Do OpenAI Assistant Threads Each Allow a Full 128K Token Limit?

Below is a concise explanation of how Threads work in relation to the maximum token limit:

• Each Thread Has Its Own Context Window:
  A Thread represents an independent conversation session. When you send messages into a Thread, the entire conversation (i.e., the concatenation of those messages) is considered for the model’s context window. For example, if you're using gpt-4-turbo with a 128K token limit, each Thread can accumulate messages up to that limit—even if multiple Threads are maintained simultaneously.

• Threads vs. Assistants:
  Since Threads are not tied to a specific Assistant, they operate independently. An Assistant can interact with messages from one or more Threads, but each Thread’s token budget is separate. That means if you create 5 threads, each of them can potentially use up to 128K tokens individually. There is no combined shared token window among the threads—you don’t have to "distribute" the 128K tokens across them.

• Implications for Parallel Processing:
  In your use case, where you want to process a tabular dataset (e.g., 3000 rows with long texts) using the chat completion API, you can indeed create multiple Threads (for example, 10) and feed rows to each one in parallel. Each Thread will operate with its own token context window. Just keep in mind that within a single Thread the conversation history might get truncated if its total token count goes beyond the model’s limit. This means your design should ensure that a Thread’s messages do not exceed the desired context window per conversation segment.

Example Code Snippet

Below is an example to illustrate creating a thread with an initial message (adapted from the documentation):

PYTHONCopied
thread = client.beta.threads.create(
    messages=[
        {
            "role": "user",
            "content": "Process document row: 'your long text here'.",
        }
    ]
)

You can then create multiple such Threads and run each in parallel, each maintaining its independent history for up to 128K tokens (with gpt‑4‑turbo).

This approach should indeed make your process faster by handling multiple conversations simultaneously using multiple Threads.