LLM Latency Calculator
Estimate LLM response time by output length — compare time to first token and total wait across models.
It estimates how long users will wait for an AI answer.
What this tool does
AI responses have two useful timing numbers. Time to first token measures when text starts appearing. Generation speed determines when the full answer finishes. This tool estimates both.
- Compare how fast several models feel to a user
- Estimate total wait time for short and long answers
- Set sensible output limits for interactive features
Tokens in the model's reply (not your prompt).
Sorted fastest to slowest. TTFT = time to first token; Gen = remaining generation time.
| Model | Provider | TTFT | Gen | Total | Relative |
|---|---|---|---|---|---|
Everything runs in your browser. Numbers are rough mid-2026 estimates — not benchmarks from your exact prompt or region.
Latency is the UX cost nobody puts in a spreadsheet. This calculator
combines time-to-first-token (TTFT) with output throughput to estimate
total wait time: total ≈ TTFT + outputTokens ÷ tokens/sec.
Figures are ballpark mid-2026 estimates from public benchmarks and vendor docs. They vary with prompt size, queue depth, caching, region, and whether the model is warm. Use them to compare orders of magnitude — not to promise SLAs.
Groq and similar speed-focused hosts trade model size and reasoning depth for raw tokens/sec. Frontier models on OpenAI or Anthropic are slower per token but often need fewer tokens to finish the job — latency and quality are not the same tradeoff.