Intelligence Observatory · 2025

    The AI
    models we
    actually trust

    Quan Bench profiles leading AI models across six dimensions of real business intelligence. Not synthetic benchmarks - applied judgment. We publish our scores openly.

    Continuously updated
    10 models profiled
    200+ prompts / dimension
    Intelligence vs Efficiency — Model Map→ scatter
    EFFICIENCY →INTELLIGENCE →BalancedDeepFastSlow

    Model Profiles

    Score scale0 → 100|● = Quansynd's Pick
    No models match
    Methodology

    How we measure intelligence

    Six axes. Weighted by business impact. Evaluated across 200 standardized prompts per dimension, re-run on every major model release.

    01 · 20%

    Reasoning

    Multi-step deduction, causal inference, and structured decomposition of ambiguous problems.

    Highest weight
    02 · 20%

    Accuracy

    Factual correctness and hallucination resistance across a curated set of verifiable knowledge tasks.

    Highest weight
    03 · 18%

    Contextual Grasp

    Coherence over long-form, multi-constraint prompts and extended conversation chains.

    High weight
    04 · 17%

    Reliability

    Consistency across repeated identical prompts and resistance to adversarial edge cases.

    High weight
    05 · 15%

    Efficiency

    Precision over volume - delivering concise, targeted responses without unnecessary verbosity.

    Medium weight
    06 · 10%

    Creativity

    Novel framing and original output generation under open-ended, unconstrained briefs.

    Base weight
    The Quan Score is a weighted composite. Scores are normalized 0-100. Updated when a major model version releases or when cumulative evidence warrants re-evaluation. This is Quansynd's internal evaluation framework made public.