LLM Latency & Time-to-First-Token (TTFT) Simulator

Simulate real-world streaming throughput, TTFT, prefill delays, and agent loop execution times across LLMs.

Time to First Token (TTFT)
540 ms
Base 380ms + Prefill 100ms + Ping 60ms
Streaming Generation Rate
~105 tok/s
Generation time: 5.71s for 600 tokens
Total End-to-End Latency
6.25 s
Single round-trip stream
Recommended Timeout
16 s
Safety threshold for API Gateway / Axios
1. Client Network Round-trip (TLS + Ping)60 ms
2. Model Prompt Processing & First Token (TTFT)540 ms
3. Token Streaming Generation (600 tokens @ 105 tok/s)5.71 s

Understanding Latency in Generative AI Applications

In production conversational AI, voice assistants, and agentic workflows, perceived latency is divided into two distinct phases: Time to First Token (TTFT) and Token Generation Throughput (tokens/second).

The Anatomy of an LLM API Request

  • Network Transit & Handshake (Ping): The physical TCP/TLS roundtrip from your client or edge worker to the AI provider's inference cluster (e.g. us-east-1).
  • Prefill Phase (Prompt Ingestion): The transformer parallelizes processing of all input prompt tokens. Large prompts (e.g. 50k tokens of RAG documents) introduce noticeable prefill compute delay before the first token is emitted.
  • Decoding Phase (Streaming Output): Autoregressive generation where tokens are generated sequentially one by one. The throughput (e.g. 100 tok/sec on GPT-4o vs 280 tok/sec on Groq LPUs) dictates how fast text streams across the user's screen.

Mitigating Latency in Agent Loops

For multi-step agent loops that perform planning, searching, and tool calling across 3 to 5 sequential rounds, total latency compounds quickly. Using faster sub-models (like Claude 3.5 Haiku or Gemini 1.5 Flash) for intermediate classification and tool routing reduces end-to-end user wait times by up to 70%.