{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"wildbench","slug":"wildbench","name":"WildBench","type":"benchmark","url":"https://huggingface.co/spaces/allenai/WildBench","page_url":"https://unfragile.ai/wildbench","categories":["testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"wildbench__cap_0","uri":"capability://safety.moderation.gpt.4.based.llm.output.evaluation.with.multi.dimensional.scoring","name":"gpt-4-based llm output evaluation with multi-dimensional scoring","description":"Evaluates LLM responses against real-world user queries using GPT-4 as an automated judge, scoring outputs across three independent dimensions: helpfulness (task completion quality), safety (absence of harmful content), and instruction-following (adherence to user intent). The evaluation framework sends both the original query and model response to GPT-4 with structured prompts designed to elicit numerical scores (typically 1-10 scale) for each dimension, enabling comparative ranking of different LLMs on identical tasks.","intents":["Compare performance of multiple LLMs on the same challenging real-world queries to identify which models handle complex user requests best","Identify which LLMs are safest and most compliant with user instructions across diverse task types","Establish baseline performance metrics for LLMs before and after fine-tuning or instruction-following optimization","Validate that new model versions maintain or improve safety and instruction-following while increasing helpfulness"],"best_for":["AI research teams benchmarking proprietary or open-source LLMs against industry standards","Model developers evaluating instruction-tuning effectiveness across safety, helpfulness, and compliance dimensions","Organizations selecting between multiple LLM providers based on real-world task performance"],"limitations":["GPT-4 judge introduces cost (~$0.03-0.06 per evaluation depending on response length) and latency (5-30 seconds per query-response pair)","Judge bias: GPT-4 may have inherent preferences for certain response styles or reasoning patterns, potentially favoring models trained on similar data","No human-in-the-loop validation — scores reflect GPT-4's judgment only, not actual user satisfaction or real-world outcomes","Evaluation quality depends entirely on prompt engineering for the judge; poorly designed evaluation prompts produce unreliable scores"],"requires":["OpenAI API key with GPT-4 access and sufficient quota","LLM outputs to evaluate (can be generated via API or provided as text)","Query dataset (WildBench provides 1,024 pre-collected real-world queries, or users can supply custom queries)"],"input_types":["text (user query)","text (LLM response to evaluate)","structured metadata (model name, timestamp, optional context)"],"output_types":["structured data (JSON with helpfulness score, safety score, instruction-following score, optional judge reasoning)","aggregated metrics (mean/median scores per model, percentile rankings)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"wildbench__cap_1","uri":"capability://data.processing.analysis.real.world.query.dataset.with.chatbot.sourced.complexity","name":"real-world query dataset with chatbot-sourced complexity","description":"Provides a curated dataset of 1,024 complex user queries collected directly from chatbot platforms and user interactions, representing genuine real-world use cases rather than synthetic or academic tasks. Queries span diverse domains (writing, coding, analysis, creative tasks, etc.) and difficulty levels, enabling evaluation of LLMs on authentic user intents that expose model limitations in instruction-following, reasoning, and safety.","intents":["Evaluate LLMs on realistic user queries that reflect actual deployment scenarios, not artificial benchmarks","Identify failure modes and edge cases where LLMs struggle with real-world complexity and ambiguity","Compare model performance on diverse task types (coding, writing, analysis, creative) to understand domain-specific strengths","Validate that benchmark results correlate with actual user satisfaction in production chatbot deployments"],"best_for":["Researchers studying LLM behavior on authentic user queries vs. synthetic benchmarks","Model developers optimizing for real-world performance rather than academic metrics","Teams evaluating LLMs for production chatbot deployment who need realistic performance estimates"],"limitations":["1,024 queries is relatively small compared to web-scale datasets; may not cover all domain-specific edge cases","Query distribution reflects chatbot platform user base (likely skewed toward English, tech-savvy users) and may not represent all user demographics","No query metadata (domain tags, difficulty labels, expected answer length) provided in base dataset, requiring manual annotation for stratified analysis","Queries are static snapshots; do not evolve with emerging user needs or new task types over time"],"requires":["Access to WildBench Hugging Face Space or downloadable dataset","Text processing capability to parse and filter queries","Optional: LLM API access to generate