Latency Estimator
Estimate AI API response latency based on model and request size
Request Configuration
Latency Breakdown
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Understanding AI API Latency
API latency is the total time from sending a request to receiving a response. For AI APIs, latency depends on model size, input/output length, server load, and network conditions. Understanding latency helps you design better user experiences and set appropriate timeouts.
This latency estimator helps you predict response times for different AI models and request sizes. Use it to plan for user experience, set SLAs, and choose the right model for latency-sensitive applications.
Components of AI API Latency
📥 Input Processing
Time to tokenize and process your prompt. Scales with input length but is generally fast.
📤 Output Generation
Time to generate tokens one by one. Usually the largest latency component—scales linearly with output length.
🌐 Network Round-Trip
Time for data to travel between your server and the API. Varies by location and connection quality.
Model Speed Comparison
| Model | Speed | Best For |
|---|---|---|
| GPT-3.5 Turbo | ~100 tok/s | Real-time chat, high volume |
| Claude 3 Haiku | ~120 tok/s | Low-latency applications |
| GPT-4 Turbo | ~80 tok/s | Balance of speed and quality |
| GPT-4 / Claude Opus | ~30-40 tok/s | Complex reasoning (async OK) |
Latency Optimization Tips
- Use streaming: Stream responses for perceived faster UX—users see tokens as they generate.
- Limit output: Set max_tokens to reasonable limits. Shorter outputs = faster responses.
- Choose faster models: Use GPT-3.5/Haiku for speed-critical paths, reserve GPT-4/Opus for quality.
- Reduce input: Trim context, use summarization, avoid unnecessary system prompts.
- Regional endpoints: Use endpoints closest to your servers to minimize network latency.
Frequently Asked Questions
Are these estimates accurate?
These are approximations based on typical performance. Actual latency varies with server load, time of day, and specific request content. Real benchmarks may differ 20-30%.
Why is output slower than input?
LLMs generate tokens sequentially—each token depends on previous ones. Input can be processed in parallel. That's why long outputs are latency-intensive.
What timeout should I set?
Set timeouts at 2-3x your expected latency. For a 2000ms expected response, use 5-6 second timeouts. Always implement retry logic with exponential backoff.