Do you employ GPT-4o, GPT-4o Mini, or GPT-3.5 Turbo? Understanding the prices related to every mannequin is essential for managing your price range successfully. By monitoring utilization on the process stage, you get an in depth perspective of prices related along with your undertaking. Let’s discover tips on how to monitor and handle your OpenAI API Worth utilization effectively within the following sections.
OpenAI API Worth
These are the costs per 1 million tokens:
| Mannequin | Enter Tokens (per 1M) | Output Tokens (per 1M) |
| GPT-3.5-Turbo | $3.00 | $6.00 |
| GPT-4 | $30.00 | $60.00 |
| GPT-4o | $2.50 | $10.00 |
| GPT-4o-mini | $0.15 | $0.60 |
- GPT-4o-mini is probably the most reasonably priced possibility, costing considerably lower than the opposite fashions, with a context size of 16k, making it excellent for light-weight duties that don’t require processing massive quantities of enter or output tokens.
- GPT-4 is the most costly mannequin, with a context size of 32k, offering unmatched efficiency for duties requiring in depth input-output interactions or complicated reasoning.
- GPT-4o presents a balanced possibility for high-volume purposes, combining a decrease value with a bigger context size of 128k, making it appropriate for duties requiring detailed, high-context processing at scale.
- GPT-3.5-Turbo, with a context size of 16k, just isn’t a multimodal possibility and solely processes textual content enter, providing a center floor by way of value and performance.
For diminished prices you possibly can think about Batch API which is charged 50% much less on each Enter Tokens and Output Tokens. Cached Inputs additionally assist cut back prices:
Cached Inputs: Cached inputs discuss with tokens which have been beforehand processed by the mannequin, permitting for sooner and cheaper reuse in subsequent requests. It reduces Enter Tokens prices by 50%.
Batch API: The Batch API permits for submitting a number of requests collectively, processing them in bulk and provides the response inside a 24-hour window.
Prices in Precise Utilization
You might all the time examine your OpenAI dashboard to trace your utilization and examine exercise to see the variety of requests despatched: OpenAI Platform.
Let’s give attention to monitoring it per request to get a task-level concept. Let’s ship a couple of prompts to the fashions and estimate the price incurred.
from openai import OpenAI
# Initialize the OpenAI shopper
shopper = OpenAI(api_key = "API-KEY")
# Fashions and prices per 1M tokens
fashions = [
{"name": "gpt-3.5-turbo", "input_cost": 3.00, "output_cost": 6.00},
{"name": "gpt-4", "input_cost": 30.00, "output_cost": 60.00},
{"name": "gpt-4o", "input_cost": 2.50, "output_cost": 10.00},
{"name": "gpt-4o-mini", "input_cost": 0.15, "output_cost": 0.60}
]
# A query to ask the fashions
query = "What's the largest city in India?"
# Initialize an empty record to retailer outcomes
outcomes = []
# Loop via every mannequin and ship the request
for mannequin in fashions:
completion = shopper.chat.completions.create(
mannequin=mannequin["name"],
messages=[
{"role": "user", "content": question}
]
)
# Extract the response content material and token utilization from the completion
response_content = completion.selections[0].message.content material
input_tokens = completion.utilization.prompt_tokens
output_tokens = completion.utilization.completion_tokens
total_tokens = completion.utilization.total_tokens
model_name = completion.mannequin
# Calculate the price based mostly on token utilization (value per million tokens)
input_cost = (input_tokens / 1_000_000) * mannequin["input_cost"]
output_cost = (output_tokens / 1_000_000) * mannequin["output_cost"]
total_cost = input_cost + output_cost
# Append the outcome to the outcomes record
outcomes.append({
"Model": model_name,
"Input Tokens": input_tokens,
"Output Tokens": output_tokens,
"Total cost": total_cost,
"Response": response_content
})
import pandas as pd
# show the ends in a desk format
df = pd.DataFrame(outcomes)
df
The prices are $ 0.000093, $ 0.001050, $ 0.000425, $ 0.000030 for GPT-3.5-Turbo, GPT-4, GPT-4o and GPT-4o-mini respectively. The associated fee relies on each enter tokens and output tokens and we will see that regardless of GPT-4o-mini producing 47 tokens for the query “What’s the largest city in India” it’s the most affordable amongst all the opposite fashions right here.
Word: Tokens are a sequence of characters they usually’re not precisely phrases and see that the enter tokens are completely different regardless of the immediate being the identical as they use a special tokenizer.
cut back prices?
