ModelRefs / o3 by OpenAI — Benchmarks, Pricing & Review (2026)

o3 by OpenAI — Benchmarks, Pricing & Review (2026)

o3 (OpenAI): o3 is OpenAI's extended-thinking reasoning model, applying deliberate chain-of-thought computation to excel on mathematics, science, and competi…

What this reference supports

o3 is an OpenAI reasoning model intended for complex tasks — analysis, planning, mathematics, coding, and tool-assisted problem solving — where additional inference-time reasoning improves output quality over standard GPT models, at the cost of higher latency and token spend per response, so it is not a drop-in replacement for lighter-weight chat models.

Use this page to compare o3 against non-reasoning GPT models and against the smaller o4 Mini on latency, cost, and output behavior, and to check which reasoning benchmarks such as GPQA carry sourced, unqualified evidence for this specific model alias rather than its separately labeled high- or low-reasoning variants.

Reasoning models differ from standard GPT models in prompting, latency, and cost — test explicit reasoning-effort settings and verification workflows against a non-reasoning baseline, and confirm current endpoint availability and rate limits before committing to a production integration, since reasoning-model access can lag behind standard GPT endpoints and carry stricter usage tiers.

Benchmark & Evaluation

ModelRefs currently has partial, narrow benchmark coverage for o3. Treat the available benchmark evidence as one input to the decision, not a guarantee that o3 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.
  • G.18 registers OpenAI's unqualified simple-evals GPQA row for the o3 alias. The separately labeled high- and low-reasoning rows remain outside this claim, and the narrow provider-run evidence limits eligibility to Partial.

Implementation considerations

  • Use outcome-focused prompts and test reasoning-effort settings.
  • Measure latency, tool behavior, error recovery, and verification cost against a non-reasoning baseline.
  • Hosted through supported OpenAI API products.
  • Model availability and endpoint features can be superseded; confirm the current catalog.

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:

  • Independent task-level reasoning evaluations are incomplete.
  • Current reasoning-effort and tool support needs endpoint-specific review.

Sources

Continue your research

Use these connected ModelRefs sections to compare alternatives, inspect implementation paths, and review the evidence and governance boundaries relevant to o3 by OpenAI — Benchmarks, Pricing & Review (2026).