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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
0 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.