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aiqbench

OpenAI · model passport

OpenAI: o3 Pro

o3-pro
  • 200,000 context
  • Released 2025-06-10(curated)
2/27axes covered
Best percentile
34.8ARC-AGI-2
Cohort standing
37.5median 50.9

Thin coverage — read with caution

This model appears on only 2 of 27 benchmark axes considered. The overall standing has been shrunk toward the neutral 50 to reflect the small sample, and no weaknesses have been claimed — a low percentile on 2 axes is a coverage artifact, not a measured weakness.

What the data says

Benchmarked on 2 of 27 tracked axes. Every figure below is the model's own published result on that board.

On only 2 board(s), a ranked list of strengths would be false authority — the per-axis figures are shown below instead, without a verdict.

Uneven: 31.9 percentile points between its best and worst axis — 34.8 pct on ARC-AGI-2 versus 2.9 pct on HHEM Hallucination (Vectara).

  • ARC-AGI-234.8 pct4.9%Verify →
  • HHEM Hallucination (Vectara)2.9 pct76.7%Verify →
  • OverallNot benchmarked
  • WebDev ArenaNot benchmarked
  • Vision ArenaNot benchmarked
  • Factuality ArenaNot benchmarked
  • Search ArenaNot benchmarked
  • Document ArenaNot benchmarked
  • Agent ArenaNot benchmarked
  • GPQA DiamondNot benchmarked
  • AIME (math)Not benchmarked
  • FrontierMathNot benchmarked
  • SimpleQA VerifiedNot benchmarked
  • Humanity's Last ExamNot benchmarked
  • SimpleBenchNot benchmarked
  • LiveBenchNot benchmarked
  • BFCL v4 (function calling)Not benchmarked
  • SWE-bench Verified (live board)Not benchmarked
  • Terminal-Bench 4.0Not benchmarked
  • MMLU-Pro (Artificial Analysis)Not benchmarked
  • LiveCodeBench (Artificial Analysis)Not benchmarked
  • IFBench (Artificial Analysis)Not benchmarked
  • SWE-bench VerifiedNot benchmarked
  • Terminal-BenchNot benchmarked
  • CodingNot benchmarked
  • Instruction followingNot benchmarked
  • Hard promptsNot benchmarked

Percentiles are the model's standing within each board's own population. Axes marked "not benchmarked" have no published result for this model — that is an absence of data, not a low score.

How to run it

Use it hosted

API model IDopenai/o3-pro
Context length200,000 tokens
Max output100,000 tokens
Acceptstext, file, image
Input price$20 per 1M tokens
Output price$80 per 1M tokens

API parameters this endpoint supports

Include reasoningMax tokensReasoningResponse formatSeedStructured outputsTool choiceTool calling

Example request

curl https://openrouter.ai/api/v1/chat/completions \
  -H "Authorization: Bearer $OPENROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"openai/o3-pro","messages":[{"role":"user","content":"Hello"}]}'

View on OpenRouter →

Identifiers, limits and prices are synced from the upstream source shown above. Any field we haven't synced is left out entirely rather than guessed.

  • o3-pro-2025-06-10 stands at 37.5 (raw mean 18.8) — measured on only 2 of 27 axes, so the standing is shrunk toward the neutral 50.

    standingtemplateVerify →

  • o3-pro-2025-06-10 stands at 37.5 versus a cohort median of 50.9 across 142 models released within 180 days — below the peer median.

    cohorttemplateVerify →

Every claim is a computed fact from the current board data, phrased by a low-temperature model call and rejected by a validation gate if any number is not in the source row. How the leaderboards work →