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OpenAI’s Predicted Outputs can substantially reduce response latency when an API request asks a model to reproduce a long document or code file with only small changes. OpenAI’s launch-era “up to 5x” framing is not a promise that every GPT-4o response—or ChatGPT itself—will be five times faster. The benefit depends on how much of the supplied prediction matches the final output, and rejected prediction tokens can add cost.
What Predicted Outputs do
Predicted Outputs are a request-level optimization for OpenAI’s Chat Completions API. A developer supplies text or code that is expected to appear in the response, using the prediction parameter. When the model’s answer matches that content, matching tokens can be accepted rather than generated in the ordinary way.
Think of editing a 500-line file to rename one property. Without a prediction, the model must generate the full returned file. With the existing file supplied as the prediction, most of the final output may match it, leaving only the changed region to diverge. This is useful when the application needs the complete updated artifact, not merely a patch.
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OpenAI introduced the feature in November 2024 for workloads such as document editing and code refactoring. Its current Predicted Outputs documentation describes it as useful when many output tokens are already known. It is an API capability, not a ChatGPT setting or a permanent speed upgrade to GPT-4o.
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Why latency can fall—and why “5x” is conditional
The opportunity comes from overlap: the longer the output and the more of it that remains unchanged, the more predicted content can match. A small, localized edit to a long file is a much better fit than asking for a fresh essay or a broad rewrite. OpenAI says gains can be greater with streaming, but the result for a particular application still depends on its workload and measurement method.
“Up to five times faster” should therefore be read as a favorable-workload claim, not a guarantee or service-level commitment. It does not mean GPT-4o’s underlying model becomes five times faster for every request. Nor does it establish that every part of the user-visible wait improves by that amount. Time to first token, the speed of subsequent tokens, total completion time, and end-to-end latency are different measures. Network delays, API queueing, prompt processing, file upload, and client-side rendering can remain unchanged and may dominate the experience.
Predicted Outputs does not remove the need to send and process the request. It is most promising when the output is long, the edit is narrow, and the prediction is an exact representation of the version being edited. Streaming may make output appear sooner, but benchmark it separately rather than assuming a particular multiplier.
How to use the API parameter
The core request shape is prediction with type: "content" and the expected output in content. The following JavaScript example uses the existing code as both input context and prediction:
import OpenAI from "openai";
const openai = new OpenAI();
const code = `
class User {
firstName = "";
lastName = "";
username = "";
}
export default User;
`.trim();
const completion = await openai.chat.completions.create({
model: "gpt-4o",
messages: [
{
role: "user",
content: 'Replace the "username" property with an "email" property. Return the entire file, with no markdown formatting.'
},
{ role: "user", content: code }
],
prediction: {
type: "content",
content: code
}
});
console.log(completion.choices[0].message.content);Keep the predicted text aligned with the exact artifact the model is meant to edit. If the application wants a complete file, say so explicitly: “Return the entire updated file. Do not return a diff, explanation, or Markdown code fence.” A prompt that invites a broad rewrite, diff, or commentary undermines the match.
Streaming can be enabled in the same Chat Completions request:
const stream = await openai.chat.completions.create({
model: "gpt-4o",
messages,
prediction: { type: "content", content: code },
stream: true
});
for await (const chunk of stream) {
process.stdout.write(chunk.choices[0]?.delta?.content || "");
}Consult the current guide for the complete request requirements and supported model identifiers before deploying. The documentation currently lists GPT-4o, GPT-4o mini, GPT-4.1, GPT-4.1 mini, and GPT-4.1 nano families. Availability is specific to the API and supported endpoint; it should not be inferred from ChatGPT model availability. Model names and support can change.
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Measure both speed and prediction quality
The response usage details include accepted_prediction_tokens and rejected_prediction_tokens. Accepted tokens show how much predicted content was used; rejected tokens show predicted content that did not appear in the completion. These counters help determine whether a workflow is actually a high-overlap case.
Benchmark prediction enabled versus disabled with the same model snapshot, prompt, source artifact, and comparable request conditions. Record time to first token and total request duration, and compare p50, p95, and p99 rather than relying on a single run. Also record streaming status, prompt and completion token counts, accepted and rejected prediction tokens, request ID, and output correctness. A faster but incorrect edit is not a successful optimization.
Check user-visible end-to-end time as well as API timing. If file upload, preprocessing, rendering, or waiting for the entire response before showing anything takes most of the time, a faster generation phase may barely change what the user experiences.
Cost: rejected prediction tokens still count
Predicted Outputs are not automatically cheaper. OpenAI’s guide says rejected prediction tokens are billed at completion-token rates. High overlap is therefore important for both speed and cost; a poor prediction may provide little latency benefit while adding billable rejected tokens.
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- Mixed overlap: Some speed benefit may remain, but measure whether it justifies the request’s token cost and implementation complexity.
- Low overlap: Frequent rewrites or stale predictions can produce many rejected tokens and little benefit.
Judge the feature by cost per successful user-visible edit at the required latency and quality—not by a token counter alone. If the task is straightforward, compare against a smaller model, deterministic application logic, or returning a patch rather than regenerating the whole document. Model prices change; check the GPT-4o model page and relevant model documentation for current rates rather than treating any quoted price as permanent.
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The current Predicted Outputs guide documents these constraints:
| Capability or setting | Documented support |
|---|---|
| GPT-4o and GPT-4o mini; GPT-4.1, GPT-4.1 mini, and GPT-4.1 nano | Supported model families |
| Text modality | Supported |
| Audio inputs or outputs | Not supported |
| Function calling | Not currently supported |
n greater than 1 |
Not supported |
logprobs |
Not supported |
| Positive presence or frequency penalties | Not supported |
max_completion_tokens |
Not supported |
These restrictions make the feature a poor fit for audio or multimodal requests, tool-heavy agent flows, multiple-candidate generation, and workflows that need log probabilities or those unsupported settings. If a tool call is necessary, one possible architecture is to perform tool selection without a prediction and use a separate text-only regeneration step afterward, if that extra stage still makes sense for the latency budget.
Common mismatch problems
- The edit expands into a rewrite. “Improve this document” gives the model latitude to change many passages. Narrow the requested edit and track rejected tokens.
- The source is stale. If a user edits the file while a request is in flight, the prediction may describe an older version. Tie it to the exact document version or content hash, and refresh or cancel stale requests.
- Formatting differs. Line endings, indentation, whitespace, escaping, or serialization changes can reduce token-level overlap despite semantic similarity. Preserve the original representation and benchmark the actual client-side content.
- The answer shape is wrong. A diff, explanation, or code fence does not match a predicted full file. Specify the desired complete output format.
Who should use Predicted Outputs?
Test it in code editors, refactoring tools, configuration editors, and document workflows that regenerate long artifacts after localized changes. It may also suit constrained grammar or style corrections where wording largely stays intact.
Be cautious with variable content pipelines, broad copy-editing, or requests that sometimes preserve a document and sometimes rewrite it. Historical acceptance rates and latency measurements can tell you whether to enable the feature selectively.
Prefer another approach for brainstorming, open-ended writing, summaries with unpredictable wording, changing retrieval-based answers, audio, tool-calling agents, and purely mechanical edits that deterministic code can perform more reliably. Predicted Outputs are most useful when the application already knows most of the answer; they are not a general fix for slow model calls.
For reference, OpenAI’s original GPT-4o announcement discussed a separate model-level speed comparison with GPT-4 Turbo. That claim is distinct from Predicted Outputs and should not be conflated with its workload-dependent latency effect. See also OpenAI’s latency optimization guidance for broader ways to address delays outside generation.
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