How long an LLM response actually takes β time to first token plus generation.
Users judge speed on how fast the first token lands and how fast the rest flows. This tool separates both so you can pick a model and streaming strategy that feels fast. Size the bill behind that speed on the token cost calculator.
The LLM Latency & Response Time Calculator estimates the total time a user waits for a model to answer, not a dollar figure. It combines three drivers: the time to first token (the initial delay in milliseconds before any output appears), the generation speed in tokens per second, and the number of output tokens in the response. Total response time is roughly the first-token delay plus output tokens divided by generation speed, and the requests per minute input frames this for a single stream so you can see how one user's experience scales.
The key trade-off is that total latency grows with response length: a fast model can still feel slow if it generates long answers, and a low time to first token matters most for short replies where users notice the initial pause. When tuning, decide whether perceived responsiveness (quick first token) or completion time (short, fast output) matters more for your use case, and shorten prompts or cap output tokens to keep waits acceptable.
Two things: time to first token (TTFT) β the wait before streaming begins, typically 0.3β2s and higher for reasoning models β and tokens per second, the generation rate. Total time = TTFT + (output tokens Γ· tokens per second). Input length mainly affects TTFT, output length affects generation.
Because users read tokens as they stream in, so perceived latency is close to TTFT rather than total time. Non-streamed responses (structured JSON, tool calls, batch jobs) block until the whole answer is ready, so the full response time is felt. Stream anything a human waits on.