responses for evaluation"],"input_types":["text (user queries in natural language)"],"output_types":["structured data (query text, optional metadata like source platform or domain)","text (raw queries for manual inspection)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"wildbench__cap_2","uri":"capability://data.processing.analysis.comparative.llm.ranking.and.leaderboard.generation","name":"comparative llm ranking and leaderboard generation","description":"Aggregates evaluation scores across the 1,024 query dataset to produce ranked leaderboards comparing multiple LLMs on helpfulness, safety, and instruction-following metrics. The ranking system computes mean/median scores per model, applies optional statistical significance testing, and generates visualizations (tables, charts) showing relative performance. Leaderboard updates as new model evaluations are submitted, enabling continuous benchmarking of emerging models.","intents":["Quickly identify which LLMs perform best on real-world tasks without running custom evaluations","Track performance improvements over time as new model versions are released","Benchmark proprietary or fine-tuned models against public leaderboard baselines","Communicate model performance to stakeholders via visual leaderboards and comparative metrics"],"best_for":["Model developers comparing their LLM against public baselines and competitors","Organizations selecting between multiple LLM providers based on published benchmarks","Researchers tracking LLM capability trends across model families and scales"],"limitations":["Leaderboard rankings reflect GPT-4 judge preferences, which may not align with human preferences or specific use-case requirements","No confidence intervals or statistical significance testing visible in public leaderboard; users cannot assess whether score differences are meaningful","Leaderboard does not account for model size, latency, cost, or other practical deployment factors — only raw performance metrics","Evaluation is one-shot (single response per query); does not measure consistency, robustness to prompt variations, or multi-turn dialogue quality"],"requires":["Completed evaluations of LLMs on the WildBench query dataset","Aggregation infrastructure to compute statistics and update leaderboard","Visualization library (e.g., Plotly, Matplotlib) to render leaderboard tables and charts"],"input_types":["structured data (evaluation scores for each model-query pair)"],"output_types":["structured data (ranked model list with mean/median scores, confidence intervals)","visual (leaderboard table, score distribution charts, comparative bar charts)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"wildbench__cap_3","uri":"capability://tool.use.integration.multi.provider.llm.evaluation.orchestration","name":"multi-provider llm evaluation orchestration","description":"Supports evaluation of LLM outputs from multiple sources and providers (OpenAI, Anthropic, open-source models via Hugging Face, local models, etc.) within a unified evaluation framework. The system accepts model responses in standardized formats (text, JSON, or API responses) and routes them through the same GPT-4 judge pipeline, enabling fair comparison across different model families, sizes, and deployment modalities without requiring custom integration code.","intents":["Compare proprietary LLMs (GPT-4, Claude, Gemini) against open-source models (Llama, Mistral) on identical queries using the same judge","Evaluate fine-tuned or custom LLMs against public baselines without building custom evaluation infrastructure","Benchmark local/on-premise LLMs alongside cloud-hosted models in a unified framework","Validate that model outputs from different providers are comparable despite format or API differences"],"best_for":["Teams evaluating both proprietary and open-source LLMs and need a unified comparison framework","Organizations with custom/fine-tuned models who want to benchmark against public leaderboards","Researchers comparing models across different providers and deployment modalities"],"limitations":["Evaluation cost scales linearly with number of models evaluated (each model × 1,024 queries × ~$0.03-0.06 per evaluation)","No built-in handling of model-specific output formats (e.g., structured JSON vs. free-form text); requires preprocessing to normalize responses","Evaluation latency increases with number of concurrent model evaluations; sequential evaluation can take hours for many models","No automatic handling of model failures or timeouts; requires manual retry logic or human intervention"],"requires":["Model outputs in text format (can be pre-generated or generated on-demand via API)","OpenAI API key for GPT-4 judge","Optional: API keys for models being evaluated (if generating responses on-demand)"],"input_types":["text (model responses from any provider)","structured data (model metadata: name, provider, version, parameters)"],"output_types":["structured data (evaluation scores with model metadata, enabling cross-provider comparison)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"wildbench__cap_4","uri":"capability://safety.moderation.safety.and.instruction.following.compliance.scoring","name":"safety