How the overall index (Gradually Meta-Score v5) works

Right now 3,623 measurements from 84 test series go in. 114 models have an overall value, 94 of them a place. A test series is one benchmark that several models have taken.

• Only independent evaluators and official leaderboards count. A number from the maker's own model card does not go in.
• Only each source's current listing counts. Older retrievals do not replace a newer series.
• For each source and benchmark one test setup counts, the one in which the most models are clearly matched. There is no picking of the best setup per model.
• If a model has several configurations in the same benchmark, the one with the highest documented reasoning effort counts. The result never decides the selection.
• A series needs at least 5 models.
• Composite indices from other providers do not count on top of their individual tests, so nothing goes in twice.

• Each series is brought to a common scale: a value counts by how far it lies above or below the average of its series. Percentages are stretched so that the gap between 95 and 98 percent counts for more than the gap between 50 and 53.
• From all series the index estimates one common strength for each model. Series that separate strong and weak models well count for more.
• If a model has only a few test series so far, the index pulls its strength slightly towards the middle. That way a single outlier does not lift a new model straight to the top. Since version 5 this pull is weaker: a model with 8 test series lands on average where it would stand with all series, instead of about 1.5 to 2 points below as up to version 4.
• On top of that comes one deviation per area from the common strength. If a model has only a few measurements in an area, the deviation is pulled towards zero. The areas are coding, reasoning, math, knowledge, agents, general tasks and multimodal tasks (core areas) and finance, law and health.
• The overall value weights the 7 core areas with 75% and the 3 other areas together with 25%. An area without a measurement of its own counts with the model's common strength, not as zero.

Value

100 is the average of a fixed reference group of models. A gap of 15 points means the higher model comes out ahead in roughly three of four head-to-head comparisons in a typical benchmark. The scale is open at both ends and is not a percentage.

Place

The position among all models with a place, sorted by overall value. A model gets a place only when its value is certain enough: the uncertainty may be at most 7.35 points. Otherwise the value is shown as provisional.

Performance groups

The top group (3 models) holds the models from place 1 that are level with the winner. Then come the upper group from 115 points (7 models), the middle group from 85 points (41) and below it the lower group (43).

Level

A model is level with the winner if it lands ahead of it in at least 5% of the recalculations. For this the index recalculates 1,000 times, each time letting test families count several times or not at all at random.

Win chance

The share of those recalculations in which a model is in first place. The win chances of one area add up to at most 100%.

An overall value needs at least 8 test series, 2 sources and 3 areas with measurements of their own. If any of these is missing, there is no value. That is a gap, not a zero.

Models that lack only the second source show a provisional value, always labelled “one source” (17 models). They get no place, no group and no win chance, and they do not move the scale.

Models without an independent overall value can get an estimate from vendor figures. It is shown separately and never goes into the index. It appears only when enough models are available to calibrate it and the estimate reproduces their order well enough.

Vals AI supplies 58% of all observations in the index. If the index is recalculated without this source, Claude Opus 5.5 leads instead of Claude Fable 5.2.

The top therefore depends on a single source. Read it as a hint, not a verdict.

• Models with few measurements carry more uncertainty. New models therefore often get a place only after a few more tests, and their value can still move by several points until then.
• The vendor estimate tends to compress the top: it rates very strong models slightly too low. The difference is still within the uncertainty, so it is not established.
• Each series uses the highest documented reasoning effort. Some top models are measured only up to a lower effort, so the index partly compares compute too. The charts mark this.
• Benchmarks measure tasks that someone picked. Your task may look different.

1. Version 1October 19, 2026

   Rank percentiles, every area weighted equally.

2. Version 2October 20, 2026

   Fixed editorial weights per area and test family.

3. Version 3October 20, 2026

   Statistical model with one common strength per model, recalculations instead of fixed tie limits.

4. Version 4November 4, 2026

   Scale with 100 as the mean and 15 points as the spread, unmeasured areas treated as unknown, evaluation with held-out test families.

5. Additions in version 4November 10, 2026

   More recalculations, performance groups, a note on the share of the largest source and provisional values for models with a single source.

6. Version 5November 11, 2026

   A weaker pull towards the middle for models with few test series. The limit for a place stays the same in points. Values from versions 4 and 5 are not directly comparable.

The newest measurement in the index is from November 10, 2026.

• Audit file (JSON)

  all included and excluded measurements with reasons

• Open data (CSV)

  the overall index as a table

• Open data (JSON)

  the same dataset as JSON

• Data status

  how recent each dataset is

• LLM comparison

  the index in the leaderboard

• Best LLM by task

  the area values as their own rankings

• Compare multiple LLMs

  up to five models side by side