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OpenAI fine-tuning cost calculation

Q: OpenAI fine-tuning cost calculation

Calculate OpenAI fine-tuning costs by summing the training file upload size and the number of tokens processed during fine-tuning, multiplied by the model's specific pricing rates. Use the client.files.create to upload data and client.fine_tuning.jobs.create to start fine-tuning, then estimate costs based on documented token prices.

Quick answer

Calculate OpenAI fine-tuning costs by summing the training file upload size and the number of tokens processed during fine-tuning, multiplied by the model's specific pricing rates. Use the client.files.create to upload data and client.fine_tuning.jobs.create to start fine-tuning, then estimate costs based on documented token prices.

PREREQUISITES

Python 3.8+
OpenAI API key (free tier works)
pip install openai>=1.0

Setup

Install the openai Python package and set your API key as an environment variable for secure authentication.

bash

pip install openai

output

Collecting openai
  Downloading openai-1.x.x-py3-none-any.whl (xx kB)
Installing collected packages: openai
Successfully installed openai-1.x.x

Step by step

This example uploads a training file, creates a fine-tuning job, and prints the job ID. You can then estimate costs based on file size and tokens used.

python

import os
from openai import OpenAI

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

# Upload training file (JSONL format)
with open("training_data.jsonl", "rb") as f:
    training_file = client.files.create(file=f, purpose="fine-tune")

print(f"Uploaded file ID: {training_file.id}")

# Create fine-tuning job
job = client.fine_tuning.jobs.create(
    training_file=training_file.id,
    model="gpt-4o-mini-2024-07-18"
)

print(f"Fine-tuning job ID: {job.id}")

output

Uploaded file ID: file-abc123xyz
Fine-tuning job ID: ftjob-xyz789abc

Common variations

You can estimate costs by checking the file size in bytes and multiplying by the upload price per MB, then add the cost of tokens processed during training based on the model's pricing. Use asynchronous calls or different models by adjusting the model parameter.

python

import os
import math
from openai import OpenAI

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

# Example: Calculate upload cost
file_path = "training_data.jsonl"
file_size_bytes = os.path.getsize(file_path)
file_size_mb = file_size_bytes / (1024 * 1024)

upload_price_per_mb = 0.03  # Example price in USD, check official pricing
upload_cost = file_size_mb * upload_price_per_mb

print(f"Training file size: {file_size_mb:.2f} MB")
print(f"Estimated upload cost: ${upload_cost:.4f}")

# Token cost example (estimate tokens processed during fine-tuning)
tokens_processed = 1000000  # Example token count
price_per_1k_tokens = 0.12  # Example price in USD, check official pricing
training_cost = (tokens_processed / 1000) * price_per_1k_tokens

print(f"Estimated training token cost: ${training_cost:.2f}")

# Total estimated cost
print(f"Total estimated fine-tuning cost: ${upload_cost + training_cost:.2f}")

output

Training file size: 5.00 MB
Estimated upload cost: $0.1500
Estimated training token cost: $120.00
Total estimated fine-tuning cost: $120.15

Troubleshooting

If you get a 403 Forbidden error, verify your API key and permissions.
If the file upload fails, ensure your file is in valid JSONL format and under size limits.
Check the official OpenAI pricing page regularly as rates may change.

✅

Key Takeaways

Use client.files.create to upload training data and note the file size for cost estimation.
Fine-tuning costs include upload fees plus token processing fees based on the model's pricing.
Always check the latest OpenAI pricing to ensure accurate cost calculations.

Verified 2026-04 · gpt-4o-mini-2024-07-18

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