How Prompt Size Affects LLM Response Time in Voice AI [Original Data & Research]

Delivering fast, seamless interactions is critical for customer satisfaction when using voice AI for customer service.

One of the most noticeable disruptions in an AI conversation is dead air - the pause between a user finishing their input and the AI responding.

Minimising this delay is a key challenge when optimising voice AI performance and the user experience.

While several factors contribute to dead air, one controllable variable is the size of the AI prompt sent to the large language model (LLM).

This raises the question: how much does prompt size affect response latency, and more specifically, the time to first token?

At Talkative, we decided to find out for ourselves - let’s explore what happened when we put this to the test.

TL;DR:

AI prompt size has a small but measurable impact on large language model (LLM) response time in voice AI applications.

In our tests using GPT-4o, larger prompts led to longer time-to-first-token (TTFT), adding to the "dead air" that disrupts natural conversation flow. On average, every additional 500 tokens in a prompt increased latency by 20–30 milliseconds.

However, prompt size is just one factor - network conditions, server load, and system-level optimisations also play critical roles. To improve CX in real-time voice interactions, reducing prompt size can help, but broader performance tuning is essential.

Why Time to First Token (TTFT) matters in voice AI interactions

In real-time conversational experiences, especially voice-based interactions, users expect responses to feel natural and instant.

The time to first token (TTFT) - how long it takes an LLM like GPT-4o to begin generating a response - is a key factor in perceived latency.

Even minor delays can disrupt the flow of a human conversation, making the AI seem unresponsive or robotic.

For voice AI applications in contact centres, optimising this metric is essential to delivering a more engaging and effective customer experience.

Testing the impact of prompt size on latency

To understand how prompt size impacts LLM latency, we ran a series of tests using OpenAI's GPT-4o model.

Each test involved sending a unique prompt of varying size to the OpenAI API and measuring the time to first token.

Test parameters:

• Model tested: GPT-4o
• Metrics captured: Time to first token (in milliseconds)
• Prompt sizes: Ranged from 280 to 13,883 tokens
• Sample size: 200 requests per prompt size
• Purpose: Eliminate caching effects and get statistically meaningful averages

While we controlled the prompt size, several variables - like server load, network latency, and OpenAI’s internal queuing - remained out of our control.

These external factors can introduce additional noise, but our testing still revealed clear trends.

Key findings: Prompt Size vs. Response Time

Our data shows a slight but consistent increase in response latency as prompt size grows.

As you can see in the table below, the median response time rises along with prompt size.

On average, every additional 500 tokens in the AI prompt adds around 20–30 milliseconds of latency.

What does this mean for voice AI applications?

While it is clear that prompt size contributes to response speed, it is not the only factor.

Even with small prompts, a request to OpenAI will add ~800ms of dead air time to AI interactions.

This suggests that while minimising prompt size can help, teams must also consider other elements like:

• Network optimisation
• Server selection and load balancing
• Preprocessing and prompt engineering

Together, these tactics can help reduce dead air and deliver faster, more human-like conversations.

The takeaway

If you’re leveraging conversational voice AI technology, particularly for real-time customer support, prompt size does play a role in response latency.

That said, AI prompts are just one part of a larger performance puzzle.

While limiting prompt size for voice AI is important to minimise dead air time, our results suggest that it may not have as significant an impact as initially anticipated.

These findings suggest that while controlling prompt size can improve response time, other factors like network latency and server load also play crucial roles in optimising Voice AI performance.

By understanding and optimising time to first token, alongside other system-level improvements, you can significantly enhance response speed, improve CX, and deliver more natural voice interactions.

Want to learn more about this research or see voice AI in action?

Reach out to our team with any questions or book a demo with us today.