OpenAI Response API vs Chat Completions: Which Should You Use for Your Next Build?

Navigating OpenAI’s Evolving API Landscape

You're gearing up to build your next AI-powered application and have chosen OpenAI as your language model provider. You've likely worked with the powerful GPT models before, and now you're back in the docs, setting up your stack. But right away, you're faced with a fundamental question: Should you stick with the tried and true Chat Completions API or embrace the new Response API that OpenAI is now recommending for all new projects?

The Chat Completions API is familiar territory. It has been the backbone of countless AI applications since early 2023. It’s flexible, well-documented, and battle-tested. Other companies like Anthropic and Google have also adopted similar message-based interfaces, helping them become a de facto industry standard. So why change what already works?

Well, OpenAI has a track record of evolving its APIs to reflect the latest innovations in AI development. The Chat Completions API was introduced when chat models like ChatGPT gained widespread use.

The Response API follows that same trend, but its focus is on agentic capabilities. It introduces first-class support for tools, simplifies conversation management with built-in state handling, and enhances support for streaming.

In this article, we will dive deeper into these new features of the response API and see how it compares with the chat completion API. When you are done with the article, you will understand the following:

• How the Response API differs from the Chat Completions API

• How it manages conversational state natively

• The Response API’s first-class support for tools

• The semantic-driven streaming support in the Response API

Let’s dive in.

Evolution of OpenAI API

The OpenAI API has evolved significantly over the last few years. When OpenAI first introduced its API in 2020, it gave developers access to GPT-3 through a simple interface. Since then, OpenAI has continued to refine and expand its API offerings to better support a variety of use cases,

Here’s a quick overview of the key APIs OpenAI has released over the years:

• The Completions API

• The Chat Completion API

• The Assistant API

The Completions API

The Completions API was OpenAI’s first publicly available API, launched in 2020. It uses a simple "text in, text out" interface. You provide a text prompt, and the model responds by continuing or completing the input. This design reflects the foundational principle of large language models: predicting the next token in a sequence.

Here’s an example of how to use the Completions API with the OpenAI Python SDK:

import openai

openai.api_key = "YOUR_API_KEY"

response = openai.Completion.create(
    model="text-davinci-003",
    prompt="Write a poem about the ocean at night.",
    max_tokens=100,
    temperature=0.7,
)

print(response.choices[0].text)

This API was groundbreaking at the time, as it marked the first introduction of a general-purpose language model that could be prompted to perform a wide range of tasks, including text generation, summarization, question answering, and more.

Chat Completions API

With the release of ChatGPT in 2022, there was a growing demand for an API that mirrored the conversational style users experienced in the ChatGPT interface. In March 2023, OpenAI responded by launching the Chat Completions API.

Unlike the original Completions API, which followed a simple text-in, text-out format, the Chat Completions API was designed around multi-turn conversations. It introduced a structured format using messages, where each message had a role (system, user, or assistant), enabling developers to build more interactive and context-aware experiences.

Here’s an example of how to use the Chat Completions API with the Python SDK:

import openai

openai.api_key = "YOUR_API_KEY"

response = openai.ChatCompletion.create(
    model="gpt-4",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What's a good recipe for homemade pizza?"}
    ],
    temperature=0.7
)

print(response.choices[0].message["content"])

Over time, OpenAI continued to improve this API by adding features like function calling, which allowed models to interact with external tools. This introduced agent-like capabilities, but they were not fully integrated into the design of the API.

The Assistant API

At OpenAI’s Developer Day 2023, the Assistant API was introduced in beta as a step toward building more capable AI agents. It introduced the concept of assistants, AI agents that could be created and given access to built-in tools like code execution, file handling, and function calling. It also brought in threads, which allowed developers to manage and persist conversation state across interactions.

Here’s how the API works in practice. First, you create an assistant and grant it access to a built-in tool like the code interpreter:

from openai import OpenAI
client = OpenAI()

assistant = client.beta.assistants.create(
  name="Math Tutor",
  instructions="You are a personal math tutor. Write and run code to answer math questions.",
  tools=[{"type": "code_interpreter"}],
  model="gpt-4o",
)

Next, you create a thread to manage the ongoing conversation:

thread = client.beta.threads.create()

User messages are added to the thread using its ID:

message = client.beta.threads.messages.create(
  thread_id=thread.id,
  role="user",
  content="I need to solve the equation `3x + 11 = 14`. Can you help me?"
)

Finally, you initiate a run that ties the assistant and the thread together:

run = client.beta.threads.runs.create_and_poll(
  thread_id=thread.id,
  assistant_id=assistant.id,
  instructions="Please address the user as Jane Doe. The user has a premium account."
)

The Assistant API introduced a powerful architecture for building agentic workflows, but it remained experimental and in beta. Many of the ideas it tested, like persistent conversation state and built-in tool support, laid the foundation for what would become the more streamlined and production-ready Response API.

