SEAL LLM Leaderboard

The expert-driven evaluation list produced by the labs under Scale AI covers more than 20 benchmarks and over 100 models including agent programming, cutting-edge reasoning and safety alignment. It can be browsed by capability tiers and is continuously updated.

AI Research Tools#Leaderboard#Reviews#LLM

Updated: 2026-10-26

What is SEAL LLM Leaderboard?

The SEAL LLM Leaderboard is a collection of AI model evaluation rankings launched by Scale Labs, a subsidiary of Scale AI, with the positioning of "testing the limits of AI". Unlike benchmarks that rely purely on automatic scoring, it emphasizes expert-driven evaluation: evaluation tasks are designed and annotated by domain experts, reducing the "score padding" problem that is common in automatic benchmarks. Currently, it includes more than 20 benchmarks and evaluates over 100 models, covering areas such as agent programming, cutting-edge reasoning and safety alignment. The models come from major labs like OpenAI, Anthropic, Google, Meta, as well as open-source contributors.

Who is it for?

Technical teams and architects who need to select models for their own scenarios; engineers and analysts who need to cite authoritative benchmark data to support selection reports; and researchers who focus on the real capability boundaries of cutting-edge models. The rankings can be browsed by All / Agentic / Safety / Frontier tiers, allowing quick location of the capability dimensions of interest.

Key features

Expert-driven question creation

Benchmark tasks are designed and annotated by domain experts. For example, DrugDiscoveryBench includes 82 expert-curated tasks that evaluate the reliability of cutting-edge coding agents in executing computational tasks for early-stage drug discovery.

Agentic Programming Evaluation

Covers real-world codebase-focused benchmarks such as SWE-Bench Pro (divided into public and private datasets) and SWE Atlas (refactoring, test writing, codebase Q&A), testing the practical implementation capabilities of coding agents.

Frontier Reasoning Benchmarks

Includes high-difficulty general reasoning tests such as Humanity's Last Exam to explore the boundary of model capabilities.

Professional Domain Benchmarks

It features professional reasoning benchmarks for finance and law, as well as vertical tasks such as drug discovery, which are close to the real needs of the industry.

Agentic and Safety Focus

Includes agent-related evaluations such as HiL-Bench (Human-in-the-Loop Benchmark), MCP Atlas, and SciPredict, as well as capability validation for the Safety alignment direction.

Audio Multimodality

AudioMultiChallenge covers audio multi-turn dialogue and audio output capabilities.

How to use SEAL LLM Leaderboard

The leaderboard has many benchmarks and a large amount of data.
It is recommended to first filter by the capability dimensions you care about (such as agent programming or professional reasoning), and then check the specific rankings.
The same benchmark usually has two versions: public dataset and private dataset.
The difference in scores between the two is also important information, which can reflect the generalization and robustness of the model.
When it comes to specific model selection, use the leaderboard as a tool to narrow down the candidate range, and then conduct a small-scale verification combined with your own real tasks.

Things to know

The ranking score is bound to the specific model version and evaluation environment. When citing, please indicate the benchmark version and evaluation time; the expert benchmarks cover a limited range of task types and cannot represent all usage scenarios, especially the long-tail and vertical scenarios which require separate verification; some benchmarks have two sets of public and private data, and the scores on the public list may be higher than the performance on private data, so pay attention to the data source when referring; the ranking list is maintained by Scale Labs, and the evaluation methods and selected models shall be subject to the official website.