Time to first token
Time to first token is the elapsed time between submitting a language-model generation request and receiving its first generated output token.
Machine Learning Platform Engineering
Time-to-first-token is the elapsed time from a request until the first generated token is delivered to the user.
- How it is used
- Evaluates user-perceived responsiveness in streaming LLM applications rather than relying only on total response latency.
- How deep it goes
- Defines the metric and contrasts it with total generation time, but does not detail its internal timing instrumentation.
- Examples
- Uses streaming-versus-batch comparisons and Langfuse monitoring of time to first output token.
- What you can do after
- Evaluate initial LLM responsiveness and monitor it through Langfuse instrumentation.
Read how Machine Learning Platform Engineering explains time to first token →
Generative AI in Action
First token latency is the elapsed time from a request until the first generated token is returned by a generative model.
- How it is used
- Frames first-token responsiveness as a core operational metric for developing and deploying generative AI applications.
- How deep it goes
- Explains the timing boundary and major influences, then stops short of detailed model-internal performance analysis.
- Examples
- Uses Azure OpenAI REST measurement, streaming requests, concurrent calls, and contrasting short and long poem-generation workloads.
- What you can do after
- Measure it via a streaming Azure OpenAI API test and interpret results alongside prompt size, model size, TPOT, and throughput.
Read how Generative AI in Action explains time to first token →
LLMs in Production
Time to first token is the elapsed time between submitting a language-model request and receiving its first generated token.
- How it is used
- Measures perceived responsiveness for streaming language-model services during load testing.
- How deep it goes
- Defines TTFT as delay until feedback and computes it in a Locust streaming test, without prescribing a TTFT target.
- Examples
- Uses a Locust streaming request to record timestamps and export TTFT, latency, and TPS to CSV.
- What you can do after
- Diagnose perceived responsiveness by measuring TTFT alongside latency and tokens per second in a Locust load test.
Related concepts
- meancontrasts with · A mean is a measure of central tendency, calculated as the arithmetic average of values or, for a random variable, as the probability-weighted average of its possible values.
- language modelmeasures · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
- streamingmeasures · Streaming is the incremental handling of a sequence of data items, including their processing, transfer, or consumption as they become available.
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