Estimate Input Token Costs for LLM Gateway

AssemblyAI’s LLM Gateway is a unified API providing access to 25+ models from Claude, GPT, Gemini, and more through a single interface. It’s a powerful way to extract insights from transcripts generated from audio and video files. Given how varied the type of input and output could be for these use cases, the pricing for LLM Gateway is based on both input and output tokens. Output tokens will vary depending on the model and the complexity of your request, but how do you determine the amount of input tokens you’ll be sending to LLM Gateway? How many tokens does an audio file and your prompt contain? This guide will show you how to roughly calculate that information to help predict LLM Gateway’s input token cost ahead of time.

This guide calculates input token costs only. Output token costs will vary based on the model used and the length of the generated response.To see the specific cost of each model (per 1M input and output tokens) applicable to your AssemblyAI account, refer to the Rates table on the Billing page of the dashboard.

Quickstart

Python
JavaScript

import requests
import time

base_url = "https://api.assemblyai.com"
headers = {"authorization": "YOUR_API_KEY"}

# Transcribe audio file
audio_url = "https://assembly.ai/wildfires.mp3"
data = {"audio_url": audio_url}

response = requests.post(base_url + "/v2/transcript", headers=headers, json=data)
transcript_id = response.json()["id"]
polling_endpoint = base_url + f"/v2/transcript/{transcript_id}"

# Poll for completion
print("Waiting for transcription to complete...")

while True:
    transcript = requests.get(polling_endpoint, headers=headers).json()
    if transcript["status"] == "completed":
        break
    elif transcript["status"] == "error":
        raise RuntimeError(f"Transcription failed: {transcript['error']}")
    time.sleep(3)

# Define your prompt
prompt = "Provide a brief summary of the transcript."

# Calculate character count (transcript + prompt)
transcript_chars = len(transcript["text"])
prompt_chars = len(prompt)
total_chars = transcript_chars + prompt_chars
print(f"\nTotal characters: {total_chars}")

# Estimate tokens (roughly 4 characters = 1 token)
estimated_tokens = total_chars / 4
tokens_in_millions = estimated_tokens / 1_000_000

# Calculate input costs for different models
gpt5_cost = 1.25 * tokens_in_millions
claude_sonnet_cost = 3.00 * tokens_in_millions
gemini_pro_cost = 1.25 * tokens_in_millions

print(f"Estimated input tokens: {estimated_tokens:,.0f}")
print(f"\nEstimated input costs:")
print(f"GPT-5: ${gpt5_cost:.4f}")
print(f"Claude Sonnet 4.6: ${claude_sonnet_cost:.4f}")
print(f"Gemini 2.5 Pro: ${gemini_pro_cost:.4f}")

const baseUrl = "https://api.assemblyai.com";
const headers = { authorization: "YOUR_API_KEY" };

// Transcribe audio file
const audioUrl = "https://assembly.ai/wildfires.mp3";
const data = { audio_url: audioUrl };

let res = await fetch(`${baseUrl}/v2/transcript`, {
  method: "POST",
  headers: { ...headers, "Content-Type": "application/json" },
  body: JSON.stringify(data),
});
if (!res.ok) throw new Error(`Error: ${res.status}`);
const transcriptResponse = await res.json();
const transcriptId = transcriptResponse.id;
const pollingEndpoint = `${baseUrl}/v2/transcript/${transcriptId}`;

// Poll for completion
console.log("Waiting for transcription to complete...");

let transcript;
while (true) {
  res = await fetch(pollingEndpoint, { headers });
  if (!res.ok) throw new Error(`Error: ${res.status}`);
  transcript = await res.json();
  if (transcript.status === "completed") {
    break;
  } else if (transcript.status === "error") {
    throw new Error(`Transcription failed: ${transcript.error}`);
  }
  await new Promise((resolve) => setTimeout(resolve, 3000));
}

// Define your prompt
const prompt = "Provide a brief summary of the transcript.";

// Calculate character count (transcript + prompt)
const transcriptChars = transcript.text.length;
const promptChars = prompt.length;
const totalChars = transcriptChars + promptChars;
console.log(`\nTotal characters: ${totalChars}`);

// Estimate tokens (roughly 4 characters = 1 token)
const estimatedTokens = totalChars / 4;
const tokensInMillions = estimatedTokens / 1_000_000;

// Calculate input costs for different models
const gpt5Cost = 1.25 * tokensInMillions;
const claudeSonnetCost = 3.00 * tokensInMillions;
const geminiProCost = 1.25 * tokensInMillions;

console.log(`Estimated input tokens: ${estimatedTokens.toLocaleString("en-US", { maximumFractionDigits: 0 })}`);
console.log(`\nEstimated input costs:`);
console.log(`GPT-5: $${gpt5Cost.toFixed(4)}`);
console.log(`Claude Sonnet 4.6: $${claudeSonnetCost.toFixed(4)}`);
console.log(`Gemini 2.5 Pro: $${geminiProCost.toFixed(4)}`);

Step-by-Step Guide

Install dependencies

Install the required library:

Python

pip install requests

Set up your API key

Import the necessary libraries and set your AssemblyAI API key, which can be found on your account dashboard:

Python
JavaScript

import requests
import time

base_url = "https://api.assemblyai.com"
headers = {"authorization": "YOUR_API_KEY"}

const baseUrl = "https://api.assemblyai.com";
const headers = { authorization: "YOUR_API_KEY" };

Transcribe your audio file

Transcribe your audio file using AssemblyAI:

Python
JavaScript

audio_url = "https://assembly.ai/wildfires.mp3"
data = {"audio_url": audio_url}

response = requests.post(base_url + "/v2/transcript", headers=headers, json=data)
transcript_id = response.json()["id"]
polling_endpoint = base_url + f"/v2/transcript/{transcript_id}"

