ChatGPT App vs Custom GPT: What Is the Difference? (July 2026)

Developers looking for a way to build inside ChatGPT usually find two names first: Custom GPTs and ChatGPT Apps. They sound close enough that it is easy to treat them as two versions of the same thing. They are not.

TL;DR: Custom GPTs are no-code assistants configured inside ChatGPT. They are good for instructions, uploaded knowledge, reusable workflows, and text-first API actions. ChatGPT Apps are coded applications built with MCP tools and UI resources. They can render forms, dashboards, editors, approval screens, and other interactive UI inside the conversation. If text is enough, build a Custom GPT. If the user needs to click, compare, edit, approve, filter, or submit structured data, build a ChatGPT App.

The Short Version

The simplest way to choose is to look at the user’s output.

Use a Custom GPT when the ideal result is a better ChatGPT response. That might be a support assistant that knows your docs, a sales enablement helper with a specific tone, a research assistant with reusable instructions, or a GPT that calls an API and summarizes the result.

Use a ChatGPT App when the ideal result is a working interface. That might be a dashboard, a kanban board, a pricing configurator, a document editor, a compliance review queue, a booking flow, or a data table with filters. The model still matters, but the user needs UI, not just a message.

That distinction matters because the implementation path is completely different. A Custom GPT is configured in ChatGPT. A ChatGPT App is shipped as an MCP server with tools, resources, schemas, security metadata, and a web UI.

What Custom GPTs Are

Custom GPTs let you configure a version of ChatGPT for a specific purpose without building a full app. In the GPT builder, you can write instructions, add conversation starters, upload knowledge files, enable built-in capabilities, and connect external APIs through Actions.

The end product is still a chat assistant. It can answer in a certain style, use the files you provided, call an action, and return a response. That is useful when the user wants a conversation that behaves consistently.

Custom GPTs are a good fit for:

• Internal assistants tuned to company docs, policies, or terminology.
• Customer support bots where the answer can stay in text.
• Reusable prompts for research, planning, writing, or analysis.
• Lightweight API workflows where the API result can be summarized.
• Teams that need a quick prototype without a frontend or backend project.

The tradeoff is that you do not control a real application surface. You cannot ship a React component, keep a sortable table on screen, open a full-screen editor, or sync user clicks back into a typed app state model. For those jobs, you need a ChatGPT App.

What ChatGPT Apps Are

A ChatGPT App is an MCP App that runs inside ChatGPT. Your server exposes MCP tools and UI resources. ChatGPT calls a tool, receives structured data, and renders the linked UI resource in a sandboxed iframe inside the conversation.

That UI resource can be a normal web app. It can render charts, forms, tables, maps, media controls, review screens, and multi-step workflows. The app can call server tools, send follow-up messages, update model-visible context, and react to host state through the MCP Apps bridge.

Current OpenAI guidance says new ChatGPT App UI should start with the MCP Apps standard:

1. Link tools to UI with _meta.ui.resourceUri.
2. Use the standard ui/* JSON-RPC bridge over postMessage.
3. Add ChatGPT-specific window.openai APIs only when you need ChatGPT-only features.

That is a big shift for developers planning long-lived apps. The OpenAI Apps SDK is still the supported way to build and distribute ChatGPT Apps, but the portable core is now the MCP Apps standard. If you build around that core, your UI has a better path to other MCP Apps-compatible hosts.

Side by Side

	Custom GPT	ChatGPT App
Requires code?	No for most use cases	Yes
Primary output	Chat response	Interactive iframe UI plus tool results
Setup surface	GPT builder	MCP server, tools, resources, UI bundle
Data shape	Instructions, knowledge, action responses	structuredContent, _meta, app state
API calls	GPT Actions	MCP tools
UI control	ChatGPT-rendered text and markdown	Your HTML, CSS, and JavaScript
Portability	ChatGPT only	Portable when built on MCP Apps APIs
Testing need	Prompt and action tests	Tool, UI, bridge, display mode, and host tests
Best use case	Knowledge assistant	Product-like workflow inside chat

Both can be useful. They just sit at different layers. A Custom GPT changes how ChatGPT behaves. A ChatGPT App gives ChatGPT a real product surface to call and render.

The MCP Apps Difference

The broader MCP Apps ecosystem is the main reason this comparison has changed since early ChatGPT App docs. ChatGPT Apps are no longer best understood as a ChatGPT-only iframe feature. They are ChatGPT’s implementation of the MCP Apps pattern.

MCP Apps split the system into three parts:

• The MCP server declares tools and UI resources.
• The host decides when to call tools and where to render UI.
• The view runs in a sandboxed iframe and talks to the host through the bridge.

This keeps your app code from reaching into the host page. The iframe cannot read ChatGPT’s DOM, cookies, or local storage. The bridge is the narrow channel for tool input, tool output, app state, messages, and host requests.

The practical benefit is portability. A Custom GPT only exists in ChatGPT. A ChatGPT App built around MCP Apps standard fields can share more code across compatible hosts because the server, resource metadata, and bridge model are standard. You may still need host-specific polish, but your core app does not have to be rewritten around one private global.

What the Model Sees

Custom GPTs are easy to reason about because the model sees the conversation, the GPT instructions, any selected knowledge, and action results. The output is a response.

