LLM Latency Calculator
Estimate expected LLM response latency — time-to-first-token and total response time — for planning real-time AI applications.
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How to use this calculator
- 1
Select the LLM you are planning to use.
- 2
Enter the expected input token count (your prompt length) and output token count (desired response length).
- 3
Select the expected server load — low for off-peak hours, high for peak traffic.
- 4
Review TTFT and total response time to determine if the model fits your latency budget.
Frequently asked questions
What is time to first token (TTFT)?
TTFT is the time from when the API request is sent to when the first response token begins streaming. For chat applications, this is the user-perceived "thinking time." Minimizing TTFT — by using streaming APIs and choosing faster models — dramatically improves perceived responsiveness even before the full response arrives.
Why does concurrency affect latency?
LLM providers share GPU compute across all simultaneous users. During peak hours, your requests may queue behind others, increasing both TTFT and generation time. High-concurrency estimates in this calculator reflect realistic worst-case shared-infrastructure latencies.
Should I always use the fastest model?
Not necessarily. Faster models (GPT-4o-mini, Claude Haiku, Gemini Flash) sacrifice some capability. For simple tasks — summarization, classification, extraction — smaller fast models often match larger models in output quality at much lower latency and cost. For complex reasoning or code generation, larger models typically produce better results.
LLM Latency Calculator — TTFT and Response Time Estimator
Why latency matters for LLM applications
User experience research shows that response latency over 1 second disrupts flow, and latency over 3 seconds causes abandonment in conversational applications. Real-time chat applications need TTFT under 500 ms and full response under 3 s. Use streaming to show tokens as they arrive — this halves perceived latency even when total time is the same.
Streaming vs waiting for full response
All major LLM providers support server-sent events (SSE) streaming. When streaming is enabled, users see the first token within the TTFT window and the response builds in real time. For a 300-token response at 60 tokens/second, streaming turns a 5-second wait into a response that starts in 400 ms. Always use streaming for user-facing chat applications.
Learn more from an authoritative source:
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Results are estimates for informational purposes only and do not constitute professional financial, medical, legal, or technical advice. Read full disclaimer →