# AI IQ

> Intelligently measuring AI intelligence. Compare AI models across composite IQ, its seven scored dimensions, speed, cost, and experimental EQ diagnostics with interactive charts and benchmarks.

AI IQ (aiiq.org) is an open data project by Liberated Software LLC that assigns composite IQ scores to AI models using public benchmarks across seven scored capability dimensions, then visualizes tradeoffs against cost and speed. Manual source captures are archived separately from extracted values so benchmark updates can be re-parsed later.

## Data API

Public API documentation is available at:

- [API Documentation](https://www.aiiq.org/docs/api/): Endpoint documentation, example responses, and privacy boundary.
- **Canonical base URL**: https://www.aiiq.org/api/v1/ (permanent alias; bare https://www.aiiq.org/api/ also works)
- **OpenAPI 3.1 spec**: https://www.aiiq.org/api/openapi.json (machine-readable endpoint and schema definitions)

Every endpoint returns a versioned envelope: `{ "apiVersion": "1", "methodologyVersion": "...", "updatedAt": "...", <data> }`. Collection endpoints place their array under a named key (`models`, `benchmarks`, `rankings`, `charts`); the model-detail endpoint places model fields at the top level alongside the envelope keys.

Structured JSON data is available at:

- [Models](https://www.aiiq.org/api/models): Sanitized public model summaries with stable IDs, rank, composite IQ, a per-dimension IQ map, a cost block (input/output/effective cost per 1M tokens), timestamp, and canonical URL.
- [Model Detail](https://www.aiiq.org/api/models/gpt-5.5): Detailed public model data with full per-dimension blocks (IQ + coverage), the cost block, source-backed benchmark results, and methodology version.
- [Benchmarks](https://www.aiiq.org/api/benchmarks): Benchmark metadata including the dimension each benchmark feeds, direction, and unit.
- [Rankings](https://www.aiiq.org/api/rankings): Ranking catalog (Composite IQ, Effective Cost, seven per-dimension IQ rankings, per-benchmark rankings) with model counts and detail URLs; fetch one full leaderboard from /api/rankings/:id.
- [Charts](https://www.aiiq.org/api/charts): Canonical chart metadata.
- [Methodology](https://www.aiiq.org/api/methodology): Public methodology metadata.
- [Domains](https://www.aiiq.org/api/domains): Applied-capability domains tracked outside Composite IQ (cybersecurity, bio, machine-learning); /api/domains/:slug returns per-model domain composite IQs plus the domain's benchmark leaderboards with model + harness rows.

### Schema

Each model summary object contains:

| Field | Type | Description |
|---|---|---|
| id | string | Stable model identifier, e.g. "gpt-5.5" |
| name | string | Human-readable model name |
| provider | string | Public provider grouping |
| rank | number\|null | Current Composite IQ rank when ranked |
| iq | number\|null | Rounded Composite IQ |
| dimensions | object | Map of the seven scored dimension slugs to rounded dimension IQ (`null` when a dimension is unscored) |
| emotionalReasoning | number\|null | Experimental diagnostic Emotional Reasoning (EQ) score, excluded from Composite IQ |
| cost | object | Cost block: `inputPer1M`, `outputPer1M`, `ioPer1M`, `usageMultiplier`, `effectivePer1M`, `unit` |
| updatedAt | string | ISO update timestamp |
| url | string | Canonical public model profile URL |

The seven scored dimension slugs are `abstract-reasoning`, `mathematical-reasoning`, `scientific-reasoning`, `frontend-engineering`, `backend-engineering`, `computer-use`, and `reliability`. (Before 2026-07-09 the D4/D5 slugs were `app-building` and `production-engineering`; the old slugs no longer appear in API responses.) Emotional Reasoning (EQ) is retained as an experimental diagnostic field; the standalone EQ field and the `composite-eq` ranking have been retired.

