{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-seal-llm-leaderboard","slug":"seal-llm-leaderboard","name":"SEAL LLM Leaderboard","type":"benchmark","url":"https://labs.scale.com/leaderboard","page_url":"https://unfragile.ai/seal-llm-leaderboard","categories":["testing-quality"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-seal-llm-leaderboard__cap_0","uri":"capability://data.processing.analysis.expert.curated.llm.model.benchmarking.with.dynamic.leaderboard.ranking","name":"expert-curated llm model benchmarking with dynamic leaderboard ranking","description":"Maintains a continuously updated leaderboard that ranks LLM models across multiple expert-designed benchmark tasks. The system ingests evaluation results from Scale's proprietary evaluation pipeline, applies standardized scoring methodologies across diverse task categories (reasoning, coding, instruction-following, safety), and dynamically re-ranks models as new evaluation data arrives. Rankings are computed using weighted aggregation of task-specific scores with transparent methodology documentation.","intents":["Compare performance of different LLM models across standardized benchmarks to inform model selection decisions","Track how model performance evolves over time as new versions are released","Identify which models excel at specific task categories (coding vs reasoning vs safety) to match use-case requirements","Validate that a newly released model meets expected performance thresholds before deployment"],"best_for":["ML engineers and product teams evaluating LLM options for production deployment","Researchers benchmarking model capabilities across standardized tasks","Enterprise teams making model procurement decisions based on comparative performance data","Open-source model developers tracking their model's competitive position"],"limitations":["Leaderboard reflects only tasks included in Scale's evaluation suite — may not cover domain-specific benchmarks relevant to niche applications","Evaluation methodology and weighting schemes are proprietary — limited transparency into how final rankings are computed","Benchmark results represent point-in-time snapshots; model performance can vary significantly based on prompt engineering, temperature settings, and system prompts not captured in leaderboard","No capability to run custom benchmarks or evaluate private/internal models against the same standardized tasks"],"requires":["Web browser access to Scale's leaderboard interface","No authentication required for viewing public leaderboard data","Internet connectivity to fetch real-time ranking updates"],"input_types":["model identifiers (e.g., 'gpt-4-turbo', 'claude-3-opus')","benchmark task categories as filters"],"output_types":["structured ranking data (model name, score, percentile, task-specific breakdowns)","comparative performance visualizations (charts, tables)","historical trend data showing model performance over time"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-seal-llm-leaderboard__cap_1","uri":"capability://search.retrieval.multi.dimensional.model.performance.filtering.and.comparison.interface","name":"multi-dimensional model performance filtering and comparison interface","description":"Provides an interactive filtering and sorting interface that allows users to slice leaderboard data across multiple dimensions: model provider (OpenAI, Anthropic, Meta, etc.), model size/type (base vs instruction-tuned), benchmark category (reasoning, coding, instruction-following), and performance metrics (absolute score, improvement over baseline, cost-efficiency). The interface supports side-by-side comparison of selected models with detailed breakdowns of task-specific performance.","intents":["Filter models by specific criteria (e.g., 'show only open-source models under 70B parameters') to narrow selection space","Compare 2-5 models side-by-side across all benchmark dimensions to identify performance trade-offs","Sort models by cost-per-token or inference latency to find optimal price-performance ratio","Drill down into task-specific performance to understand which models excel at reasoning vs coding vs instruction-following"],"best_for":["Product managers building model selection matrices for cost-performance optimization","ML engineers comparing models before integration into production systems","Researchers analyzing model capability distributions across task categories","Non-technical stakeholders exploring model options without deep ML expertise"],"limitations":["Filter options are limited to dimensions included in Scale's evaluation schema — cannot filter by custom attributes (e.g., 'models with vision capabilities', 'models trained after 2024')","Comparison interface shows only models present in the leaderboard; cannot import external evaluation results for comparison","Performance metrics are aggregated across all benchmark tasks — no ability to weight specific task categories more heavily in custom scoring","Historical comparison data may be limited for very recent model releases"],"requires":["Web browser with JavaScript enabled","No API key or authentication required for public leaderboard access"],"input_types":["filter selections (dropdown, checkbox, range inputs)","model identifiers for comparison","sort criteria (ascending/descending)"],"output_types":["filtered model list with rankings","side-by-side comparison tables","performance visualization charts (bar charts, radar plots)","detailed metric breakdowns per model"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-seal-llm-leaderboard__cap_2","uri":"capability://memory.knowledge.benchmark.task.transparency.and.methodology.documentation","name":"benchmark task transparency and methodology documentation","description":"Provides detailed documentation of each benchmark task included in the leaderboard, including task description, evaluation methodology, scoring rubric, example inputs/outputs, and the rationale for task inclusion. Documentation is accessible via the leaderboard interface and explains how models are evaluated on each task, what constitutes a correct answer, and how partial credit is awarded. This enables users to understand what capabilities each benchmark actually measures.","intents":["Understand what specific capability each benchmark task is designed to measure (e.g., 'multi-step reasoning', 'code generation with type safety')","Evaluate whether a benchmark task is relevant to your specific use case or domain","Identify potential biases or limitations in benchmark design that might favor certain model architectures","Reproduce benchmark evaluation locally by understanding the exact task specification and scoring criteria"],"best_for":["Researchers validating benchmark methodology and identifying potential gaming vectors","ML engineers determining whether benchmark results are predictive of real-world performance","Model developers understanding what capabilities they need to improve to rank higher","Enterprise teams assessing whether leaderboard rankings correlate with their internal