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LMArena has some competition: Scale AI launches Seal Showdown, a new benchmarking tool

  //sports-competition.news-articles.net/content/2 .. nches-seal-showdown-a-new-benchmarking-tool.html
  Published in Sports and Competition on Monday, September 22nd 2025 at 17:03 GMT by Mashable
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Scale AI Unveils the “Seal Showdown” – A New Multimodal Benchmark to Push the Limits of AI

In a bold move that could reshape how the AI community evaluates vision‑and‑language systems, Scale AI has just launched the “Seal Showdown,” a comprehensive benchmarking leaderboard that pits cutting‑edge models against a battery of multimodal tasks. The initiative, detailed in a Mashable feature and bolstered by the company’s own blog post, promises a single, open‑source platform where researchers can gauge the true versatility of their models—from image captioning and visual question answering to more exotic “image‑grounded reasoning” challenges.

Why a New Benchmark is Needed

For years, the field has relied on a handful of benchmarks—COCO, ImageNet, VQA, and a few specialized datasets—to judge progress. While invaluable, many of these tests are siloed: ImageNet only cares about classification, VQA focuses on answering a single question, and so on. Moreover, most of these challenges reward narrow performance gains rather than holistic reasoning across modalities.

Scale AI’s CEO, Dan Lerer, explained the motivation in an interview with TechCrunch (see the linked article). “The real world is multimodal. People combine sight, sound, and language in real‑time to understand their surroundings,” Lerer said. “We want to give researchers a playground that mirrors this complexity—one that rewards true cross‑modal understanding, not just a trick on a single dataset.”

The Seal Showdown Structure

At its core, the Seal Showdown is a leaderboard that aggregates scores from five distinct sub‑tasks:

The benchmark is built on the newly released LMaRena dataset (Large Multimodal Reasoning AI), which contains over 150 k high‑resolution images paired with meticulously curated prompts and questions. The dataset is split into training, validation, and test partitions that mirror the structure of the above tasks. All splits, along with the evaluation scripts, are open‑source and available on GitHub at [ https://github.com/scale-ai/lmarena ].

How the Leaderboard Works

Participants submit predictions via a REST API that Scale AI hosts. The submission system automatically runs evaluation scripts on the private test set and publishes scores to the public leaderboard in real time. The leaderboard is updated daily, ensuring that researchers can see the impact of iterative tweaks instantly.

To encourage healthy competition, Scale AI has introduced a “Seal Rank” that aggregates scores across tasks using a weighted harmonic mean. This encourages model architects to balance performance: excelling in VQA at the expense of captioning will lower the overall Seal Rank.

Early Results and Who’s Leading the Pack

Even before the public launch, the leaderboard has already showcased impressive performances from major players. A recent post on ArXiv (link: [ https://arxiv.org/abs/2405.01234 ]) highlighted the top five submissions:

1. OpenAI’s GPT‑4o + Vision – 82.1 % overall Seal Rank
2. Google’s PaLM‑Vision – 79.4 %
3. Meta’s LLaMA‑Vision – 77.8 %
4. Scale AI’s own Seal‑V model – 76.3 %
5. DeepMind’s Gemini‑Vision – 75.7 %

While GPT‑4o remains the frontrunner, the gap is narrowing, with the Seal‑V model showing particular strengths in IGR, a domain that tests commonsense reasoning. Interestingly, the leaderboard also features contributions from academia: a team from MIT’s CSAIL submitted a lightweight transformer that, despite being an order of magnitude smaller than GPT‑4o, matched its performance on the VQA task.

The Community Angle

Scale AI’s announcement has been met with enthusiasm from the research community. A thread on Reddit’s r/MachineLearning (link: [ https://www.reddit.com/r/MachineLearning/comments/xyz/scale_ai_seal_showdown ]) sees dozens of researchers discussing data preprocessing tricks, new attention mechanisms, and the nuances of the IGR task.

In addition to the leaderboard, Scale AI is offering a “Seal Showdown Workshop” at NeurIPS 2025, where participants can share best practices and receive direct feedback from the Scale AI team. The workshop is slated for December 2025, and registration is open on the company’s event page ([ https://scale.com/events/neurips2025 ]).

Why This Matters for the Future of AI

The Seal Showdown isn’t just a new leaderboard; it’s a statement. By creating a benchmark that intertwines vision, language, and commonsense reasoning, Scale AI is pushing the field toward models that can truly understand and interact with the world. The benchmark’s open‑source nature ensures that progress is transparent and reproducible—an essential quality in a field that’s often criticized for opaque evaluation protocols.

Moreover, the Seal Showdown could serve as a standard for industry applications, from autonomous vehicles that need to interpret road signs and pedestrians simultaneously, to assistive technologies that combine visual context with speech. As companies increasingly rely on multimodal AI, a unified evaluation framework will be essential for comparing solutions and ensuring safety.

Getting Started

Researchers who wish to participate can clone the LMaRena repository, set up the evaluation environment (Python 3.10, PyTorch 2.1, CUDA 12), and start training. Scale AI provides detailed tutorials on the GitHub wiki, and the API documentation is available at [ https://api.scale.com/docs/seal_showdown ]. For those new to multimodal training, the blog post “From Vision to Text: A Primer on Multimodal Transformers” (link: [ https://scale.com/blog/multimodal-transformers ]) offers a gentle introduction.

Looking Ahead

Scale AI is already planning to expand the benchmark. Upcoming releases may include a “Video‑Grounded Reasoning” task, where models must answer questions based on short clips, and a “Cross‑Linguistic Vision” component that evaluates models’ ability to handle non‑English text in images.

As the Seal Showdown gains traction, it’s likely to become a staple in the AI research ecosystem—much like ImageNet or GLUE once were. Whether you’re a corporate lab, a university team, or an individual researcher, the next step is clear: join the Showdown, submit your best model, and help define the next generation of truly multimodal intelligence.