Set an higher restrict on Max Tokens
query = "Explain VAE?"
completion = shopper.chat.completions.create(
mannequin="gpt-4o-mini-2024-07-18",
messages=[
{"role": "user", "content": question}
],
max_tokens=50 # Set the specified higher restrict for output tokens
)
print("Output Tokens: ",completion.utilization.completion_tokens, "n")
print("Output: ", completion.selections[0].message.content material)Limiting the output tokens helps cut back prices and this will even let the mannequin focus extra on the reply. However selecting an applicable quantity for the restrict is essential right here.
Batch API
Utilizing Batch API reduces prices by 50% on each Enter Tokens and Output Tokens, the one trade-off right here is that it takes a while to get the responses (It may be as much as 24 hours relying on the variety of requests).
query="What's a tokenizer"Making a dictionary with request parameters for a POST request.
input_dict = {
"custom_id": f"request-1",
"method": "POST",
"url": "/v1/chat/completions",
"body": {
"model": "gpt-4o-mini-2024-07-18",
"messages": [
{
"role": "user",
"content": question
}
],
"max_tokens": 100
}
}Writing the serialized input_dict to a JSONL file.
import json
request_file = "/content/batch_request_file.jsonl"
with open(request_file, 'w') as f:
f.write(json.dumps(input_dict))
f.write('n')
print(f"Successfully wrote a dictionary to {request_file}.")Sending a Batch Request utilizing ‘client.batches.create’
from openai import OpenAI
shopper = OpenAI(api_key = "API-KEY")
batch_input_file = shopper.information.create(
file=open(request_file, "rb"),
objective="batch"
)
batch_input_file_id = batch_input_file.id
input_batch = shopper.batches.create(
input_file_id=batch_input_file_id,
endpoint="/v1/chat/completions",
completion_window="24h",
metadata={
"description": "GPT4o-Mini-Test"
}
)Checking the standing of the batch, it may take as much as 24 hours to get the response. If the variety of requests or batches are much less it needs to be fast sufficient (like on this instance).
status_response = shopper.batches.retrieve(input_batch.id)
print(input_batch.id,status_response.standing, status_response.request_counts)
accomplished BatchRequestCounts(accomplished=1, failed=0, whole=1)
if status_response.standing == 'accomplished':
output_file_id = status_response.output_file_id
# Retrieve the content material of the output file
output_response = shopper.information.content material(output_file_id)
output_content = output_response.content material
# Write the content material to a file
with open('/content material/batch_output.jsonl', 'wb') as f:
f.write(output_content)
print("Batch results saved to batch_output.jsonl")That is the response I obtained within the JSONL file:
"content": "A tokenizer is a tool or process used in natural language
processing (NLP) and text analysis that splits a stream of text into
smaller, manageable pieces called tokens. These tokens can represent various
data units such as words, phrases, symbols, or other meaningful elements in
the text.nnThe process of tokenization is crucial for various NLP
applications, including:nn1. **Text Analysis**: Breaking down text into
components makes it easier to analyze, allowing for tasks like frequency
analysis, sentiment analysis, and more"
Conclusion
Understanding and managing ChatGPT API Value is important for maximizing the worth of OpenAI’s fashions in your tasks. By analyzing token utilization and model-specific pricing, you can also make knowledgeable choices to stability efficiency and affordability. Among the many choices, GPT-4o-mini is a cheap mannequin for a lot of the duties, whereas GPT-4o presents a strong but economical different for high-volume purposes because it has an even bigger context size at 128k. Batch API is one other useful different to assist save prices for bulk processing for non-urgent duties.
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Ceaselessly Requested Questions
Ans. You’ll be able to cut back prices by setting an higher restrict on Max Tokens, utilizing Batch API for bulk processing
Ans. Set a month-to-month price range in your billing settings to cease requests as soon as the restrict is reached. It’s also possible to set an e mail alert for if you method your price range and monitor utilization via the monitoring dashboard.
Ans. Sure, Playground utilization is taken into account the identical as common API utilization.
Ans. Examples embody gpt-4-vision-preview, gpt-4-turbo, gpt-4o and gpt-4o-mini which course of and analyze each textual content and pictures for varied duties.
I am a tech fanatic, graduated from Vellore Institute of Know-how. I am working as a Knowledge Science Trainee proper now. I’m very a lot fascinated by Deep Studying and Generative AI.
