100% | 288 |
| 21 | ernie | 284 ms | 303 ms | 100% | 288 |
| 22 | openai | 294 ms | 488 ms | 99.3% | 288 |
| 23 | siliconflow | 295 ms | 401 ms | 100% | 288 |
| 24 | xai | 295 ms | 401 ms | 99.3% | 288 |
| 25 | sarvam | 295 ms | 305 ms | 100% | 288 |
| 26 | groq | 296 ms | 490 ms | 99.3% | 288 |
| 27 | targon | 297 ms | 403 ms | 100% | 288 |
| 28 | novita | 297 ms | 495 ms | 99.0% | 288 |
| 29 | writer | 298 ms | 498 ms | 100% | 288 |
| 30 | together | 298 ms | 499 ms | 99.3% | 288 |
| 31 | deepseek | 299 ms | 403 ms | 100% | 288 |
| 32 | kimi | 299 ms | 491 ms | 99.0% | 288 |
| 33 | iflytek | 300 ms | 770 ms | 100% | 288 |
| 34 | doubao | 360 ms | 823 ms | 100% | 288 |
| 35 | sensenova | 390 ms | 958 ms | 100% | 288 |
| 36 | sambanova | 397 ms | 503 ms | 100% | 288 |
| 37 | friendli | 398 ms | 800 ms | 100% | 288 |
| 38 | ai21 | 399 ms | 600 ms | 99.7% | 288 |
| 39 | minimax | 399 ms | 600 ms | 100% | 288 |
| 40 | qwen | 401 ms | 699 ms | 100% | 288 |
| 41 | stepfun | 415 ms | 1099 ms | 100% | 288 |
| 42 | hyperbolic | 498 ms | 902 ms | 98.6% | 288 |
| 43 | upstage | 581 ms | 602 ms | 100% | 288 |
| 44 | deepinfra | 594 ms | 1096 ms | 100% | 288 |
| 45 | featherless | 700 ms | 1907 ms | 99.7% | 288 |
| 46 | hunyuan | 1532 ms | 1598 ms | 100% | 288 |
fireworks leads by 1 ms over nscale here. The median provider measured from Europe (Germany) answers in 295 ms, so the spread between the fastest and a typical option is 198 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 | groq | openai/gpt-oss-120b | 801 ms | 1391 ms | 72 |
| 2 | typesafe | jev-1.13.0 | 899 ms | 1499 ms | 72 |
| 3 | gemini-flash-lite-latest | 1192 ms | 3983 ms | 72 | |
| 4 | anthropic | claude-haiku-4-5 | 0 ms | 0 ms | 4 |
| 5 | openai | gpt-4o-mini | 0 ms | 0 ms | 1 |
| 6 | cerebras | gpt-oss-120b | 0 ms | 0 ms | 1 |
| 7 | openrouter | nvidia/nemotron-3-nano-30b-a3b:free | 0 ms | 0 ms | 1 |
Why measure from Europe (Germany) 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 Europe (Germany); 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.