What it measures

Vals AI's economy-weighted multimodal composite across financial charts/certificates, visual coding, and education.

Reading the measurement

The choice of components and weights defines this aggregate. Inspect the individual tasks to see whether the model improved broadly or gained on one heavily weighted component. Compare within the same index revision and keep cost, latency and reliability visible separately.

Published evaluation

The linked publication documents the task and scoring procedure. Model results retain the source’s evaluation setting, native score units and available uncertainty.

Where this fits in an evaluation suite

Ask what the task actually requires: recall, deduction, search, or a mixture. Separate closed-book reasoning from retrieval and tool-assisted problem solving.

Start with a capability hypothesis, then choose a second test with a different failure mode. Keep the checkpoint, reasoning effort, agent tools, sampling policy, and scoring procedure fixed when measuring a training intervention. Record failures and missing outputs in the denominator.

A reproducible comparison

Record the source publication, benchmark version, scoring units, denominator, tool access and attempt budget. Preserve uncertainty and distinguish developer-reported results from independent evaluations.

Publish the exact run configuration, item coverage, score units, and available uncertainty. If a source supplies only an aggregate or a single run, preserve that limit. A new model name or a rounded score alone does not establish a capability improvement.

PRIMARY EVIDENCE & FURTHER READING

  1. Vals Multimodal Index — evaluation methodology and results ↗

    Vals AI · 2026

Reviewed 2026-10-26. Explanation and interpretation by RLTasks. Benchmark ownership remains with its original authors.