ChatGPT Apps have more data lanes:

• content is for model-visible text or media.
• structuredContent is typed data the model can reason over.
• _meta is for app-only data the UI needs but the model should not read.
• App state is for user interactions that should become model context.

That separation is one of the strongest reasons to build an app. A sales dashboard can show a large table in the iframe while exposing only the summary the model needs. A claims review screen can keep sensitive UI-only metadata out of model context. An editor can sync the user’s selected paragraph or approval status back to the model without dumping the full DOM into chat.

Custom GPTs do not give you that same set of lanes. They are simpler, which is good when the task is simple and limiting when the task becomes product-like.

When Custom GPTs Win

Start with a Custom GPT if the job is mostly instructions plus knowledge. A support team can build a GPT that answers from uploaded docs. A finance team can build a GPT that explains internal policy. A founder can make a GPT that turns notes into a certain kind of memo.

Custom GPTs also win when speed matters more than control. You do not need to scaffold a project, deploy an MCP server, design a UI, write tests, or submit an app for review. If the workflow can live inside the chat response, that speed is hard to beat.

The strongest Custom GPT use cases share three traits:

• The user wants an answer, draft, summary, or recommendation.
• The output does not need a persistent interactive surface.
• The team wants to configure behavior without owning app infrastructure.

If those are true, adding a ChatGPT App can be extra work without much payoff.

When ChatGPT Apps Win

Build a ChatGPT App when the user needs to work with state, not just read an answer. The moment the user needs to sort records, pick options, review changes, compare items, fill a form, adjust a chart, inspect a file, or approve a write action, UI starts to earn its keep.

ChatGPT Apps also make more sense when the app needs strict contracts. You can define tool schemas, output schemas, resource metadata, CSP rules, display modes, and app-only tools. That gives you a better test surface than a prompt-only workflow.

Good ChatGPT App candidates include:

• Approval queues where the model prepares work and the user confirms it.
• Dashboards where the user filters or drills into structured data.
• Editors where the model proposes changes and the user revises them.
• Configuration flows where each step depends on prior state.
• Search and browse tools where the user needs to compare many results.
• Internal tools that should work in ChatGPT and other MCP Apps-compatible hosts.

For these workflows, a text response usually forces the user to copy data into another tool. A ChatGPT App keeps the workflow in the conversation.

Developer Mode, Distribution, and Testing

For live ChatGPT testing, expose your MCP server over HTTPS. Developer mode is required to add a custom app. Enable it from the user component in the bottom-left corner under Settings > Security and login > Developer mode. Then open Plugins from the ChatGPT sidebar, select an existing development app, or use the + button to add one. Complete the app form if needed, then run your test prompts in a new chat.

For public distribution, OpenAI now submits and publishes ChatGPT Apps as Plugins. A plugin can contain an MCP-backed app, skills, or both, so an app-only plugin still uses the same MCP App or Apps SDK code. In the OpenAI Platform plugin portal, choose With MCP and submit the public MCP server, listing and policy details, verified publisher identity, exact CSP domains, tool annotations, starter prompts, five positive tests, three negative tests, availability, release notes, and reviewer credentials when needed.

That is a real software lifecycle. You should test more than prompts:

• Unit test tool handlers and schema validation.
• Test structuredContent against the declared output shape.
• Render the iframe in each display mode your app requests.
• Check light mode, dark mode, mobile widths, and state restore.
• Run golden prompts to confirm the model chooses the right tools.
• Test auth, permission prompts, and write-action confirmation flows.

This is where sunpeak fits. sunpeak is an open-source MCP App framework, ChatGPT App framework, testing framework, and inspector. A new project can run npx sunpeak new and pnpm dev to start a local inspector and MCP server. For an existing MCP server, npx sunpeak inspect --server <url> lets you inspect tools and app UI without moving the backend into a new project.

Which One Should You Build?

Ask these questions in order:

1. Is the user happy with a text or markdown answer?
2. Can the workflow be configured with instructions, knowledge, and maybe an API action?
3. Does the team want to avoid owning app infrastructure?

If the answer is yes, build a Custom GPT.

Now ask the app questions:

1. Does the user need to click, edit, filter, approve, browse, or submit?
2. Do you need typed tool results and explicit UI state?
3. Do you want the app to work beyond ChatGPT where MCP Apps are supported?
4. Do you need automated UI, tool, and host runtime tests?

If the answer is yes, build a ChatGPT App.

The useful distinction is not “simple versus advanced.” It is “assistant versus application.” Custom GPTs are great assistants. ChatGPT Apps are applications that ChatGPT can call, render, and coordinate with.

Building a ChatGPT App

If you have decided the app path is right, start with the MCP Apps architecture instead of a ChatGPT-only wrapper. Define one narrow tool, return predictable structuredContent, attach a UI resource with _meta.ui.resourceUri, and render the smallest useful interface. Add ChatGPT-specific APIs later only where they improve the ChatGPT experience.

With sunpeak, the fastest path is:

npx sunpeak new chatgpt-app
cd chatgpt-app
pnpm dev

That starts a local inspector at localhost:3000 and an MCP server at localhost:8000. You can iterate on the UI, switch host modes, test tool results, and add Playwright coverage before connecting the app to ChatGPT developer mode.

For an existing server:

npx sunpeak inspect --server http://localhost:8000/mcp

That gives you the same inspection loop without changing your backend. Build the app like a real web product, because that is what a ChatGPT App becomes once users depend on it.