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 job degree, you get an in depth perspective of prices related along with your venture. Let’s discover methods 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 ultimate for light-weight duties that don’t require processing giant 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 functions, combining a decrease price 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, shouldn’t be a multimodal possibility and solely processes textual content enter, providing a center floor by way of price and performance.
For decreased prices you may contemplate Batch API which is charged 50% much less on each Enter Tokens and Output Tokens. Cached Inputs additionally assist scale back prices:
Cached Inputs: Cached inputs confer with tokens which have been beforehand processed by the mannequin, permitting for quicker 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 possibly can all the time test your OpenAI dashboard to trace your utilization and test exercise to see the variety of requests despatched: OpenAI Platform.
Let’s give attention to monitoring it per request to get a task-level thought. Let’s ship a number 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 is the largest metropolis in India?"
# Initialize an empty record to retailer outcomes
outcomes = []
# Loop by way of 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 (price 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({
"Mannequin": model_name,
"Enter Tokens": input_tokens,
"Output Tokens": output_tokens,
"Whole price": total_cost,
"Response": response_content
})
import pandas as pd
# show the leads to 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 depends on each enter tokens and output tokens and we are able to see that regardless of GPT-4o-mini producing 47 tokens for the query “What’s the biggest metropolis 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 spot that the enter tokens are completely different regardless of the immediate being the identical as they use a special tokenizer.
The right way to scale back prices?
Set an higher restrict on Max Tokens
query = "Clarify 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 scale back prices and this can even let the mannequin focus extra on the reply. However selecting an acceptable 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",
"methodology": "POST",
"url": "/v1/chat/completions",
"physique": {
"mannequin": "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 material/batch_request_file.jsonl"
with open(request_file, 'w') as f:
f.write(json.dumps(input_dict))
f.write('n')
print(f"Efficiently wrote a dictionary to {request_file}.")Sending a Batch Request utilizing ‘shopper.batches.create’
from openai import OpenAI
shopper = OpenAI(api_key = "API-KEY")
batch_input_file = shopper.information.create(
file=open(request_file, "rb"),
function="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-Check"
}
)Checking the standing of the batch, it may possibly take as much as 24 hours to get the response. If the variety of requests or batches are much less it ought 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, complete=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 outcomes saved to batch_output.jsonl")That is the response I obtained within the JSONL file:
"content material": "A tokenizer is a device or course of utilized in pure language
processing (NLP) and textual content evaluation that splits a stream of textual content into
smaller, manageable items known as tokens. These tokens can symbolize numerous
knowledge models equivalent to phrases, phrases, symbols, or different significant parts in
the textual content.nnThe strategy of tokenization is essential for numerous NLP
functions, together with:nn1. **Textual content Evaluation**: Breaking down textual content into
elements makes it simpler to research, permitting for duties like frequency
evaluation, sentiment evaluation, and extra"
Conclusion
Understanding and managing ChatGPT API Value is crucial 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 steadiness efficiency and affordability. Among the many choices, GPT-4o-mini is an economical mannequin for many of the duties, whereas GPT-4o presents a strong but economical different for high-volume functions 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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Continuously Requested Questions
Ans. You’ll be able to scale 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. You may as well set an e-mail alert for whenever you method your price range and monitor utilization by way of the monitoring dashboard.
Ans. Sure, Playground utilization is taken into account the identical as common API utilization.
Ans. Examples embrace gpt-4-vision-preview, gpt-4-turbo, gpt-4o and gpt-4o-mini which course of and analyze each textual content and pictures for numerous duties.
I am a tech fanatic, graduated from Vellore Institute of Expertise. I am working as a Information Science Trainee proper now. I’m very a lot interested by Deep Studying and Generative AI.
