benchmark · model-level

Artificial Analysis

Intelligence Index·22 models·homepage ↗

Rank · benchmarks#17 / 17

Score26

How it works

Artificial Analysis runs a standard battery of evals (reasoning, knowledge, math, etc.) and blends them into a single 'Intelligence Index' per model. A general-capability proxy, not coding-specific.

Metric

Intelligence Index

Models

Type

Model-level

How we ranked it

composite 26 / 100

Every benchmark is scored on four weighted criteria — how directly it measures a real coding agent, how much of the stack it covers, how real its tasks are, and how open and reproducible it is. Those blend into the composite that ranks it #17 of 17.

Agent-nativeweight 30%

Scores a real (harness × model) pair — the full agent — not just the bare model.

Stack coverageweight 30%

Data-driven: this benchmark exposes 22 models.

Task realismweight 25%

Executable, real-world coding tasks with hard pass/fail — not human preference or aggregate scores.

Open & reproducibleweight 15%

Open data with per-run receipts you can audit.

Our review

A convenient one-number summary of raw model capability, but it's model-level, not coding-focused, and an aggregate-of-aggregates whose methodology is only semi-open. We show it for orientation; it barely touches the agentic-coding axis this site cares about.