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

1 import requests
2 import time
3 
4 base_url = "https://api.assemblyai.com"
5 headers = {"authorization": "YOUR_API_KEY"}
6 
7 # Transcribe audio file
8 audio_url = "https://assembly.ai/wildfires.mp3"
9 data = {"audio_url": audio_url, "speech_models": ["universal-3-pro"]}
10 
11 response = requests.post(base_url + "/v2/transcript", headers=headers, json=data)
12 transcript_id = response.json()["id"]
13 polling_endpoint = base_url + f"/v2/transcript/{transcript_id}"
14 
15 # Poll for completion
16 print("Waiting for transcription to complete...")
17 
18 while True:
19     transcript = requests.get(polling_endpoint, headers=headers).json()
20     if transcript["status"] == "completed":
21         break
22     elif transcript["status"] == "error":
23         raise RuntimeError(f"Transcription failed: {transcript['error']}")
24     time.sleep(3)
25 
26 # Define your prompt
27 prompt = "Provide a brief summary of the transcript."
28 
29 # Calculate character count (transcript + prompt)
30 transcript_chars = len(transcript["text"])
31 prompt_chars = len(prompt)
32 total_chars = transcript_chars + prompt_chars
33 print(f"\nTotal characters: {total_chars}")
34 
35 # Estimate tokens (roughly 4 characters = 1 token)
36 estimated_tokens = total_chars / 4
37 tokens_in_millions = estimated_tokens / 1_000_000
38 
39 # Calculate input costs for different models
40 gpt5_cost = 1.25 * tokens_in_millions
41 claude_sonnet_cost = 3.00 * tokens_in_millions
42 gemini_pro_cost = 1.25 * tokens_in_millions
43 
44 print(f"Estimated input tokens: {estimated_tokens:,.0f}")
45 print(f"\nEstimated input costs:")
46 print(f"GPT-5: ${gpt5_cost:.4f}")
47 print(f"Claude 4.5 Sonnet: ${claude_sonnet_cost:.4f}")
48 print(f"Gemini 2.5 Pro: ${gemini_pro_cost:.4f}")

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

1 import requests
2 import time
3 
4 base_url = "https://api.assemblyai.com"
5 headers = {"authorization": "YOUR_API_KEY"}

Transcribe your audio file

Transcribe your audio file using AssemblyAI:

Python

JavaScript

1 audio_url = "https://assembly.ai/wildfires.mp3"
2 data = {"audio_url": audio_url, "speech_models": ["universal-3-pro"]}
3 
4 response = requests.post(base_url + "/v2/transcript", headers=headers, json=data)
5 transcript_id = response.json()["id"]
6 polling_endpoint = base_url + f"/v2/transcript/{transcript_id}"
7 
8 # Poll for completion
9 print("Waiting for transcription to complete...")
10 
11 while True:
12     transcript = requests.get(polling_endpoint, headers=headers).json()
13     if transcript["status"] == "completed":
14         break
15     elif transcript["status"] == "error":
16         raise RuntimeError(f"Transcription failed: {transcript['error']}")
17     time.sleep(3)

Calculate character count

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

Python

JavaScript

1 # Define your prompt
2 prompt = "Provide a brief summary of the transcript."
3 
4 # Calculate character count (transcript + prompt)
5 transcript_chars = len(transcript["text"])
6 prompt_chars = len(prompt)
7 total_chars = transcript_chars + prompt_chars
8 print(f"\nTotal characters: {total_chars}")

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

1 # Estimate tokens (roughly 4 characters = 1 token)
2 estimated_tokens = total_chars / 4
3 tokens_in_millions = estimated_tokens / 1_000_000
4 
5 print(f"Estimated input tokens: {estimated_tokens:,.0f}")

Language considerations

Token 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

1 # Calculate input costs for different models (rates per 1M tokens)
2 gpt5_cost = 1.25 * tokens_in_millions
3 claude_sonnet_cost = 3.00 * tokens_in_millions
4 gemini_pro_cost = 1.25 * tokens_in_millions
5 
6 print(f"\nEstimated input costs:")
7 print(f"GPT-5: ${gpt5_cost:.4f}")
8 print(f"Claude 4.5 Sonnet: ${claude_sonnet_cost:.4f}")
9 print(f"Gemini 2.5 Pro: ${gemini_pro_cost:.4f}")

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

GPT-5 (gpt-5): ~$0.0015
Claude 4.5 Sonnet (claude-sonnet-4-5-20250929): ~$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

1	import requests
2	import time
3
4	base_url = "https://api.assemblyai.com"
5	headers = {"authorization": "YOUR_API_KEY"}
6
7	# Transcribe audio file
8	audio_url = "https://assembly.ai/wildfires.mp3"
9	data = {"audio_url": audio_url, "speech_models": ["universal-3-pro"]}
10
11	response = requests.post(base_url + "/v2/transcript", headers=headers, json=data)
12	transcript_id = response.json()["id"]
13	polling_endpoint = base_url + f"/v2/transcript/{transcript_id}"
14
15	# Poll for completion
16	print("Waiting for transcription to complete...")
17
18	while True:
19	transcript = requests.get(polling_endpoint, headers=headers).json()
20	if transcript["status"] == "completed":
21	break
22	elif transcript["status"] == "error":
23	raise RuntimeError(f"Transcription failed: {transcript['error']}")
24	time.sleep(3)
25
26	# Define your prompt
27	prompt = "Provide a brief summary of the transcript."
28
29	# Calculate character count (transcript + prompt)
30	transcript_chars = len(transcript["text"])
31	prompt_chars = len(prompt)
32	total_chars = transcript_chars + prompt_chars
33	print(f"\nTotal characters: {total_chars}")
34
35	# Estimate tokens (roughly 4 characters = 1 token)
36	estimated_tokens = total_chars / 4
37	tokens_in_millions = estimated_tokens / 1_000_000
38
39	# Calculate input costs for different models
40	gpt5_cost = 1.25 * tokens_in_millions
41	claude_sonnet_cost = 3.00 * tokens_in_millions
42	gemini_pro_cost = 1.25 * tokens_in_millions
43
44	print(f"Estimated input tokens: {estimated_tokens:,.0f}")
45	print(f"\nEstimated input costs:")
46	print(f"GPT-5: ${gpt5_cost:.4f}")
47	print(f"Claude 4.5 Sonnet: ${claude_sonnet_cost:.4f}")
48	print(f"Gemini 2.5 Pro: ${gemini_pro_cost:.4f}")