The Response API: The next step in OpenAI’s API evolution

In March 2025, nearly two years after the launch of the Chat Completions API, OpenAI introduced the Response API, a unified interface that combines the best of both the Chat Completions and Assistant APIs.

From the Chat Completions API, it inherits its simplicity: you send a list of messages and get a response. There’s no need to create assistants, manage thread objects, or handle extra orchestration. The Assistant API brings over the features that made agentic workflows possible, including built-in tool support, native state management, and event-driven streaming. The result is an API that’s lightweight and easy to use, yet powerful enough to support fully capable AI agents.

With the introduction of the Response API, OpenAI announced plans to deprecate the Assistant API. However, the Chat Completions API isn’t going anywhere. That means developers will now have two primary APIs to choose from on the OpenAI platform. So, which one should you use?

OpenAI’s general recommendation is simple: use the Response API for new projects, and stick with the Chat Completions API for existing ones that are already in production. Now let’s take a look at some of the features that make the Response API stand out from the Chat Completions API.

Conversation Statement Management

The Chat Completions API is stateless; each call is independent and doesn’t retain memory of previous interactions unless you explicitly pass the full message history every time. The Response API changes that by offering optional stateful conversations, managed directly by the OpenAI platform. This works similarly to how threads functioned in the Assistant API, but with a much simpler interface.

To maintain state between calls, you just pass the previous_response_id of an earlier response. This tells the API to continue the conversation from where it left off:

from openai import OpenAI
client = OpenAI()

response = client.responses.create(
    model="gpt-4o-mini",
    input="tell me a joke",
)
print(response.output_text)

second_response = client.responses.create(
    model="gpt-4o-mini",
    previous_response_id=response.id,
    input=[{"role": "user", "content": "explain why this is funny."}],
)
print(second_response.output_text)

If you prefer to manage state manually, as you would in the Chat Completions API, you still can. The Response API is backward-compatible and supports manual message construction.

You can also opt out of automatic state management entirely by setting the store parameter to False. This disables storage of the conversation state:

from openai import OpenAI

client = OpenAI()

response = client.responses.create(
    model="gpt-4o-mini",
    input=[
        {"role": "user", "content": "knock knock."},
        {"role": "assistant", "content": "Who's there?"},
        {"role": "user", "content": "Orange."},
    ],
    store=False
)

print(response.output_text)

In short, the Response API improves on the Chat Completions API by making conversation state optional, seamless, and easier to manage.

Built-in Tool Support in the response API

The response API, similar to the Assistant API, offers built-in tool support. With the chat completion API, you could only have access to tools via function calling or via dedicated models. Let’s go through some of the built-in tools response API currently supports.

Web Search Tool

The Response API includes a built-in web search tool that allows the model to perform live searches. Using it is straightforward, just pass the tool in the tools parameter by specifying its type as web_search_preview.

from openai import OpenAI
client = OpenAI()

response = client.responses.create(
    model="gpt-4.1",
    tools=[{"type": "web_search_preview"}],
    input="What was a positive news story from today?"
)

print(response.output_text)

While the Chat Completions API also supports web search, it requires using specific models like gpt-4o-search-preview or gpt-4o-mini-search-preview. In contrast, the Response API offers greater flexibility, web search can be enabled across a wider range of models, making it easier to integrate into different applications without being locked into special model variants.

File Search Tool

The Response API introduces support for the file search tool. This tool allows the model to retrieve and reference information from files you've uploaded, making it great for use cases like answering questions from documentation, PDFs, knowledge bases, or research papers.

The Chat Completions API doesn’t support this tool at all. This is one of the Response API’s more agentic capabilities, carried over and improved from the Assistant API.

To use the file search tool, you first upload your files and organize them into a vector store. You then attach this store to your request using the tools parameter.

Here’s a basic example of how to use it:

from openai import OpenAI
client = OpenAI()

response = client.responses.create(
    model="gpt-4o-mini",
    input="What is deep research by OpenAI?",
    tools=[{
        "type": "file_search",
        "vector_store_ids": ["<vector_store_id>"]
    }]
)
print(response)

This tool gives the model retrieval-augmented generation (RAG) abilities with just a few lines of code.

Image Generation

Another powerful feature of the Response API is its support for image generation as a tool. Instead of requiring a dedicated model like in previous APIs, image generation is now treated as a native tool, just like web search or file search. This gives developers a more unified and consistent interface for triggering different capabilities.

