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SEAL Leaderboard

Private, expert-graded leaderboards from Scale AI that rank frontier LLMs on domains contaminated public benchmarks can no longer measure.

Free· Free to view; paid custom evals via Scale enterprise salesEvaluationMulti-model (GPT, Claude, Gemini, Llama, etc.)
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Best for

Pick SEAL Leaderboard if you want a contamination-resistant, expert-graded view of how frontier LLMs compare on specific capabilities like coding, math, or multilingual reasoning.

Skip if

Skip it if you need fully open, reproducible benchmarks you can rerun locally, or if you care about small, fine-tuned, or domain-specialist models that Scale doesn't include.

SEAL (Safety, Evaluations and Alignment Lab) Leaderboards are Scale AI's public ranking of frontier large language models across narrow, high-stakes domains such as coding, advanced math, multilingual reasoning, instruction following, tool use, agentic tasks, and adversarial robustness. Unlike Chatbot Arena's crowd vote or static academic benchmarks, SEAL evaluations use private, unpublished prompt sets graded by domain experts Scale recruits through its data-labeling network, which is intended to keep the test data out of model training corpora and reduce contamination.

It is a free, browsable resource aimed at engineers, researchers, and buyers trying to pick a model for a specific task rather than chase a single composite score. Each leaderboard page exposes the methodology, the rubric, sample (redacted) prompts, and per-task breakdowns for major closed and open models (GPT, Claude, Gemini, Llama, Mistral, Qwen, DeepSeek, and others). Scale rotates evaluation sets periodically to stay ahead of training-data leakage.

SEAL is not a model or an API; it is essentially a published evaluation report card that doubles as marketing for Scale's paid enterprise evaluation services. Treat the rankings as one signal among several, since the underlying prompts are not auditable by third parties and Scale is also a commercial partner of several of the labs whose models it scores.

Editor's take

SEAL is one of the more credible public leaderboards because Scale actually pays experts to grade and keeps prompts private, which mitigates the contamination rot eating most academic benchmarks. The conflict-of-interest is real though, so use it alongside LiveBench, Artificial Analysis, and your own task-specific evals rather than as the single source of truth.

— The AI Tool Bible editorial team

Pros

  • Private, unpublished prompt sets reduce benchmark contamination
  • Expert human grading rather than crowd voting or LLM-as-judge
  • Per-domain breakdowns (coding, math, multilingual, agentic, adversarial)
  • Covers both major closed and open frontier models
  • Free public access with transparent methodology pages

Cons

  • ⚠️ Prompts are not third-party auditable
  • ⚠️ Scale has commercial relationships with several ranked labs
  • ⚠️ Refresh cadence per domain can lag the model release cycle
  • ⚠️ Limited coverage of small or fine-tuned models

Use cases

model-selectionbenchmark-trackingcontamination-resistant-evalcapability-comparison

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