# Poll for completion
print("Waiting for transcription to complete...")

while True:
    transcript = requests.get(polling_endpoint, headers=headers).json()
    if transcript["status"] == "completed":
        break
    elif transcript["status"] == "error":
        raise RuntimeError(f"Transcription failed: {transcript['error']}")
    time.sleep(3)

const audioUrl = "https://assembly.ai/wildfires.mp3";
const data = { audio_url: audioUrl };

let res = await fetch(`${baseUrl}/v2/transcript`, {
  method: "POST",
  headers: { ...headers, "Content-Type": "application/json" },
  body: JSON.stringify(data),
});
if (!res.ok) throw new Error(`Error: ${res.status}`);
const transcriptResponse = await res.json();
const transcriptId = transcriptResponse.id;
const pollingEndpoint = `${baseUrl}/v2/transcript/${transcriptId}`;

// Poll for completion
console.log("Waiting for transcription to complete...");

let transcript;
while (true) {
  res = await fetch(pollingEndpoint, { headers });
  if (!res.ok) throw new Error(`Error: ${res.status}`);
  transcript = await res.json();
  if (transcript.status === "completed") {
    break;
  } else if (transcript.status === "error") {
    throw new Error(`Transcription failed: ${transcript.error}`);
  }
  await new Promise((resolve) => setTimeout(resolve, 3000));
}

Calculate character count

We’ll count the characters in both the transcript and your prompt:

Python
JavaScript

# Define your prompt
prompt = "Provide a brief summary of the transcript."

# Calculate character count (transcript + prompt)
transcript_chars = len(transcript["text"])
prompt_chars = len(prompt)
total_chars = transcript_chars + prompt_chars
print(f"\nTotal characters: {total_chars}")

// Define your prompt
const prompt = "Provide a brief summary of the transcript.";

// Calculate character count (transcript + prompt)
const transcriptChars = transcript.text.length;
const promptChars = prompt.length;
const totalChars = transcriptChars + promptChars;
console.log(`\nTotal characters: ${totalChars}`);

For this specific file with the example prompt, the transcript contains approximately 4,880 characters and the prompt contains 42 characters, for a total of 4,922 characters.

Estimate tokens

Different LLM providers use different tokenization methods, but a rough estimate is that 4 characters equals approximately 1 token. This is based on guidance from:

Python
JavaScript

# Estimate tokens (roughly 4 characters = 1 token)
estimated_tokens = total_chars / 4
tokens_in_millions = estimated_tokens / 1_000_000

print(f"Estimated input tokens: {estimated_tokens:,.0f}")

// Estimate tokens (roughly 4 characters = 1 token)
const estimatedTokens = totalChars / 4;
const tokensInMillions = estimatedTokens / 1_000_000;

console.log(`Estimated input tokens: ${estimatedTokens.toLocaleString("en-US", { maximumFractionDigits: 0 })}`);

Language considerationsToken counts can differ significantly across languages. Non-English languages typically require more tokens per character than English. For instance, text in languages like Spanish, Chinese, or Arabic may use 2-3 characters per token instead of 4, resulting in higher token costs for the same amount of content.

Calculate input token costs

LLM Gateway’s pricing is calculated per 1M input tokens. Here are the current rates for popular models:

Python
JavaScript

# Calculate input costs for different models (rates per 1M tokens)
gpt5_cost = 1.25 * tokens_in_millions
claude_sonnet_cost = 3.00 * tokens_in_millions
gemini_pro_cost = 1.25 * tokens_in_millions

print(f"\nEstimated input costs:")
print(f"GPT-5: ${gpt5_cost:.4f}")
print(f"Claude Sonnet 4.6: ${claude_sonnet_cost:.4f}")
print(f"Gemini 2.5 Pro: ${gemini_pro_cost:.4f}")

// Calculate input costs for different models (rates per 1M tokens)
const gpt5Cost = 1.25 * tokensInMillions;
const claudeSonnetCost = 3.00 * tokensInMillions;
const geminiProCost = 1.25 * tokensInMillions;

console.log(`\nEstimated input costs:`);
console.log(`GPT-5: $${gpt5Cost.toFixed(4)}`);
console.log(`Claude Sonnet 4.6: $${claudeSonnetCost.toFixed(4)}`);
console.log(`Gemini 2.5 Pro: $${geminiProCost.toFixed(4)}`);

For our example file with approximately 1,230 input tokens:

GPT-5 (gpt-5): ~$0.0015
Claude Sonnet 4.6 (claude-sonnet-4-6): ~$0.0037
Gemini 2.5 Pro (gemini-2.5-pro): ~$0.0015

These calculations estimate input token costs only. Output tokens are not included and will vary based on:

The model you choose
The complexity of your request
The length of the generated response

To see the complete pricing for both input and output tokens for all available models, visit the Rates table on the Billing page of your dashboard.

Next steps

LLM Gateway Overview - Learn about all available models and capabilities
Apply LLM Gateway to Audio Transcripts - Complete guide to using LLM Gateway with transcripts
Billing page - View LLM pricing for your account

Getting started

Features

API reference

Core concepts

Voice AI Guides

Estimate Input Token Costs for LLM Gateway

Quickstart

Step-by-Step Guide

Install dependencies

Set up your API key

Transcribe your audio file

Calculate character count

Estimate tokens

Calculate input token costs

Next steps

​Quickstart

​Step-by-Step Guide

​Install dependencies

​Set up your API key

​Transcribe your audio file

​Calculate character count

​Estimate tokens

​Calculate input token costs

​Next steps

Quickstart

Step-by-Step Guide

Install dependencies

Set up your API key

Transcribe your audio file

Calculate character count

Estimate tokens

Calculate input token costs

Next steps