and instruction-following compliance scoring","description":"Evaluates LLM responses for safety (absence of harmful, illegal, unethical, or biased content) and instruction-following (adherence to user intent, constraints, and format requirements) as independent scoring dimensions. The GPT-4 judge uses specialized prompts to assess whether responses violate safety guidelines, refuse harmful requests appropriately, and follow explicit user instructions (e.g., 'respond in JSON format', 'do not mention X'). Scores are aggregated per model to identify safety/compliance strengths and weaknesses.","intents":["Identify which LLMs are safest and most compliant with user instructions for production deployment","Detect models that refuse harmful requests appropriately vs. those that comply with jailbreak attempts","Measure instruction-following quality (e.g., format compliance, constraint adherence) across models","Validate that safety fine-tuning or instruction-tuning improves compliance without degrading helpfulness"],"best_for":["Organizations deploying LLMs in regulated industries (finance, healthcare, legal) where compliance is critical","Teams evaluating LLMs for customer-facing applications where safety and instruction-following are non-negotiable","Model developers optimizing instruction-tuning and safety fine-tuning effectiveness"],"limitations":["Safety scoring reflects GPT-4's judgment of what is 'safe' — may not align with domain-specific safety requirements (e.g., financial regulations, medical ethics)","No evaluation of adversarial robustness or jailbreak resistance; scores reflect single-pass responses, not multi-turn attack scenarios","Instruction-following scoring is coarse-grained (binary or 1-10 scale); does not measure partial compliance or degree of deviation","No evaluation of hallucination, factual accuracy, or reasoning quality — only safety and instruction-following"],"requires":["LLM responses to evaluate","Original user queries and any explicit instructions/constraints","OpenAI API key for GPT-4 judge"],"input_types":["text (user query with explicit instructions/constraints)","text (LLM response to evaluate)"],"output_types":["structured data (safety score, instruction-following score, optional judge reasoning/explanation)"],"categories":["safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"wildbench__cap_5","uri":"capability://automation.workflow.batch.evaluation.with.result.caching.and.cost.optimization","name":"batch evaluation with result caching and cost optimization","description":"Supports batch evaluation of multiple LLMs on the 1,024-query dataset with intelligent caching to avoid redundant GPT-4 judge calls. If the same query-response pair has been evaluated before, the cached score is reused rather than re-querying GPT-4, reducing API costs and latency. Batch jobs can be submitted asynchronously and tracked via job IDs, enabling evaluation of many models without blocking the user interface.","intents":["Evaluate multiple LLM versions or fine-tuned variants on the same dataset without incurring redundant evaluation costs","Quickly add new models to the leaderboard by reusing cached evaluations for common queries","Run large-scale evaluation campaigns (e.g., evaluating 50+ models) without hitting API rate limits or incurring prohibitive costs","Track evaluation progress and retrieve results asynchronously without waiting for real-time completion"],"best_for":["Teams evaluating many model variants (different sizes, training runs, fine-tuning experiments) on the same dataset","Organizations with budget constraints who need to minimize GPT-4 API costs while evaluating multiple models","Researchers running large-scale benchmarking campaigns with many models and need asynchronous job tracking"],"limitations":["Caching assumes identical query-response pairs produce identical scores; does not account for GPT-4 non-determinism or temporal drift in judge behavior","Cache invalidation is manual; if evaluation criteria or judge prompts change, cached scores become stale and must be manually cleared","Batch job infrastructure adds complexity; requires database or persistent storage to track job status and results","No built-in cost estimation or budget alerts; users may exceed API spending limits without warning"],"requires":["Batch evaluation infrastructure (job queue, result storage, async task runner)","Persistent cache storage (database or key-value store) for evaluation results","OpenAI API key with sufficient quota for batch evaluations"],"input_types":["structured data (batch job specification: list of models, queries, responses)"],"output_types":["structured data (job ID, status, progress percentage)","structured data (evaluation results with cached/fresh indicator)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"wildbench__cap_6","uri":"capability://data.processing.analysis.judge.reasoning.and.explanation.extraction","name":"judge reasoning and explanation extraction","description":"Optionally extracts detailed reasoning and