ModelRefs / GPT-4o by OpenAI — Benchmarks, Pricing & Review (2026)

GPT-4o by OpenAI — Benchmarks, Pricing & Review (2026)

GPT-4o (OpenAI): GPT-4o by OpenAI.. 128K context. Pricing: from $0.00250/1K in. Specs, benchmarks and code examples.

What this reference supports

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.

Benchmark and evidence coverage for this profile should be checked, not assumed complete, and endpoint-by-endpoint modality coverage is not fully mapped here. Confirm current pricing, rate limits, and data-handling terms with OpenAI's own documentation before selection.

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.
  • The system card documents first-party evaluation context and limitations; existing canonical benchmark records remain separately governed.

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.

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

This reference is Provisional. Model behavior, access, pricing, limits, and lifecycle can change; verify the linked provider documentation and run task-specific evaluations before implementation.

Known coverage gaps:

  • Endpoint-by-endpoint modality coverage is not fully mapped.
  • Independent safety and reliability evaluations are not attached at claim level.

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 by OpenAI — Benchmarks, Pricing & Review (2026).