ModelRefs / GPT-4o - AI model implementation reference

GPT-4o - AI model implementation reference

GPT-4o is an OpenAI multimodal model family exposed through hosted products and APIs. Its practical role depends on the selected endpoint and snapshot: text and image workflows are distinct from realtime or audio variants, and those surfaces should not be treated as interchangeable.

Overview

This page helps you evaluate GPT-4o, OpenAI's multimodal model family for text, image, and (on supported variants) audio — its practical role depends on the exact endpoint and snapshot you select, since realtime, audio, chat, and batch surfaces are not interchangeable.

Use this page to check which GPT-4o snapshot or modality-specific variant fits your workload, what latency, tool-use, and safety behavior to test under your intended endpoint configuration, and which benchmark and provider-documentation references to review before selecting it.

Five benchmark records for gpt-4o-2024-05-13 are source-verified. The editorial status remains Provisional only where OpenAI does not disclose benchmark execution dates, training-data composition, parameters, or a complete mutable-alias changelog; ModelRefs marks those fields unavailable instead of guessing.

Benchmark & Evaluation

ModelRefs currently has partial, narrow benchmark coverage for GPT-4o. Treat the available benchmark evidence as one input to the decision, not a guarantee that GPT-4o is the strongest option for your workload, and evaluate it on representative workloads before selecting it.

  • Provider-reported benchmark results should be interpreted with methodology, dataset, prompting, tool, sampling, and recency limitations in mind.
  • Five source-scoped canonical records now cover SWE-bench Verified, MMLU, GPQA, SimpleQA, and MGSM for gpt-4o-2024-05-13. The OpenAI simple-evals publication and public harness support the reported scores, but OpenAI does not disclose the evaluation execution dates and ModelRefs does not infer them from publication dates.

Implementation considerations

  • Select the exact GPT-4o snapshot or modality-specific variant.
  • Test safety, latency, tool behavior, and modality handling under the intended endpoint configuration.
  • Hosted through OpenAI products and supported API endpoints.
  • Realtime, audio, chat, and batch paths can have different capabilities, data flows, and costs.

Architecture disclosure

  • OpenAI describes GPT-4o as an autoregressive omni model; deployed endpoints expose narrower modality combinations.
  • OpenAI does not disclose the snapshot's parameter count, layer design, tokenizer specification, training compute, or serving topology.

Prompts and code examples

Version-pinned Responses API request

import OpenAI from "openai";

const client = new OpenAI();
const response = await client.responses.create({
  model: "gpt-4o-2024-05-13",
  input: [
    {
      role: "user",
      content: [
        { type: "input_text", text: "Return three concise risks of deploying an untested model." },
      ],
    },
  ],
});

console.log(response.output_text);

Provider implementation source

Training disclosure

  • OpenAI has not published a complete training-data inventory, compute ledger, parameter count, or reproducible recipe for this snapshot.
  • API customer data is not used for training unless the customer opts in; this policy does not describe GPT-4o's original corpus.
  • Fine-tuning support is snapshot-specific; reviewed documentation does not establish it for gpt-4o-2024-05-13.

History and source-reviewed changelog

  1. 2024-05-13 — GPT-4o announced

    OpenAI announced GPT-4o, initially releasing text and image input with text output while staging broader modalities.

    Milestone source
  2. 2024-08-08 — GPT-4o System Card published

    OpenAI published the System Card covering design, red teaming, evaluations, and safety mitigations.

    Milestone source
  3. 2024-11-21 — Pinned simple-evals evidence snapshot

    The reviewed simple-evals revision supplies four ModelRefs benchmark records. Its date is publication provenance, not execution time.

    Milestone source

Risks and limitations

  • Outputs can be incorrect or unsuitable for the intended task; use task-specific evaluation, grounding, and human review where consequences are material.
  • API availability, model aliases, rate limits, data controls, regions, and prices are mutable and differ by product channel.

Source coverage

ModelRefs has verified the listed first-party sources and five qualifying benchmark results for the exact GPT-4o snapshot. The remaining constraint is upstream: OpenAI does not publish the benchmark execution dates, so ModelRefs preserves those fields as undisclosed rather than estimating them.

Connected ModelRefs evidence

Sources

Continue your research

Use these connected ModelRefs sections to compare alternatives, inspect implementation paths, and review the evidence and governance boundaries relevant to GPT-4o - AI model implementation reference.