What is TTFT?
Complete Guide for AI Engineers
Learn everything about Time to First Token (TTFT) - the critical metric for LLM performance. Includes benchmarks, optimization tips, and real-world examples.
Table of Contents
- 1. What Is TTFT?
- 2. Why TTFT Matters More Than You Think
- 3. TTFT vs Token Throughput vs Total Latency
- 4. What Affects TTFT?
- 5. What Is a Good TTFT?
- 6. How to Measure TTFT Properly
- 7. Common TTFT Mistakes
- 8. How to Optimize TTFT in Production
When you're building AI products — especially AI voice agents, chatbots, or real-time assistants — one metric matters more than almost anything else:
TTFT: Time to First Token
If your AI takes too long to start responding, users don't care how smart it is…
They just feel like it's broken.
In this guide, you'll learn:
- →What TTFT is (in plain English)
- →Why it's one of the most important LLM performance metrics
- →How it differs from token throughput and total latency
- →How to measure and optimize it in production
What Is TTFT?
TTFT (Time to First Token) measures the time between:
- When you send a request to a language model
- When you receive the first token of its response
In simple terms:
It's how long it takes for the model to start responding.
If a model takes 1.2 seconds before saying its first word — your TTFT is 1200ms, even if the rest of the answer streams quickly.
Why TTFT Matters More Than You Think
Most people focus on:
- Tokens per second
- Total response latency
- Model accuracy
But for real-time systems, TTFT is the moment of truth.
TTFT is the first impression
From a human perspective:
Even if the full answer arrives quickly after that.
The initial silence is what people notice.
This is especially critical for:
- 🎙️ AI voice agents
- 💬 Live customer support bots
- 📈 Trading assistants
- ✨ Real-time copilots
- 🤖 Conversational apps
⚠️ For voice: High TTFT literally sounds like the agent is "thinking too long" or freezing.
TTFT vs Token Throughput vs Total Latency
These metrics are often confused. Here's the difference:
| Metric | What it measures | Why it matters |
|---|---|---|
| TTFT | Time until first token | How fast the AI starts responding |
| Token throughput | Tokens per second | How fast the model streams once started |
| Total latency | Full response time | How long the entire answer takes |
Example:
| Event | Time |
| You send prompt | 0ms |
| First token arrives | 900ms |
| Full answer done | 1400ms |
⚠️ Key insight: A model can have fast streaming but terrible TTFT — and that will still feel slow.
What Affects TTFT?
TTFT depends on multiple factors:
1. Model Architecture
Bigger models generally have higher TTFT:
- •GPT-4 level models often have slower TTFT
- •Smaller or optimized models (Flash, Mini, etc.) tend to be faster
2. Model Provider Load
TTFT can spike during:
- •High API usage times
- •Partial outages
- •Infrastructure congestion
💡 This is why live monitoring matters.
3. Prompt Length
Longer inputs → more processing → higher TTFT
4. Network & Location
Latency from your server to the model provider also plays a role.
Tip: Choose regions closer to provider infrastructure
What Is a Good TTFT?
It depends on use case:
Voice Agents
Critical for conversational flow. Users notice delays above 700ms.
Chatbots
Good balance between speed and complexity for text-based interactions.
Background Tasks
Acceptable for async processes where immediate response isn't critical.
Internal Tools
Less strict requirements for internal-facing applications.
⚠️ For voice: Anything above ~700–800ms starts to feel very noticeable.
Why TTFT Fluctuates by Hour of Day
One thing most devs miss:
LLM TTFT changes constantly throughout the day.
A model that's fast at 10am UTC might be slow at 8pm UTC due to global traffic patterns.
📊We've observed:
Some models are fast during US hours but slow during Asia peak
Others show latency spikes during major events or outages
Even within the same provider, different models behave very differently
Live TTFT Performance (Last 24h)Provider Averages
Real-time data showing how TTFT changes throughout the day
Loading performance data...
⚠️This is why relying on a single static benchmark is misleading.
How to Measure TTFT Properly
📋What You Need
A consistent prompt
Server timestamps when request is sent
Timestamp when first token arrives
Repeated sampling across time
🎯This Allows You To
Track model latency trends
Compare providers fairly
Build routing logic for production
If you're building voice agents or large-scale AI systems, doing this manually gets painful — which is why tools like Metrik exist.
Common TTFT Mistakes
Here's where most teams go wrong:
Measuring once and assuming stability
TTFT changes constantly throughout the day
Ignoring time-of-day effects
Models perform differently during peak vs off-peak hours
Confusing total latency with TTFT
These are different metrics that matter for different reasons
Not routing dynamically
Always using the same model regardless of current performance
Blaming "model intelligence" for latency issues
Your AI isn't dumb — it's just stuck waiting
How to Optimize TTFT in Production
Real strategies that actually help:
Use Shorter Prompts
When possible, reduce input length to minimize processing time
Use Optimized Models
Choose smaller or "Flash"/"Mini" variants for real-time tasks
Route Dynamically Based on Live Latency
Most impactful strategy: Switch between multiple LLMs in real-time based on current TTFT performance
Measure Per Region & Time
Track TTFT by geographic region and time bucket
Monitor & Alert
Set up alerts when latency spikes beyond acceptable thresholds
Why We Built Real-Time TTFT Monitoring
While building AI voice agents, we kept getting unpredictable delays.
Sometimes GPT felt instant
Sometimes Claude lagged
Sometimes models that were fast last night were unusable the next morning
So we built a live LLM latency + TTFT monitor and API
Tracks performance across 26 models in real time and helps route to the fastest one automatically.
Check live TTFT performance:
metrik-dashboard.vercel.appFinal Takeaways
TTFT = Time to First Token
It's the most important metric for real-time AI systems
It matters more than raw throughput for user experience
It fluctuates constantly by model, provider, and time of day
You should measure it continuously, not once
If your AI feels slow...
It's probably not "dumb" —
It's just stuck in TTFT hell.
Want Real-Time TTFT Data?
Metrik tracks Time to First Token across 26+ LLM models, updated every hour. Get live benchmarks, historical trends, and API access.
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