Scale Ai Introduces Llm Performance Rankings Datagrom

Bonisiwe Shabane

-Jan 2, 2026, 6:27 PM

scale ai introduces llm performance rankings datagrom

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. 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) Artificial intelligence training data provider Scale AI Inc., which serves the likes of OpenAI and Nvidia Corp., today published the results of its first-ever SEAL Leaderboards.

It’s a new ranking system for frontier large language models based on private, curated and unexploitable datasets that attempts to rate their capabilities in common use cases, such as generative AI coding, instruction following,... The SEAL Leaderboards show that OpenAI’s GPT family of LLMs ranks first in three of the four initial domains it’s using to rank AI models, with Anthropic PBC’s popular Claude 3 Opus grabbing first... Google LLC’s Gemini models also did well, ranking joint-first with the GPT models in a couple of the domains. Scale AI says it created the SEAL Leaderboards because of the lack of transparency around the performance of AI, in a world where there are now hundreds of LLMs available for companies to use. The rankings were developed by Scale AI’s Safety, Evaluations, and Alignment Lab and claim to maintain neutrality and integrity by refusing to divulge the nature of the prompts it uses to evaluate LLMs. The company notes that though there are other efforts to rank LLMs, such MLCommons’ benchmarks and Stanford HAI’s transparency index, its expertise in AI training data means it’s uniquely positioned to overcome some challenges...

These include problems around the lack of high-quality evaluation datasets that aren’t contaminated, inconsistent reporting of evaluations, the unverified expertise of evaluators and the lack of adequate tooling to properly understand evaluation results. For instance, Scale AI points out that MLCommon’s benchmarks are publicly available, so companies might train their models specifically to respond accurately to the prompts they use. 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. Reeling from its Meta partnership, Scale AI launches SEAL Showdown, a new AI leaderboard aimed at fixing flawed AI benchmarks with a diverse user base. Reeling from a disastrous partnership with Meta that sparked a client exodus and mass layoffs, data-labeling firm Scale AI is making a bold play to reclaim its authority in the AI industry.

The company today launched “SEAL Showdown,” a new public leaderboard designed to dethrone influential but criticized rivals like LMArena. Scale AI claims its new platform will fix the “benchmark wars” by using a diverse global user base and safeguards against manipulation. This strategic pivot aims to address growing concerns that current AI rankings are easily gamed and fail to reflect real-world performance, offering Scale a path to rebuild its reputation on a foundation of trust. A local-first browser extension that centralizes your prompts and chains across ChatGPT, Claude, Gemini, Grok, AI Studio, and Mistral – with a free 60-item plan.

Scale Ai Introduces Llm Performance Rankings Datagrom

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