## IQ Methodology

IQ scores are computed from benchmarks organized into seven scored capability dimensions:

- **Abstract Reasoning**: ARC-AGI-3, ARC-AGI-2, ARC-AGI-1
- **Mathematical Reasoning**: FrontierMath Tier 4, FrontierMath Tier 1-3, ProofBench, MathArena, AIME
- **Scientific Reasoning**: Humanity's Last Exam, CritPt, SciCode, GPQA Diamond
- **Frontend Engineering**: Arena.ai WebDev, DesignArena Frontend, DesignArena Full Stack, Vibe Code Bench
- **Backend Engineering**: LiveCodeBench, FrontierCode Diamond, SWE-Bench Verified, SWE-Bench Pro, DeepSWE, SWE-rebench, SWE Marathon
- **Computer Use**: Terminal-Bench 2.1, Terminal-Bench Hard, BrowseComp, OSWorld-Verified, Toolathlon, MCP Atlas, Agents' Last Exam
- **Reliability**: IFBench, AA Omniscience, BullshitBench v2, AA Long Chain Reasoning, FACTS Grounding

Each benchmark score is mapped to an IQ-equivalent via piecewise-linear interpolation through an expected-score ladder. Every scored benchmark defines the source score expected at IQ 70, 85, 100, 115, 130, 145, and 160; saturated or gameable benchmarks are constrained by the shape of that ladder rather than by a separate cap. The current values are the first benchmark-specific calibration pass after the initial compatibility conversion; some high-end expected scores remain intentionally off-scale where saturation, contamination, or benchmark age make a perfect in-range score insufficient for IQ 145 or 160. Missing benchmark values are imputed only inside the composite IQ scoring copy: source-backed values come first, then explicit direct-predecessor `imputeFrom` values, then `min(within-dimension benchmark average, benchmark p80)` when a dimension is partially covered.

Derived IQ requires at least 2 scored dimensions and always averages all 7 scored dimensions. Missing whole dimensions use a matched lower-quartile waterfall: comparable models with real data for the missing dimension and similar other-dimension capability, then nearest comparable models, then the global dimension lower quartile. If no real data exists for that dimension, no neutral default is invented and the model does not receive a derived all-dimension IQ. Emotional Reasoning (EQ) is computed separately from EQ-Bench 3, Arena.ai Overall, and AttuneBench as a diagnostic score excluded from Composite IQ.

Raw benchmark fields in the public dataset remain source-backed. Imputed benchmark and dimension values are used for derived IQ scoring only; composite IQ, dimension IQs, and effective cost are runtime-derived values.

## Pages

- [Home](https://www.aiiq.org/): Interactive charts — IQ bell curve, IQ vs cost, IQ vs speed, benchmark comparisons
- [Methodology](https://www.aiiq.org/methodology/): Full scoring methodology with IQ 70-160 expected-score ladders and math
- [API](https://www.aiiq.org/docs/api/): Public API documentation
- [Models](https://www.aiiq.org/models/): Public model directory with links to model profile pages
- [Dimensions](https://www.aiiq.org/dimensions/): Index of the seven scored IQ dimensions; each dimension has a top-level page (e.g. https://www.aiiq.org/backend-engineering/)
- [ML Benchmarks](https://www.aiiq.org/machine-learning/): Machine Learning Engineering domain leaderboards (WeirdML, PostTrainBench, KernelBench, GSO-Bench), tracked outside Composite IQ
- [Cybersecurity](https://www.aiiq.org/cybersecurity/): Cybersecurity domain benchmarks and leaderboards
- [Bio](https://www.aiiq.org/bio/): Biology domain benchmarks and leaderboards
- [Benchmarks](https://www.aiiq.org/benchmarks/): Raw third-party benchmark charts grouped by dimension
- [Tradeoffs](https://www.aiiq.org/tradeoffs/): Cost and speed tradeoff charts against IQ
- [More Charts](https://www.aiiq.org/more/): Supplementary and experimental visualizations