Scale Ai Launches Seal Leaderboards For Expert Driven Llm Evaluations

Bonisiwe Shabane

-Jan 2, 2026, 6:29 PM

scale ai launches seal leaderboards for expert driven llm evaluations

SEAL LLM Leaderboards evaluate frontier LLM capabilities. These leaderboards provide insight into models through robust datasets and precise criteria to benchmark the latest AI advancements. Gemini 2.5 Pro Experimental (March 2025) Claude 3.7 Sonnet (Thinking) (February 2025) Gemini 2.5 Pro Experimental (March 2025) Gemini 2.5 Pro Experimental (March 2025)

Scale AI has announced the launch of SEAL Leaderboards, an innovative and expert-driven ranking system for large language models (LLMs). This initiative is a product of the Safety, Evaluations, and Alignment Lab (SEAL) at Scale, which is dedicated to providing neutral, trustworthy evaluations of AI models. The SEAL Leaderboards aim to address the growing need for reliable performance comparisons as LLMs become more advanced and widely utilized. With hundreds of LLMs, comparing their performance and safety has become increasingly challenging. Scale, a trusted third-party evaluator for leading AI labs, has developed the SEAL Leaderboards to rank frontier LLMs using curated private datasets that cannot be manipulated. These evaluations are conducted by verified domain experts, ensuring the rankings are unbiased and provide a true measure of model performance.

The SEAL Leaderboards initially cover several critical domains, including: Each domain features prompt sets created from scratch by experts, tailored to evaluate performance in that specific area best. The evaluators are rigorously vetted, ensuring they possess the necessary domain-specific expertise. To maintain the integrity of the evaluations, Scale’s datasets remain private and unpublished, preventing them from being exploited or included in model training data. The SEAL Leaderboards limit entries from developers who might have accessed the specific prompt sets, ensuring unbiased results. Scale collaborates with trusted third-party organizations to review their work, adding another layer of accountability.

Scale AI, a data labeling startup that serves nearly all the leading AI models, has closed a $1 billion Series F financing round for a reported valuation of $13.8 billion with nearly all existing... Founded by MIT dropout Alexandr Wang, Scale AI’s latest funding was led by existing investor Accel with participation from Y Combinator, Nat Friedman, Index Ventures, Founders Fund, Coatue, Thrive Capital, Spark Capital, NVIDIA, Tiger... New investors are Amazon, Meta, AMD Ventures, Qualcomm Ventures, Cisco Investments, Intel Capital, ServiceNow Ventures, DFJ Growth, WCM, and Elad Gil. “In 2016, I was studying AI at MIT. Even then, it was clear that AI is built from three fundamental pillars: data, compute, and algorithms. I founded Scale to supply the data pillar that advances AI by fueling its entire development lifecycle,” Wang wrote in a blog post.

Since then, Scale AI has grown in scope to supply data to the AI models of OpenAI, Meta, Microsoft and others. Last August, OpenAI named Scale AI as its preferred partner to help clients fine-tune OpenAI models for their own purposes. Nice, a serious contender to @lmsysorg in evaluating LLMs has entered the chat. LLM evals are improving, but not so long ago their state was very bleak, with qualitative experience very often disagreeing with quantitative rankings. This is because good evals are very difficult… LLM evals are the hot topic in AI right now, and the work @scale_AI is doing is helping shape the frontier!

@danielxberrios @summeryue0 📣 Scale is excited to release the SEAL leaderboards today, kicking off the first truly expert-driven, trustworthy LLM contest open to all: https://lnkd.in/g4K5mdfC Compared to existing benchmarks, these leaderboards developed by our Safety, Evaluations,... These leaderboards are regularly updated to include new models and capabilities. Our goal is to foster a culture of transparency and openness in the development and evaluation of frontier models. 👉 Finally, we are also announcing the general availability of Scale Evaluation: a platform to enable organizations to evaluate and iterate on their AI models and applications. Learn more: https://lnkd.in/g9SuEsaN 👈 Check out the leaderboard yourself here: https://lnkd.in/gffe85mg And learn more about the development and motivation behind the leaderboards: https://lnkd.in/g9W9SJH5

