LLM Latency & TTFT Estimator
Estimate TTFT, total generation time, and p99 latency for any LLM. Check whether your model fits your latency budget for interactive products.
TTFT and TPS are community benchmark estimates — actual latency varies by provider load, region, and context length. Treat as a planning guide, not an SLA. Verified 2026-08-20.
How to Use
Select a model
Choose the model you are evaluating or already using. TTFT and tokens-per-second estimates are based on community benchmarks.
Set output length
Enter expected output tokens per response. Longer outputs increase total generation time but not TTFT.
Enter your latency budget
The maximum end-to-end latency acceptable for your use case: <500ms for real-time, 1–3s for interactive chat, 5s+ for async tasks.
Compare streaming vs non-streaming
Toggle streaming to see how much it reduces perceived latency. All interactive products should use streaming.
Why latency architecture matters
Cost and quality dominate model selection discussions, but latency determines whether your product feels usable. Users abandon AI features that take >3s to respond. TTFT and streaming are your primary levers: switching from a frontier model to a flash model reduces TTFT 3–5× and enables streaming-first UX at lower cost. For async workloads (batch, background processing), latency is irrelevant — prioritize cost and quality instead.
How to Cut This Cost
Limit max output tokens strictly. Generation time is linear in output length — if you only need 100 tokens, cap at 100, not 2,000.
Use streaming (SSE) so TTFT is perceived as response start rather than waiting for full completion. UX win with no cost increase.
Switch to a Fast tier model (Haiku 4.5, GPT-5 mini, Gemini Flash). For latency-sensitive paths, faster models usually beat slower ones even on quality per second.
FAQ
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Last verified: 2026-08-20 · methodology · data sources