llmping guide
What Is Time to First Token?
Published May 12, 2026. Updated May 12, 2026. Markdown version.
Key facts
- TTFT is measured before the first generated token, while total latency includes the whole completion.
- Streaming can improve perceived speed even when total completion time stays the same.
- The llmping benchmark records TTFT next to P50, P95, P99, output speed, sample count, and collection timestamp.
TTFT is the response-start metric
Time to first token is the elapsed time from request dispatch to the first token chunk returned by the model endpoint. It includes client network time, provider edge routing, queue time, prompt ingestion, safety checks, and the model's first decode step.
TTFT is not the same as total response time. A model can have a fast first token and slow total completion if it streams slowly. A model can also have a slower first token but finish a short answer quickly because the output rate is high.
| Metric | Starts | Ends | Best use |
|---|---|---|---|
| TTFT | Request sent | First generated token | Chat perceived speed |
| P50 latency | Request sent | Completed response or measured event | Typical user experience |
| P95 latency | Request sent | Completed response or measured event | SLO and tail monitoring |
| Tokens/sec | First token | Last token | Long completion throughput |
Why developers should track TTFT
LLM products are usually judged in the first second. If the interface shows no movement, the user reads the product as slow even when the answer eventually arrives. TTFT is the metric that catches this gap.
The metric is especially important for support bots, code assistants, search answer interfaces, and AI copilots. These products do not need the entire answer to feel responsive. They need the first useful text to appear quickly and predictably.
The public llmping leaderboard exposes TTFT in a native HTML table at https://llmping.com/leaderboard/ and in JSON at https://llmping.com/data/latency-benchmark.json so the metric can be cited by crawlers and reused by developers.
What makes TTFT slow
The biggest causes are long prompts, cold provider queues, overloaded regions, and cross-region network routing. Retrieval augmented generation can also hurt TTFT when the application waits for search, reranking, and prompt assembly before it sends the model request.
Model size matters, but it is not the only factor. A smaller model behind a busy route can be slower than a larger model on a well-provisioned path. That is why llmping stores provider, model, region, timestamp, and sample count on each benchmark row.
How to reduce TTFT
Reduce prompt tokens first. Move stable instructions into provider-supported cached context when available, shorten retrieved snippets, and avoid sending data the model does not need for the first answer.
Run model calls from the region nearest the provider endpoint. If your users are global, benchmark from each production region and route to the model/provider pair with the best P95, not only the best median.
Stream every interactive response. Streaming does not make the model think faster, but it turns TTFT into visible progress and avoids making users wait for the final token.
Source links
Benchmark dataset: LLM API Latency Leaderboard. JSON download: latency-benchmark.json. Full markdown corpus: llms-full.txt.