Streaming Simulator
Simulate and visualize LLM streaming response behavior
Simulation Parameters
Streamed Output
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What is LLM Streaming?
Streaming allows AI models to send tokens as they're generated rather than waiting for the complete response. Instead of a single response after several seconds, users see text appear word-by-word in real-time—dramatically improving perceived latency and user experience.
This streaming simulator helps you visualize how different token generation speeds affect the user experience. Experiment with various speeds to understand what feels responsive versus sluggish, and plan your UI accordingly.
How LLM Streaming Works
Server-Sent Events (SSE)
Most LLM APIs use SSE—a one-way data stream from server to client. Each event contains a token chunk that your frontend displays immediately.
Token Generation Speed
Speed varies by model: GPT-4 generates ~40 tokens/sec, Claude 3 Haiku ~120 tok/s. Faster models feel more responsive.
Time to First Token (TTFT)
The delay before the first token appears. Even with streaming, there's initial latency as the model processes your prompt.
Streaming UX Best Practices
- Show a typing indicator: While waiting for the first token, show a pulsing cursor or "thinking" animation.
- Scroll to follow: Auto-scroll the chat window as new text appears so users see the latest content.
- Handle interruptions: Let users stop generation mid-stream if the response is going in the wrong direction.
- Buffer for smoothness: Consider buffering a few tokens before displaying to avoid choppy character-by-character rendering.
- Show progress: For long responses, indicate approximate progress or token count.
Model Speed Comparison
| Model | Speed | User Feel |
|---|---|---|
| Claude 3 Haiku | ~120 tok/s | Very fast, near-instant |
| GPT-3.5 Turbo | ~100 tok/s | Fast, responsive |
| GPT-4 Turbo | ~80 tok/s | Good, noticeable but smooth |
| GPT-4 / Claude Opus | ~30-40 tok/s | Slower, visible typing speed |
Implementing Streaming
Frontend
Use the Fetch API with ReadableStream, or libraries like vercel/ai. Parse SSE events and append to your message state.
Backend
Set stream: true in your API call. Forward SSE events to the client or use edge functions for lowest latency.
Frequently Asked Questions
Does streaming cost more?
No. Streaming is billed the same as non-streaming—cost is based on total tokens, not the delivery method.
When should I NOT use streaming?
For structured outputs (JSON mode) or when you need to parse the complete response before showing anything. Streaming can make validation harder.
How do I handle errors mid-stream?
Check for error events in the SSE stream. Display what was generated so far, show an error message, and offer a retry option.