Short answer: “Chat completion models” is an imprecise term: Chat Completions is an API endpoint, while models are selected inside it. For a new project, OpenAI recommends the Responses API. Chat Completions remains a good choice for direct, application-managed workflows. Do not start a new integration with the deprecated Assistants API; OpenAI has announced its shutdown for August 26, 2026. Existing Assistants implementations should begin migration now.
What is actually being compared?
OpenAI’s architecture has four separate layers:
- Model: the language or multimodal model that performs the work.
- Endpoint: Chat Completions, Responses, or Assistants.
- State and orchestration: your application code, Responses conversation mechanisms, or Assistants Threads and Runs.
- Tools and business logic: retrieval, functions, authentication, permissions, and external services.
Confusing these layers leads to outdated advice. The practical current comparison is usually Chat Completions versus Responses, with Assistants relevant mainly to systems that still need to migrate.
Chat Completions API
Chat Completions accepts a sequence of messages and returns a model response through POST https://api.openai.com/v1/chat/completions. A typical request is:
{
"model": "MODEL_ID",
"messages": [
{"role": "developer", "content": "You are a helpful support agent."},
{"role": "user", "content": "Where is my order?"}
]
}See the Chat Completions API reference and OpenAI’s API transition guide.
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How state works
Chat Completions gives the application more responsibility for conversation state. In the usual design, your database stores history and your code sends the relevant messages with each request. The endpoint does not provide the Assistants-style Assistant/Thread/Run lifecycle, although state-related features and stored-response patterns can vary by model, parameters, and organization data controls. Do not treat it as absolutely stateless.
Tool calling
Function calling is supported, but your application generally runs the orchestration loop:
- Send messages and authorized tool definitions.
- Detect a tool call and validate its arguments.
- Authenticate and authorize the requested operation.
- Execute the function with timeouts and idempotency controls.
- Append the tool result to the conversation.
- Send the updated messages back until the model returns a final answer.
This approach takes more engineering than a managed run, but gives you precise control over retries, permissions, latency, logging, and tenant isolation.
When it fits
- Your application already owns conversation history, retrieval, and memory.
- You need a simple request/response contract for generation, classification, extraction, or chat.
- Existing infrastructure and observability use the Chat Completions schema.
- You operate a high-volume pipeline or a multi-provider abstraction layer.
- You do not need Responses-only tools or agent features.
OpenAI’s current guidance still allows Chat Completions where its simplicity or a specific supported capability is the better fit; see OpenAI’s Chat Completions guidance.
What the Assistants API provided
Assistants introduced persistent server-side objects:
- Assistant: model, instructions, and tools.
- Thread: persistent conversation state.
- Message: user or assistant content in a thread.
- Run: execution of an Assistant against a thread.
- Run steps: records of tool calls and execution details.
The normal lifecycle was:
Create assistant → create thread → add message → create run → poll or stream → handle tool calls → submit outputs → read the final messageHosted File Search and Code Interpreter, automatic truncation management, and recorded run steps reduced application-side work. Threads persisted messages, but they did not create unlimited usable model memory: context limits, truncation, retrieval, deletion, and tool output size still affected what the model received.
Why developers adopted it
Assistants was attractive when teams wanted OpenAI to manage conversation objects, multi-step execution, and hosted tools instead of building those layers themselves. The trade-off was a more opinionated asynchronous lifecycle, more objects to monitor and delete, and less direct control over execution.
Its current status
OpenAI says the Assistants API is deprecated and recommends the Responses API for new projects. The run-lifecycle documentation specifies a shutdown date of August 26, 2026. It may still be accessible on August 18, 2026, but it is not a responsible foundation for a new production integration with removal imminent. Sources: Assistants API FAQ and run lifecycle documentation.
The Tool Desk
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Responses unifies text generation, multimodal inputs and outputs, tool calls, streaming, and current agent-oriented capabilities. OpenAI’s quickstart demonstrates requests such as:
curl https://api.openai.com/v1/responses
-H "Content-Type: application/json"
-H "Authorization: Bearer $OPENAI_API_KEY"
-d '{
"model": "MODEL_ID",
"input": "Explain how DNS works."
}'In JavaScript, install the official SDK with npm install openai and call:
import OpenAI from "openai";
const client = new OpenAI();
const response = await client.responses.create({
model: "MODEL_ID",
input: "Write a one-sentence bedtime story about a unicorn."
});
console.log(response.output_text);When Responses is preferable
- Built-in web search, file search, Code Interpreter, computer-use workflows, or remote MCP servers.
- Multimodal input and output patterns.
- New agentic capabilities and a current migration path from Assistants.
- A unified interface for tool calls, streaming, and conversation mechanisms.
Tool availability depends on the model, endpoint, account, and region. Check the current model catalog before committing to a capability.
