Hello again, I see I can choose GPT-4-Turbo and other GPT-4 models. If using the API mode, what are the performance differences when I choose what platform to work on, and do you know the pricing difference too?
#Difference in price performance of GPT-4 models
ruby socket
frosty ravineBOT
Hi @ruby socket. I'm kapa.ai, your friendly support bot. I will try to answer your question by searching through the knowledge base. :wave:
OpenAI offers a range of GPT-4 models, each with its own set of features, capabilities, and pricing. Here are some key differences:
-
GPT-4 Turbo: This is the latest generation model. It has an updated knowledge cutoff of April 2023 and introduces a 128k context window. The model is 3X cheaper for input tokens and 2X cheaper for output tokens compared to the original GPT-4 model. The maximum number of output tokens for this model is 4096. You can access it by passing
gpt-4-turbo-previewas the model name in the API (source (https://help.openai.com/en/articles/8555510-gpt-4-turbo-in-the-openai-api)). -
GPT-4-0125-preview: This is the latest GPT-4 model intended to reduce cases of “laziness” where the model doesn’t complete a task. It returns a maximum of 4,096 output tokens and has a context window of 128,000 tokens. The training data is up to date until Dec 2023 (source (https://platform.openai.com/docs/models)).
-
GPT-4-1106-preview: This GPT-4 Turbo model features improved instruction following, JSON mode, reproducible outputs, parallel function calling, and more. It returns a maximum of 4,096 output tokens and has a context window of 128,000 tokens. The training data is up to date until Apr 2023 (source (https://platform.openai.com/docs/models)).
-
GPT-4-vision-preview: This is a GPT-4 model with the ability to understand images, in addition to all other GPT-4 Turbo capabilities. It has a context window of 128,000 tokens and the training data is up to date until Apr 2023 (source (https://platform.openai.com/docs/models)).
-
GPT-4: This model currently points to
gpt-4-0613and has a context window of 8,192 tokens. The training data is up to date until Sep 2021 (source (https://platform.openai.com/docs/models)).
- GPT-4-32k: This model currently points to
gpt-4-32k-0613and has a context window of 32,768 tokens. The training data is up to date until Sep 2021. This model was never rolled out widely in favor of GPT-4 Turbo (source (https://platform.openai.com/docs/models)).
The cost for each model varies. The GPT-4 Turbo model is less expensive than previous GPT-4 model variants. You can learn more about the pricing on the OpenAI Pricing page (https://openai.com/pricing) (source (https://help.openai.com/en/articles/7127966-what-is-the-difference-between-the-gpt-4-model-versions)).
In terms of performance, GPT-4 models are more capable in complex reasoning situations compared to previous models. However, for many basic tasks, the difference between GPT-4 and GPT-3.5 models is not significant (source (https://platform.openai.com/docs/models)).
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ruby socket
@frosty ravine so it seems like GPT-4 Turbo is one of the best options due to its more recent training and lower price. One would think it would be more expensive? On another note, what is JSON mode and how would that help me?
frosty ravineBOT
Let me check the knowledge base for you. One moment! :books:
Yes, GPT-4 Turbo is indeed a great option due to its more recent training data and lower price. It's part of OpenAI's commitment to making advanced AI technologies more accessible and affordable.
As for JSON mode, it's a feature that constrains the model to generate strings that parse into valid JSON objects. This can be particularly useful when you want the model's output to be in a structured format that's easy to manipulate programmatically.
Here's how it works:
- You enable JSON mode by setting
response_formatto{ "type": "json_object" }when making an API call (source (https://platform.openai.com/docs/guides/text-generation)). - When JSON mode is enabled, the model is instructed to produce JSON via a message in the conversation. If you don't include an explicit instruction to generate JSON, the model may generate an unending stream of whitespace and the request may run continually until it reaches the token limit (source (https://platform.openai.com/docs/guides/text-generation)).
- The JSON in the message the model returns may be partial if
finish_reasonislength, which indicates the generation exceededmax_tokensor the conversation exceeded the token limit. To guard against this, you should checkfinish_reasonbefore parsing the response (source (https://platform.openai.com/docs/guides/text-generation)). - JSON mode doesn't guarantee the output matches any specific schema, only that it is valid and parses without errors (source (https://platform.openai.com/docs/guides/text-generation)).
Here's an example of how to use JSON mode in Python:
from openai import OpenAI
client = OpenAI()response = client.chat.completions.create(
model="gpt-3.5-turbo-0125",
response_format={ "type": "json_object" },
messages=[
{"role": "system", "content": "You are a helpful assistant designed to output JSON."},
{"role": "user", "content": "Who won the world series in 2020?"}
]
)
print(response.choices[0].message.content)
In this example, the response includes a JSON object that looks something like the following:
```json
"content": "{\"winner\": \"Los Angeles Dodgers\"}"