The Public Standard for
Real-World AI Benchmarks

Generic benchmarks only go so far.
Vals AI evaluates models on the real tasks each industry relies on.

Vals AI runs 35 independent AI benchmarks across legal, finance, healthcare, coding, math, and more. Each leaderboard compares large language models (LLMs) on accuracy, cost, and latency, and each methodology page explains how the benchmark is run.

As of October 19, 2026, Claude Sonnet 5.5 ranks first on the Vals Index with 70.85%, followed by Claude Opus 5.5 (70.18%) and Gemini 4 Argon (69.24%). See the full Vals Index leaderboard.

Vals Indexes

Vals Multimodal Index

Benchmark consisting of a weighted performance across finance, coding, and education tasks. Showing the potential impact that LLMs can have on the economy.

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Finance benchmarks

Healthcare benchmarks

Mental Health benchmarks

Math benchmarks

AIME

Challenging national math exam given to top high-school students

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MATH 500

Academic math benchmark on probability, algebra, and trigonometry

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MGSM

A multilingual benchmark for mathematical questions.

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Science benchmarks

Academic benchmarks

GPQA Diamond

Graduate-level Google-Proof Q&A benchmark evaluating models on questions that require deep reasoning.

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MMLU Pro

Academic multiple-choice benchmark covering 14 subjects including STEM, humanities, and social sciences.

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MMMU Pro

Multimodal Multi-task Benchmark

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Education benchmarks

Voice benchmarks

Coding benchmarks

Code Migration

Can language models reimplement working programs in another language?

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Terminal-Bench 2.1

State-of-the-art set of difficult terminal-based tasks

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SWE-bench Verified

Solving production software engineering tasks

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LiveCodeBench

Our Implementation of the LiveCodeBench benchmark

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Terminal-Bench 2.0

State-of-the-art set of difficult terminal-based tasks

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Cyber benchmarks

Games benchmarks

Social Mobility benchmarks