Behind the scenes, the tool uses the GPT image model, so you still get the full capabilities of gpt-image-1, but without switching models. That means you can generate images while still using gpt-4o or any other supported model in your workflow.

Here’s how you can use it:

from openai import OpenAI
import base64

client = OpenAI() 

response = client.responses.create(
    model="gpt-4.1-mini",
    input="Generate an image of gray tabby cat hugging an otter with an orange scarf",
    tools=[{"type": "image_generation"}],
)

# Save the image to a file
image_data = [
    output.result
    for output in response.output
    if output.type == "image_generation_call"
]
    
if image_data:
    image_base64 = image_data[0]
    with open("otter.png", "wb") as f:
        f.write(base64.b64decode(image_base64))

MCP Support

OpenAI introduced support for MCP within the Response API, which enables the model to access remote MCP servers directly from the API. This essentially gives the response api access to an unlimited number of tools that implement the MCP protocol. Here’s how you can access an MCP server with it:

The Response API includes native support for MCP, enabling the model to directly access remote MCP servers. This effectively unlocks access to an unlimited number of external tools and knowledge bases that implement the MCP protocol.

Here’s an example of how to connect to an MCP server using the Response API:

from openai import OpenAI

client = OpenAI()

resp = client.responses.create(
    model="gpt-4.1",
    tools=[
        {
            "type": "mcp",
            "server_label": "deepwiki",
            "server_url": "https://mcp.deepwiki.com/mcp",
            "require_approval": "never",
        },
    ],
    input="What transport protocols are supported in the 2025-03-26 version of the MCP spec?",
)

print(resp.output_text)

In this example, the MCP tool connects to the deepwiki MCP server, which hosts various documentation. This allows your application to answer detailed questions based on any documentation available within the wiki.

Given MCP’s growing adoption, this integration is a game-changer; now developers can easily plug into remote MCP servers without needing to handle the MCP SDK or complex integrations themselves.

Additional Tools and What’s Next

OpenAI is expected to continue adding more tools to the Response API over time. So far, they have introduced the Computer Use tool, which builds on their Computer-Using Agent (CUA) model, as well as the code interpreter tool. These additions will make the Response API even more powerful and agentic.

Response API Streaming With Semantic Events

The Chat Completions API’s streaming feature delivered raw text chunks as they were generated. In contrast, the Response API introduces semantic streaming, where each streamed result is a structured event providing rich information beyond just text.

For example, the API emits semantic events like:

• ResponseCreatedEvent — signals when a streaming response starts

• ResponseCompletedEvent — signals when the stream finishes

• ResponseOutputTextDelta — delivers partial text updates as they arrive

Beyond text, the Response API also streams events related to tool usage, such as ResponseFileSearchCallSearching and ResponseFileSearchCallCompleted, enabling developers to track tool interactions in real time.

This event-driven approach makes streaming more informative and easier to integrate into complex, agentic workflows.

Is This Vendor Lock-In?

The Response API brings powerful new capabilities, but it also raises an important question: Is this a form of vendor lock-in?

Many of the features introduced, like built-in tools, native state management, and semantic streaming, are tightly integrated into the OpenAI platform. These enhancements don't carry over to other model providers. So once you start building with the Response API, switching to a different provider may require significant rework, reducing the incentive to move away from OpenAI.

That said, OpenAI isn’t forcing developers to use these features. They’re optional enhancements designed to improve developer experience and agent capabilities. You can still use the Response API in a minimal, model-only fashion, just like Chat Completions, or continue using the Chat Completions API itself.

This also opens up new possibilities: as the Response API becomes more widely adopted, we may see community-built wrappers that abstract these features and make them usable across different model backends.

Conclusion

The Response API brings a clear evolution in how AI agents are built. With native support for tools, built-in state management, and a more event-driven interface, it simplifies many of the complexities developers previously had to handle manually. If you're building an agentic application and you're comfortable working within the OpenAI ecosystem, the Response API is the clear choice. It provides more out of the box, with less boilerplate.

However, if your project needs to remain model-agnostic or you’re aiming for maximum portability, the Chat Completions API might still be the better fit. Thanks to its simplicity and wide adoption, it remains a solid choice. The Response API’s backward compatibility also means you can still use it in a familiar, lightweight way without adopting the full toolset.

Ultimately, the Response API represents OpenAI’s vision for the future of AI development: more structured, more capable, and more agent-driven. Whether that future aligns with your next build depends on your goals. The good news is you’ve got options, and both APIs are here to stay.

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