Feature comparison
| Criterion | Chat Completions | Responses | Assistants API |
|---|---|---|---|
| Primary abstraction | Messages sent to a model | Inputs, outputs, and tools | Assistant, Thread, Message, Run |
| Conversation ownership | Usually application-managed | Application-managed or current conversation mechanisms | Server-managed Threads |
| Function calling | Supported; application runs the loop | Supported with richer tool workflows | Supported through Runs |
| Hosted web, file, and code tools | Not the general default path | Supported, subject to availability | Supported historically |
| Control and simplicity | Direct and lightweight | Direct with broader capabilities | More managed but more lifecycle objects |
| New production project | Suitable for simple or compatibility-sensitive systems | Preferred when its capabilities fit | Do not start |
| Lifecycle | Supported | Current strategic direction | Deprecated; shutdown August 26, 2026 |
Which should you choose?
Basic chatbot or structured extraction
Use Chat Completions if your service stores history, retrieves account data, and needs a straightforward model call. Use Responses instead if you want its current tools or multimodal features.
Retrieval-augmented generation
Choose between your own retrieval pipeline with Chat Completions and Responses’ file-search tooling. Base the decision on control, compliance, indexing requirements, and tool availability rather than assuming one endpoint produces better answers.
Customer support with business actions
Chat Completions works well when your application controls identity, permissions, CRM lookups, and function execution. Responses is preferable when built-in tools or a more agentic workflow reduce implementation work.
Web research, data analysis, or computer use
Start with Responses and verify the required tool and regional support. For larger multi-agent systems requiring handoffs and tracing, evaluate the Agents SDK through OpenAI’s platform documentation: https://platform.openai.com/docs.
Existing Assistants application
Begin migration immediately. Responses is the recommended destination, but it is not a byte-for-byte endpoint swap.
Migration checklist for Assistants users
- Inventory Assistant instructions, model settings, metadata, and tenant references.
- Export or map Thread messages and application ownership rules.
- Recreate function definitions and secure tool execution.
- Move file uploads, vector stores, and File Search assumptions.
- Replace Run polling, streaming, cancellation, retries, and event handling.
- Test context truncation, summaries, retrieval quality, and long conversations.
- Document deletion, retention, authorization, and regional behavior.
- Recalculate token, tool, storage, and repeated-context costs.
- Run regression tests and cut over before August 26, 2026.
Changing a URL is not enough: Assistant objects, Threads, Runs, files, tool calls, and lifecycle events do not map one-to-one onto a single Chat Completions request.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Cost: compare the workload, not the endpoint name
There is no universal “Chat Completions price” versus “Assistants price.” Total cost depends on the selected model, input and output tokens, cached input where available, reasoning behavior, resubmitted history, tool calls, storage, and batch or synchronous execution.
Assistants’ FAQ historically listed Code Interpreter at $0.03 per session and File Search storage at $0.10 per GB per day, with the first GB free under the rules described there. Because Assistants is being shut down, treat those figures as source-dated pricing signals, not a reason to adopt it. Check the live OpenAI API pricing and model catalog for current rates.
Privacy, retention, and regional limits
OpenAI’s data-controls documentation says API data is not used to train or improve models unless the customer opts in. That statement is separate from abuse-monitoring logs, application state, and developer-created files.
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- Chat Completions generally has no persistent application state by default, with feature-specific exceptions.
- Responses data may be stored for at least 30 days when application state is enabled or
storeis true. - Conversations and conversation items remain until deleted.
- Files, vector stores, and other developer-created objects have their own retention behavior.
Review endpoint-specific retention, data residency, and feature limitations at OpenAI’s endpoint data-controls documentation. Check your organization’s region before assuming that background mode, computer use, extended prompt caching, or MCP support is available.
Common misconceptions
“I need memory, so I need Assistants.”
No. Memory can live in your database, a retrieval layer, summaries, a Responses conversation, or another purpose-built system. Decide who owns persistence, deletion, and tenant isolation.
“Chat Completions is always cheaper.”
Not necessarily. A direct call may reduce orchestration overhead, but resending history or retrieval context can increase token usage. Models and tools determine the bill.
“Responses is always better.”
Responses is the current recommended direction, not a universal requirement. Chat Completions remains sensible for a controlled message pipeline or compatibility-sensitive integration.
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“Persistent Threads mean the model remembers everything.”
Threads persist stored messages; usable context remains bounded by model limits, truncation, retrieval behavior, and tool-output size.
Bottom line for 2026
Use Chat Completions when you want a direct model-call interface and are prepared to own history, retrieval, tools, and orchestration. Use Responses for new applications that need OpenAI’s current built-in tools, multimodal features, or agent workflows. Do not start new work on Assistants API; migrate existing Assistants systems before August 26, 2026.
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