explanations from the GPT-4 judge for each evaluation, providing transparency into why a response received a particular score. The judge can be prompted to explain its scoring rationale (e.g., 'This response is helpful because it addresses all three parts of the user's question, but loses points for being overly verbose'). Explanations are stored alongside scores and can be displayed in the leaderboard or exported for analysis.","intents":["Understand why a model received a particular score and identify specific areas for improvement","Debug model failures by reading judge explanations for low-scoring responses","Validate that judge scoring is reasonable and aligned with human expectations by reviewing explanations","Identify systematic judge biases or errors by analyzing patterns in explanations across many evaluations"],"best_for":["Model developers iterating on models and needing detailed feedback on why responses are weak","Researchers analyzing judge behavior and validating that GPT-4 scoring aligns with human judgment","Teams building trust in automated evaluation by reviewing judge reasoning for a sample of evaluations"],"limitations":["Extracting explanations increases GPT-4 API cost and latency by ~30-50% (longer prompts and responses)","Judge explanations are subjective and may not align with human judgment; require manual validation","Explanations are not structured (free-form text); difficult to aggregate or analyze programmatically without NLP","No guarantee that explanations are accurate or complete; judge may omit important reasoning or provide post-hoc rationalizations"],"requires":["Modified GPT-4 judge prompts that request explanations","Additional storage for explanation text (can be large if evaluating 1,024 queries × many models)","Optional: NLP pipeline to extract structured insights from explanations"],"input_types":["text (user query, LLM response)"],"output_types":["structured data (score + free-form explanation text)","text (explanation for display in leaderboard or export)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"wildbench__cap_7","uri":"capability://planning.reasoning.custom.evaluation.prompt.configuration","name":"custom evaluation prompt configuration","description":"Allows users to customize the GPT-4 judge prompts to align with domain-specific evaluation criteria or organizational preferences. Users can modify scoring rubrics, add custom evaluation dimensions (e.g., 'creativity', 'conciseness'), adjust the scoring scale, or provide domain-specific context to the judge. Custom prompts are applied consistently across all model evaluations, enabling evaluation tailored to specific use cases.","intents":["Evaluate LLMs on domain-specific criteria (e.g., medical accuracy, legal compliance, creative quality) not covered by default helpfulness/safety/instruction-following dimensions","Align evaluation with organizational values or regulatory requirements (e.g., 'responses must be concise', 'must avoid jargon')","Experiment with different evaluation rubrics to understand how judge criteria affect model rankings","Validate that evaluation results are robust to prompt variations and not artifacts of specific judge phrasing"],"best_for":["Organizations with domain-specific evaluation requirements (finance, healthcare, legal, creative industries)","Teams experimenting with evaluation methodologies and need to test different rubrics","Researchers studying how evaluation criteria affect model rankings and judge behavior"],"limitations":["Custom prompts require careful engineering; poorly designed prompts produce unreliable or biased scores","No validation that custom prompts are unambiguous or aligned with human judgment; requires manual testing","Changing evaluation prompts makes results incomparable with previous evaluations; requires re-evaluation of all models","No built-in prompt versioning or A/B testing; difficult to track which prompt version produced which scores"],"requires":["Understanding of prompt engineering and evaluation design","Access to prompt configuration interface (API or UI)","Optional: domain expertise to design meaningful evaluation criteria"],"input_types":["text (custom evaluation prompt template)"],"output_types":["structured data (evaluation scores using custom criteria)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"wildbench__cap_8","uri":"capability://data.processing.analysis.temporal.performance.tracking.and.trend.analysis","name":"temporal performance tracking and trend analysis","description":"Tracks model performance over time as new versions are released and re-evaluated on the WildBench dataset. The system maintains historical evaluation records, enabling visualization of performance trends (e.g., 'GPT-4 helpfulness score improved from 7.2 to 7.8 between versions'), detection of performance regressions, and analysis of how model families evolve. Trend data can be exported for research or reporting.","intents":["Monitor whether new model versions improve performance on real-world tasks or introduce regressions","Track capability improvements across model families (e.g., Llama 1 → 