New models and developer products announced at DevDay

New models and developer products announced at DevDay | OpenAI

November 6, 2023

New models and developer products announced at DevDay

GPT‑4 Turbo with 128K context and lower prices, the new Assistants API, GPT‑4 Turbo with Vision, DALL·E 3 API, and more.

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Update: We previously stated that applications using the_gpt-3.5-turbo_ name will automatically be upgraded to the new model version on December 11. We have edited the blogpost to remove this line since this will no longer be happening.

Today, we shared dozens of new additions and improvements, and reduced pricing across many parts of our platform. These include:

• New GPT‑4 Turbo model that is more capable, cheaper and supports a 128K context window
• New Assistants API that makes it easier for developers to build their own assistive AI apps that have goals and can call models and tools
• New multimodal capabilities in the platform, including vision, image creation (DALL·E 3), and text-to-speech (TTS)

We’ll begin rolling out new features to OpenAI customers starting at 1pm PT today.

Learn more about OpenAI DevDay announcements for ChatGPT⁠.

GPT-4 Turbo with 128K context

We released the first version of GPT‑4 in March and made GPT‑4 generally available to all developers in July. Today we’re launching a preview of the next generation of this model, GPT‑4 Turbo⁠.

GPT‑4 Turbo is more capable and has knowledge of world events up to April 2023. It has a 128k context window so it can fit the equivalent of more than 300 pages of text in a single prompt. We also optimized its performance so we are able to offer GPT‑4 Turbo at a 3x cheaper⁠ price for input tokens and a 2x cheaper price for output tokens compared to GPT‑4.

GPT‑4 Turbo is available for all paying developers to try by passinggpt-4-1106-preview in the API and we plan to release the stable production-ready model in the coming weeks.

Function calling updates

Function calling⁠ lets you describe functions of your app or external APIs to models, and have the model intelligently choose to output a JSON object containing arguments to call those functions. We’re releasing several improvements today, including the ability to call multiple functions in a single message: users can send one message requesting multiple actions, such as “open the car window and turn off the A/C”, which would previously require multiple roundtrips with the model (learn more⁠). We are also improving function calling accuracy: GPT‑4 Turbo is more likely to return the right function parameters.

Improved instruction following and JSON mode

GPT‑4 Turbo performs better than our previous models on tasks that require the careful following of instructions, such as generating specific formats (e.g., “always respond in XML”). It also supports our new JSON mode⁠, which ensures the model will respond with valid JSON. The new API parameterresponse_format enables the model to constrain its output to generate a syntactically correct JSON object. JSON mode is useful for developers generating JSON in the Chat Completions API outside of function calling.

Reproducible outputs and log probabilities

The newseed parameter enables reproducible outputs by making the model return consistent completions most of the time. This beta feature is useful for use cases such as replaying requests for debugging, writing more comprehensive unit tests, and generally having a higher degree of control over the model behavior. We at OpenAI have been using this feature internally for our own unit tests and have found it invaluable. We’re excited to see how developers will use it. Learn more⁠.

We’re also launching a feature to return the log probabilities for the most likely output tokens generated by GPT‑4 Turbo and GPT‑3.5 Turbo in the next few weeks, which will be useful for building features such as autocomplete in a search experience.

Updated GPT-3.5 Turbo

In addition to GPT‑4 Turbo, we are also releasing a new version of GPT‑3.5 Turbo that supports a 16K context window by default. The new 3.5 Turbo supports improved instruction following, JSON mode, and parallel function calling. For instance, our internal evals show a 38% improvement on format following tasks such as generating JSON, XML and YAML. Developers can access this new model by callinggpt-3.5-turbo-1106 in the API. Older models will continue to be accessible by passinggpt-3.5-turbo-0613 in the API until June 13, 2024. Learn more⁠.

Assistants API, Retrieval, and Code Interpreter

Today, we’re releasing the Assistants API⁠, our first step towards helping developers build agent-like experiences within their own applications. An assistant is a purpose-built AI that has specific instructions, leverages extra knowledge, and can call models and tools to perform tasks. The new Assistants API provides new capabilities such as Code Interpreter and Retrieval as well as function calling to handle a lot of the heavy lifting that you previously had to do yourself and enable you to build high-quality AI apps.

This API is designed for flexibility; use cases range from a natural language-based data analysis app, a coding assistant, an AI-powered vacation planner, a voice-controlled DJ, a smart visual canvas—the list goes on. The Assistants API is built on the same capabilities that enable our new GPTs product⁠: custom instructions and tools such as Code interpreter, Retrieval, and function calling.

A key change introduced by this API is persistent and infinitely long threads, which allow developers to hand off thread state management to OpenAI and work around context window constraints. With the Assistants API, you simply add each new message to an existingthread.

Assistants also have access to call new tools as needed, including:

• Code Interpreter: writes and runs Python code in a sandboxed execution environment, and can generate graphs and charts, and process files with diverse data and formatting. It allows your assistants to run code iteratively to solve challenging code and math problems, and more.
• Retrieval: augments the assistant with knowledge from outside our models, such as proprietary domain data, product information or documents provided by your users. This means you don’t need to compute and store embeddings for your documents, or implement chunking and search algorithms. The Assistants API optimizes what retrieval technique to use based on our experience building knowledge retrieval in ChatGPT.

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