We released gpt-3.5-turbo and gpt-4 earlier this year, and in only a short few months, have seen incredible applications built by developers on top of these models.

Today, we’re following up with some exciting updates:

• new function calling capability in the Chat Completions API
• updated and more steerable versions of gpt-4 and gpt-3.5-turbo
• new 16k context version of gpt-3.5-turbo (vs the standard 4k version)
• 75% cost reduction on our state-of-the-art embeddings model
• 25% cost reduction on input tokens for gpt-3.5-turbo
• announcing the deprecation timeline for the gpt-3.5-turbo-0301 and gpt-4-0314 models

All of these models come with the same data privacy and security guarantees we introduced on March 1 — customers own all outputs generated from their requests and their API data will not be used for training.

Developers can now describe functions to gpt-4-0613 and gpt-3.5-turbo-0613, and have the model intelligently choose to output a JSON object containing arguments to call those functions. This is a new way to more reliably connect GPT's capabilities with external tools and APIs.

These models have been fine-tuned to both detect when a function needs to be called (depending on the user’s input) and to respond with JSON that adheres to the function signature. Function calling allows developers to more reliably get structured data back from the model. For example, developers can:

• Create chatbots that answer questions by calling external tools (e.g., like ChatGPT Plugins)

Convert queries such as “Email Anya to see if she wants to get coffee next Friday” to a function call like send_email(to: string, body: string), or “What’s the weather like in Boston?” to get_current_weather(location: string, unit: 'celsius' | 'fahrenheit').

• Convert natural language into API calls or database queries

Convert “Who are my top ten customers this month?” to an internal API call such as get_customers_by_revenue(start_date: string, end_date: string, limit: int), or “How many orders did Acme, Inc. place last month?” to a SQL query using sql_query(query: string).

• Extract structured data from text

Define a function called extract_people_data(people: [{name: string, birthday: string, location: string}]), to extract all people mentioned in a Wikipedia article.

These use cases are enabled by new API parameters in our /v1/chat/completions endpoint, functions and function_call, that allow developers to describe functions to the model via JSON Schema, and optionally ask it to call a specific function. Get started with our developer documentation and add evals if you find cases where function calling could be improved

Since the alpha release of ChatGPT plugins, we have learned much about making tools and language models work together safely. However, there are still open research questions. For example, a proof-of-concept exploit illustrates how untrusted data from a tool’s output can instruct the model to perform unintended actions. We are working to mitigate these and other risks. Developers can protect their applications by only consuming information from trusted tools and by including user confirmation steps before performing actions with real-world impact, such as sending an email, posting online, or making a purchase.

gpt-4-0613 includes an updated and improved model with function calling.

gpt-4-32k-0613 includes the same improvements as gpt-4-0613, along with an extended context length for better comprehension of larger texts.

With these updates, we’ll be inviting many more people from the waitlist to try GPT-4 over the coming weeks, with the intent to remove the waitlist entirely with this model. Thank you to everyone who has been patiently waiting, we are excited to see what you build with GPT-4!

gpt-3.5-turbo-0613 includes the same function calling as GPT-4 as well as more reliable steerability via the system message, two features that allow developers to guide the model's responses more effectively.

gpt-3.5-turbo-16k offers 4 times the context length of gpt-3.5-turbo at twice the price: $0.003 per 1K input tokens and $0.004 per 1K output tokens. 16k context means the model can now support ~20 pages of text in a single request.

Today, we’ll begin the upgrade and deprecation process for the initial versions of gpt-4 and gpt-3.5-turbo that we announced in March. Applications using the stable model names (gpt-3.5-turbo, gpt-4, and gpt-4-32k) will automatically be upgraded to the new models listed above on June 27th. For comparing model performance between versions, our Evals library supports public and private evals to show how model changes will impact your use cases. 

Developers who need more time to transition can continue using the older models by specifying gpt-3.5-turbo-0301, gpt-4-0314, or gpt-4-32k-0314 in the ‘model’ parameter of their API request. These older models will be accessible through September 13th, after which requests specifying those model names will fail. You can stay up to date on model deprecations via our model deprecation page. This is the first update to these models; so, we eagerly welcome developer feedback to help us ensure a smooth transition.

We continue to make our systems more efficient and are passing those savings on to developers, effective today.

text-embedding-ada-002 is our most popular embeddings model. Today we’re reducing the cost by 75% to $0.0001 per 1K tokens.

gpt-3.5-turbo is our most popular chat model and powers ChatGPT for millions of users. Today we're reducing the cost of gpt-3.5-turbo’s input tokens by 25%. Developers can now use this model for just $0.0015 per 1K input tokens and $0.002 per 1K output tokens, which equates to roughly 700 pages per dollar.

gpt-3.5-turbo-16k will be priced at $0.003 per 1K input tokens and $0.004 per 1K output tokens.

Developer feedback is a cornerstone of our platform’s evolution and we will continue to make improvements based on the suggestions we hear. We’re excited to see how developers use these latest models and new features in their applications.