AI Weekly News: Stay current without the noise May 29, 2024: Scale AI Introduces LLM Performance Rankings - Scale AI launches its first SEAL Leaderboards, ranking large language models (LLMs) on performance across specific domains like coding, multilinguality, and math. OpenAIs GPT models and Googles Gemini excel, with Anthropics Claude 3 Opus leading in math. The rankings, aimed at providing transparency in AI capabilities, derive from evaluations using private datasets and are set to update periodically, including new models and domains. Datagrom keeps business leaders up-to-date on the latest AI innovations, automation advances, policy shifts, and more, so they can make informed decisions about AI tech. Scale AI offers new leaderboards based on its own benchmarks.

What’s new: Scale AI, which helps companies prepare and manage training data, introduced the Safety, Evaluations and Alignment Lab (SEAL) Leaderboards. Four leaderboards test models’ abilities to (i) generate code, (ii) work on Spanish-language inputs and outputs, (iii) follow detailed instructions, and (iv) solve fifth-grade math problems. The company currently tests 11 models from Anthropic, Google, Meta, Mistral, and OpenAI. Developers who want to have their model ranked can contact Scale AI via email.How it works: The leaderboards track performance on proprietary datasets of roughly 1,000 examples. In all but the math tests, models to be evaluated are grouped and pitted against each other. Each pair receives 50 prompts at a time.

Human annotators evaluate the models’ responses and grade which was superior and by how much. Then the models receive another 50 prompts. Models are ranked using a variation on Elo, which scores competitors relative to each other. To keep the test sets from leaking, a given model will be tested only once except in “exceptional cases” where Scale AI believes the risk of overfitting is low. Results: As of this writing, GPT-4 Turbo tops the Coding leaderboard with GPT-4o a very close second. GPT-4o tops the Spanish and Instruction Following leaderboards, just ahead of Gemini 1.5 Pro in Spanish and GPT-4 Turbo in Instruction Following.

On the Math leaderboard, Claude 3 Opus holds a narrow lead over GPT-4 Turbo (second) and GPT-4o (third). Behind the news: As more models are trained on data scraped from the web, leakage of test data into training sets has made it more difficult to evaluate their performance on common benchmarks. Earlier this year, researchers at Shanghai Jiao Tong University evaluated 31 open-source large language models and found that several had a high probability of inaccurate benchmark results due to data leakage. Scale AI built the GSM1k math dataset partly to show that some high-profile language models show evidence of overfitting to the common math benchmark GSM8k. Why it matters: Traditionally, benchmarks have been open source efforts. But proprietary benchmarks are emerging to help developers evaluate their models and applications with greater confidence.

By keeping their datasets under wraps, companies like Scale AI and Vals AI ensure that models haven’t been exposed to test questions and answers previously, making evaluations more reliable. However, private benchmarks lack the transparency of their open counterparts. A mix of public, private, and internal evals may be necessary to get a well rounded picture of a given model’s capabilities.We’re thinking: We welcome Scale AI’s contribution to the important field of evals,... The Scale AI SEAL Leaderboards are a new initiative designed to rank large language models (LLMs) based on unbiased evaluations and expert assessments. Here’s a breakdown of their key features and methodologies: 1.

**Purpose**: The SEAL Leaderboards aim to provide a trustworthy and expert-driven ranking system for LLMs. They are intended to eliminate biases that may arise from traditional evaluation methods, ensuring that the results reflect the true performance of various models [1][3]. 2. **Evaluation Criteria**: The leaderboards assess models across different domains and tasks, including: - **Coding**: Evaluating how well models perform on coding-related tasks. - **Instruction Following**: Ranking models based on their ability to follow specific instructions accurately.

Scale Ai Launches Seal Leaderboards For Expert Driven Llm Evaluations

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