use-case performance"],"limitations":["Documentation may not capture all nuances of human evaluation — some subjective judgment calls in scoring may not be fully documented","Benchmark tasks are fixed and cannot be customized — users cannot request evaluation on domain-specific variants","No access to raw evaluation data or individual model outputs on benchmark tasks — only aggregated scores are published","Documentation updates may lag behind leaderboard ranking updates if methodology changes"],"requires":["Web browser to access leaderboard documentation","No special tools or APIs required"],"input_types":["benchmark task identifier","model identifier (to view task-specific performance)"],"output_types":["task description and specification (text)","evaluation rubric and scoring criteria (structured text/JSON)","example inputs and expected outputs","methodology documentation (markdown/HTML)"],"categories":["memory-knowledge","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-seal-llm-leaderboard__cap_3","uri":"capability://data.processing.analysis.temporal.performance.tracking.and.model.evolution.analysis","name":"temporal performance tracking and model evolution analysis","description":"Tracks model performance over time as new model versions are released and re-evaluated, maintaining historical snapshots of leaderboard rankings and task-specific scores. The system enables visualization of performance trends, showing how a model's capabilities have improved (or degraded) across benchmark versions. Users can view performance trajectories for individual models or compare how different models' capabilities have evolved relative to each other.","intents":["Track whether a model's performance is improving or stagnating over successive releases","Identify which benchmark categories show the most improvement in new model versions","Compare the rate of capability improvement across competing models (e.g., 'GPT-4 vs Claude vs Llama improvements over 6 months')","Forecast future model capabilities based on historical improvement trends"],"best_for":["Model developers tracking their own model's competitive position over time","Researchers analyzing capability scaling trends across model families","Product teams planning model upgrade cycles based on performance improvement velocity","Investors/analysts assessing the competitive trajectory of different AI companies"],"limitations":["Historical data is only available for models that have been continuously evaluated — older models or models that were delisted may have incomplete history","Benchmark task definitions may change over time, making direct historical comparisons problematic","Performance improvements may reflect benchmark-specific optimization rather than genuine capability gains","Temporal resolution is limited to leaderboard update frequency — cannot track intra-day or intra-week performance variations"],"requires":["Web browser with JavaScript for interactive timeline visualizations","Internet connectivity to fetch historical ranking data"],"input_types":["model identifier(s)","date range for historical analysis","benchmark category filter (optional)"],"output_types":["time-series performance data (score vs date)","trend visualizations (line charts, area charts)","performance improvement metrics (absolute change, percentage change, improvement rate)","comparative trajectory analysis (multiple models on same chart)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-seal-llm-leaderboard__cap_4","uri":"capability://data.processing.analysis.cost.performance.efficiency.metrics.and.optimization.guidance","name":"cost-performance efficiency metrics and optimization guidance","description":"Computes and displays cost-efficiency metrics that correlate model performance with inference costs (cost-per-token, cost-per-inference, cost-per-task-completion). The system enables filtering and sorting by efficiency metrics, helping users identify models that deliver strong performance within budget constraints. Guidance includes recommendations for cost-optimal model selection based on specific performance thresholds and budget parameters.","intents":["Find the cheapest model that meets a specific performance threshold (e.g., 'models scoring >80 on reasoning with lowest cost-per-token')","Compare cost-performance trade-offs across model families to optimize inference budget","Estimate total cost of ownership for deploying a specific model at scale","Identify cost-performance sweet spots where small performance sacrifices yield significant cost savings"],"best_for":["Startups and small teams optimizing inference costs under tight budgets","Enterprise teams managing large-scale inference workloads with cost constraints","Product managers making model selection decisions with cost-performance trade-offs","ML engineers building cost-aware model routing systems"],"limitations":["Cost data reflects published pricing and may not account for volume discounts, custom pricing agreements, or self-hosted deployment costs","Cost-efficiency metrics assume uniform task distribution — real-world workloads may have different cost-performance profiles for specific task types","Does not account for latency or throughput constraints — a cheaper model may be too slow for real-time applications","Inference cost estimates do not include fine-tuning costs or context window costs for long-context applications"],"requires":["Web browser to access leaderboard interface","Knowledge of your performance requirements and budget constraints"],"input_types":["performance threshold (minimum acceptable score)","budget constraint (maximum cost-per-token or cost-per-inference)","benchmark category filter"],"output_types":["cost-efficiency rankings (models sorted by cost-per-performance-point)","cost-performance scatter plots (performance vs cost visualization)","efficiency recommendations (model suggestions with cost-performance justification)","budget impact analysis (estimated costs for different model choices)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":21,"verified":false,"data_access_risk":"low","permissions":["Web browser access to Scale's leaderboard interface","No authentication required for viewing public leaderboard data","Internet connectivity to fetch real-time ranking updates","Web browser with JavaScript enabled","No API key or authentication required for public leaderboard access","Web browser to access leaderboard documentation","No special tools or APIs required","Web browser with JavaScript for interactive timeline visualizations","Internet connectivity to fetch historical ranking data","Web browser to access leaderboard interface"],"failure_modes":["Leaderboard reflects only tasks included in Scale's evaluation suite — may not cover domain-specific benchmarks relevant to niche applications","Evaluation methodology and weighting schemes are proprietary — limited transparency into how final rankings are computed","Benchmark results represent point-in-time snapshots; 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