2 → 3) to understand scaling trends","Identify which models are improving fastest and which are stagnating","Communicate model progress to stakeholders via performance trend visualizations"],"best_for":["Model developers tracking performance improvements across training runs and versions","Researchers analyzing capability scaling trends across model families","Organizations monitoring whether their deployed models are improving or degrading over time"],"limitations":["Temporal trends are confounded by evaluation date (GPT-4 behavior may drift over time), making it unclear whether score changes reflect model improvements or judge drift","No statistical significance testing for trend detection; small score changes may be noise rather than real improvements","Historical data is sparse if models are not re-evaluated frequently; trends may be incomplete or misleading","No causal analysis; cannot determine whether performance improvements are due to model changes, training data, or other factors"],"requires":["Historical evaluation records with timestamps","Persistent storage for trend data (database or time-series database)","Visualization library for trend charts"],"input_types":["structured data (evaluation scores with model version and timestamp)"],"output_types":["structured data (trend data: score over time per model)","visual (line charts showing performance trends, regression alerts)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"wildbench__headline","uri":"capability://testing.quality.benchmark.for.evaluating.llms.on.real.world.user.queries","name":"benchmark for evaluating llms on real-world user queries","description":"WildBench is a benchmark designed to evaluate large language models on complex, real-world user queries, assessing their helpfulness, safety, and instruction-following capabilities using GPT-4 as a judge.","intents":["best LLM benchmark","benchmark for evaluating AI chatbots","how to test LLMs on real-world tasks","top tools for LLM evaluation","LLM performance testing framework"],"best_for":["evaluating AI models","assessing chatbot performance"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["testing-quality"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":61,"verified":false,"data_access_risk":"high","permissions":["OpenAI API key with GPT-4 access and sufficient quota","LLM outputs to evaluate (can be generated via API or provided as text)","Query dataset (WildBench provides 1,024 pre-collected real-world queries, or users can supply custom queries)","Access to WildBench Hugging Face Space or downloadable dataset","Text processing capability to parse and filter queries","Optional: LLM API access to generate responses for evaluation","Completed evaluations of LLMs on the WildBench query dataset","Aggregation infrastructure to compute statistics and update leaderboard","Visualization library (e.g., Plotly, Matplotlib) to render leaderboard tables and charts","Model outputs in text format (can be pre-generated or generated on-demand via API)"],"failure_modes":["GPT-4 judge introduces cost (~$0.03-0.06 per evaluation depending on response length) and latency (5-30 seconds per query-response pair)","Judge bias: GPT-4 may have inherent preferences for certain response styles or reasoning patterns, potentially favoring models trained on similar data","No human-in-the-loop validation — scores reflect GPT-4's judgment only, not actual user satisfaction or real-world outcomes","Evaluation quality depends entirely on prompt engineering for the judge; poorly designed evaluation prompts produce unreliable scores","1,024 queries is relatively small compared to web-scale datasets; may not cover all domain-specific edge cases","Query distribution reflects chatbot platform user base (likely skewed toward English, tech-savvy users) and may not represent all user demographics","No query metadata (domain tags, difficulty labels, expected answer length) provided in base dataset, requiring manual annotation for stratified analysis","Queries are static snapshots; do not evolve with emerging user needs or new task types over time","Leaderboard rankings reflect GPT-4 judge preferences, which may not align with human preferences or specific use-case requirements","No confidence intervals or statistical significance testing visible in public leaderboard; users cannot assess whether score differences are meaningful","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.8500000000000001,"ecosystem":0.3,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.35,"ecosystem":0.15,"match_graph":0.2,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:34.803Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=wildbench","compare_url":"https://unfragile.ai/compare?artifact=wildbench"}},"signature":"GcMYHGTg3m1xHsPngF3B1EtI2AbTBVe3iXXMku4RDv9c4+0T22P5G/7+RanJxEpTUZe10num81BJKi0aq04PCw==","signedAt":"2026-06-21T14:47:30.188Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/wildbench","artifact":"https://unfragile.ai/wildbench","verify":"https://unfragile.ai/api/v1/verify?slug=wildbench","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}