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LLM Latency Estimator

Estimate time to first token, generation time and total latency for current LLMs from your token counts, with a UX pattern for each wait.

LLM Latency Estimator

TTFB
910ms
Time to first byte
Generation
1.4s
Token generation
Total latency
2.3s
End-to-end
Speed
70
tokens/sec
UX recommendation
Stream response — user sees content immediately
Streaming first byte at 910ms, complete at 2.3s

Model comparison

UX hint
claude-haiku-4-5Anthropic260ms556ms816ms180Spinner
gpt-6-lunaOpenAI260ms556ms816ms180Spinner
gemini-3.5-flash-liteGoogle260ms556ms816ms180Spinner
deepseek-flashDeepSeek260ms556ms816ms180Spinner
mistral-small-2603Mistral260ms556ms816ms180Spinner
qwen3.8-flashQwen260ms556ms816ms180Spinner
openai/gpt-oss-120bGroq260ms556ms816ms180Spinner
claude-sonnet-5-5Anthropic510ms833ms1.3s120Spinner
gpt-6.1-solOpenAI510ms833ms1.3s120Spinner
gpt-5.4-miniOpenAI510ms833ms1.3s120Spinner
gemini-3.8-flashGoogle510ms833ms1.3s120Spinner
deepseek-v4-proDeepSeek510ms833ms1.3s120Spinner
grok-4.3xAI510ms833ms1.3s120Spinner
mistral-large-2512Mistral510ms833ms1.3s120Spinner
muse-spark-1.3Meta510ms833ms1.3s120Spinner
claude-fable-5-1Anthropic910ms1.4s2.3s70Stream
claude-opus-5-5Anthropic910ms1.4s2.3s70Stream
gpt-6-astraOpenAI910ms1.4s2.3s70Stream
gpt-5.5OpenAI910ms1.4s2.3s70Stream
gemini-3.1-pro-previewGoogle910ms1.4s2.3s70Stream
grok-4.7xAI910ms1.4s2.3s70Stream
mistral-medium-3504Mistral910ms1.4s2.3s70Stream
qwen3.8-maxQwen910ms1.4s2.3s70Stream

Estimates based on typical API latencies. Actual performance varies by load, region, prompt complexity, and provider infrastructure. TTFB includes additional 10ms for 500 input token processing overhead.

Updated . Provided as is. Check the output before you rely on it in production.

How to use LLM Latency Estimator

  1. 1

    Select a model

    Choose one of the current models offered across OpenAI, Anthropic, Google, xAI, DeepSeek, Mistral and Groq. Speed assumptions follow each model's price tier.

  2. 2

    Enter token counts

    Set the expected input token count (your prompt) and output token count (the response), or use a quick preset for common scenarios.

  3. 3

    Read the latency estimate

    See the estimated time to first token, generation time and total latency. The UX badge suggests whether to stream, show a spinner or run a background job.

  4. 4

    Compare across models

    The comparison table runs the same input and output sizes across every model, sorted from fastest to slowest.

Questions and answers

What is LLM Latency Estimator?
LLM latency is the wait from sending a request to receiving the full response: time to first token plus output tokens divided by generation speed. This estimator applies a starting assumption per price tier to each current model, adjusts for input length, ranks models by total time and suggests a UX pattern.
How accurate are the latency estimates?
They are planning estimates, not measurements. Each model gets a starting assumption for its price tier (premium, mid or budget). Real latency varies with load, region and prompt shape, so measure your own traffic before committing to a UX.
Does it send data to a server?
No. All calculations run in your browser.
What UX recommendations does it provide?
Under 500 ms needs no loading indicator, 500 ms to 2 s a spinner or skeleton, 2 to 10 s a streamed response, 10 to 30 s streaming with a progress indicator, and over 30 s a background job with a notification.
For AI agents: how to call this tool

Machine-readable contract, endpoints and examples. Humans can ignore this section.

Best Path For Builders

Browser workflow

Runs instantly in the browser with private local processing and copy/export-ready output.

Browser Workflow

This tool is optimized for instant in-browser execution with local data handling. Run it here and copy/export the output directly.

/llm-latency-estimator/

For automation planning, fetch the canonical contract at /api/tool/llm-latency-estimator.json.