Claude API vs OpenAI Assistants API: A Comparison
Claude API vs OpenAI Assistants API: the short answer
If you're comparing these two, the core difference isn't model quality — it's architecture. Claude's API (the Messages API) is stateless: you send the full conversation history with every request, and Anthropic doesn't store it for you. OpenAI's Assistants API is stateful: it manages threads, messages, and run state server-side, so you don't have to pass the whole history back each time.
That architectural split cascades into everything else — how you handle context, how you implement tool use, how you debug, and how much control you have over cost. This article breaks down the practical differences so you can pick the right one (or know what you're giving up by switching).
Request model: stateless vs stateful
With Claude's Messages API, every call looks like this:
curl https://api.anthropic.com/v1/messages \
-H "x-api-key: $ANTHROPIC_API_KEY" \
-H "anthropic-version: 2023-06-01" \
-H "content-type: application/json" \
-d '{
"model": "claude-sonnet-4-5",
"max_tokens": 1024,
"messages": [
{"role": "user", "content": "What is a thread in the OpenAI Assistants API?"}
]
}'You own the conversation array. Nothing persists on Anthropic's servers between calls — you're responsible for storing history, truncating it, and sending it back.
OpenAI's Assistants API inverts this. You create a thread, add messages to it, and trigger a run against an assistant. The platform stores the message history and manages state transitions (queued, in_progress, completed, requires_action). You poll or use webhooks to find out when a run is done.
Tradeoffs:
- Stateless (Claude) gives you full control over context — you decide what gets sent, how it's truncated, and how it's cached. It also means no vendor lock-in on conversation storage.
- Stateful (Assistants API) reduces boilerplate but means your conversation data lives in OpenAI's thread storage, and you're tied to their run lifecycle for anything beyond a single turn.
If you're building something where you already have your own database and want to control exactly what context the model sees, stateless is usually simpler to reason about and cheaper to operate, since you can prune or summarize history yourself instead of paying for every stored message to be replayed.
Tool use / function calling
Both APIs support structured tool calls, but the shapes differ.
Claude's tool use is part of the Messages API itself — you define tools with a JSON schema, the model returns a tool_use content block, you run the tool, and send a tool_result block back in the next message:
{
"role": "user",
"content": [
{
"type": "tool_result",
"tool_use_id": "toolu_01A...",
"content": "72°F, sunny"
}
]
}It's a single, synchronous-feeling loop: call, get tool_use, respond, repeat.
The Assistants API wraps this in run states. A run pauses with status requires_action and a required_action.submit_tool_outputs payload, you submit outputs, and the run resumes. Functionally similar, but there's more state machine to manage — you need to poll run status, handle multiple pending tool calls per step, and track run IDs across the lifecycle.
Neither approach is objectively better; the Assistants API model fits long-running, multi-step agents well, while Claude's approach is easier to unit test because each call is self-contained.
Streaming
Both support server-sent events for token-by-token output. Claude streams content_block_delta events for both text and tool-use arguments. The Assistants API streams run step events, which include more intermediate state (step creation, message deltas, run status changes) because there's more state to report in the first place.
If you're building a chat UI, Claude's streaming is generally fewer event types to handle. If you're building a dashboard that needs to show "the agent is now running step 3 of 5," the richer event stream in the Assistants API can save you some manual state tracking.
Context, files, and retrieval
OpenAI's Assistants API bundles file search and code interpreter as built-in tools you attach to an assistant — useful if you want retrieval-augmented answers without building your own vector store. Claude doesn't bundle this; you build retrieval yourself (vector DB + Messages API) or use Claude's large context window to pass documents directly in the prompt, which works well given Claude's context length and strong long-document comprehension.
This is a real tradeoff: less built-in tooling on Claude's side, but more flexibility in choosing your own retrieval stack, embeddings model, and chunking strategy instead of being tied to OpenAI's implementation.
Pricing and operational cost
Pricing structures differ in a subtle but important way. The Assistants API bills for stored messages and retrieval usage in addition to tokens, so a long-running thread with many turns and file searches can accumulate costs beyond raw token counts. Claude's Messages API bills purely on input/output tokens per call (plus prompt caching discounts if you use them), with no separate storage or retrieval line items, because there's nothing server-side to store.
If you're trying to predict and control cost per user or per feature, stateless billing is easier to model — you know exactly what you're sending and can cap it.
When each makes sense
Pick Claude's Messages API if:
- You want full control over context and conversation state
- You're building tool-using agents with tight, testable request/response cycles
- You care about predictable per-call cost
- You want to wrap the model behind your own API surface — which is exactly what SubToAPI does: it exposes Claude through standard application API keys (
sub_live_...) with streaming, tool use, and usage metadata, without you having to build auth, rate limiting, or team seat management from scratch. See the quickstart or messages docs for the request shape.
Pick OpenAI's Assistants API if:
- You want built-in file search / retrieval without standing up your own vector store
- Your use case is naturally long-running and you're fine with OpenAI managing thread state
- You don't mind the run-status polling loop in exchange for less state management code
Migration notes
Moving from Assistants API to Claude usually means: flattening threads into explicit message arrays, replacing submit_tool_outputs with tool_result blocks, and rebuilding any file-search logic with your own retrieval layer. It's more code up front but gives you a simpler mental model once it's running. If you're already issuing requests through a gateway like SubToAPI, swapping models or providers behind that layer is a config change rather than a rewrite — see /docs/tools for the tool-use request format and /pricing for plan details.
Questions
Is Claude's Messages API stateful like the Assistants API? No. The Messages API is stateless — you send full conversation history on every request. Anthropic doesn't store threads server-side the way OpenAI's Assistants API does.
Which API is cheaper to run at scale? It depends on usage pattern, but Claude's pure token-based billing is easier to predict, since the Assistants API adds storage and retrieval costs on top of tokens for long-running threads.
Can I use Claude with an OpenAI-style stateful workflow? Yes, but you build it yourself — store conversation history in your own database and pass it back each call, or use a gateway like SubToAPI that exposes Claude via a standard API key while you manage state on your side.