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aiqbench

Google · model passport

Google: Gemini 2.5 Pro Preview 06-05

gemini-2-5-pro-preview
  • 1,048,576 context
2/27axes covered
Best percentile
94.0MMLU-Pro (Artificial Analysis)
Composite standing
66.4shrunk · 2/27 axes

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: 6.2 percentile points between its best and worst axis — 94 pct on MMLU-Pro (Artificial Analysis) versus 87.8 pct on LiveCodeBench (Artificial Analysis).

  • MMLU-Pro (Artificial Analysis)94 pct85.8%Verify →
  • LiveCodeBench (Artificial Analysis)87.8 pct77.8%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
  • ARC-AGI-2Not 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
  • IFBench (Artificial Analysis)Not benchmarked
  • HHEM Hallucination (Vectara)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 IDgoogle/gemini-2.5-pro-preview
Context length1,048,576 tokens
Max output65,536 tokens
Acceptsfile, image, text, audio
Input price$1.25 per 1M tokens
Cached input price$0.13 per 1M tokens
Output price$10 per 1M tokens

API parameters this endpoint supports

Include reasoningMax tokensReasoningResponse formatSeedStop sequencesStructured outputsTemperatureTool choiceTool callingTop-p

Example request

curl https://openrouter.ai/api/v1/chat/completions \
  -H "Authorization: Bearer $OPENROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"google/gemini-2.5-pro-preview","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.

  • Gemini 2.5 Pro Preview (Mar' 25) stands at 66.4 (raw mean 90.9) — measured on only 2 of 27 axes, so the standing is shrunk toward the neutral 50.

    standingtemplateVerify →

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 →