Methodology

The index comes from public benchmark results.

Index method

  1. We compare each result with the results of other models on the same benchmark.

  2. We add the results in eight categories. Each category has a weight.

    Agentic
    20%
    Coding
    20%
    Reasoning
    15%
    Multimodal
    10%
    Knowledge
    15%
    Multilingual
    5%
    Instruction
    5%
    Math
    10%
  3. A model gets an index when it has results from 8 benchmarks in 4 categories.

  4. The ± range shows how much an index can change. Lists show models in order of index.

  5. “Above fit” marks a result far above the model’s index.

Example

The method on the top model.

Model
Claude Fable 5.2
Results counted
37
Categories
6 of 8
Coverage
90%
Index
85.6 ±3.7
Place
1 of 283

By category

Its index per category, out of 100.

Agentic
82.2
Coding
86.2
Reasoning
86.5
Multimodal
82.0
Knowledge
87.1
Multilingual
—
Instruction
—
Math
79.3