LiveBench
BenchmarkFreeContinuously updated contamination-free LLM benchmark.
Capabilities8 decomposed
contamination-free benchmark dataset curation with continuous updates
Medium confidenceAutomatically ingests questions from recent information sources (news, research papers, current events) with temporal filtering to ensure test data was not published before model training cutoffs, preventing data leakage. Uses publication date verification and source freshness validation to guarantee benchmark questions are genuinely novel and not present in training corpora.
Implements continuous dataset refresh with publication-date-based contamination detection rather than static benchmarks, using temporal filtering to ensure questions post-date model training cutoffs and are sourced from verifiable recent publications
Prevents the data leakage problem that affects MMLU, HumanEval, and other static benchmarks where models may have seen test data during training, providing genuinely fresh evaluation signals
multi-domain llm capability evaluation across math, coding, reasoning, language, and data analysis
Medium confidenceOrchestrates evaluation across five distinct capability domains using domain-specific question formats and scoring rubrics. Each domain uses tailored evaluation logic: math uses numerical accuracy checking, coding uses execution-based validation, reasoning uses logical consistency scoring, language uses semantic similarity metrics, and data analysis uses output format and correctness validation.
Implements domain-specific evaluation pipelines with tailored scoring logic per capability area (execution-based for code, numerical for math, semantic for language) rather than uniform multiple-choice or token-matching evaluation
Provides richer capability profiling than single-domain benchmarks (like HumanEval for code-only) by simultaneously measuring five distinct dimensions with appropriate evaluation methods for each
real-time benchmark result aggregation and leaderboard generation
Medium confidenceCollects model evaluation results from submitted runs, aggregates scores across questions and domains, and generates live leaderboards ranked by overall and domain-specific performance. Uses incremental aggregation to update rankings as new model submissions arrive without requiring full recomputation.
Implements live leaderboard updates with incremental aggregation logic that avoids full recomputation on each new submission, enabling real-time ranking visibility as models are continuously evaluated
Provides dynamic leaderboards that reflect current model capabilities as new benchmark questions are added, unlike static leaderboards that become stale as models and benchmarks evolve
automated question generation and sourcing from recent information feeds
Medium confidenceContinuously monitors and ingests questions from recent publications, news sources, research papers, and other current information feeds using automated extraction pipelines. Filters ingested content by publication date, relevance to benchmark domains, and question quality metrics before adding to the active benchmark pool.
Implements automated question extraction from diverse information feeds with temporal filtering and domain classification, enabling continuous benchmark expansion without manual authoring bottlenecks
Scales benchmark maintenance beyond static question sets by automatically sourcing fresh questions from current information, preventing the staleness problem that affects manually-curated benchmarks
model response submission and evaluation pipeline with standardized formats
Medium confidenceAccepts model responses submitted via API or web interface in standardized formats, validates response structure and content, routes responses to domain-specific evaluators, and records results with metadata (submission timestamp, model version, evaluator version). Supports batch submission for efficient evaluation of multiple models.
Implements standardized submission pipeline with domain-specific routing and batch processing support, enabling seamless integration into model evaluation workflows without custom evaluation code per domain
Provides unified submission interface across all five capability domains, eliminating the need to implement separate evaluation logic for math, coding, reasoning, language, and data analysis
domain-specific evaluation logic with execution-based and semantic validation
Medium confidenceImplements specialized evaluators for each capability domain: code evaluator executes submissions in sandboxed environments and checks output correctness, math evaluator performs numerical comparison with tolerance handling, reasoning evaluator validates logical consistency, language evaluator uses semantic similarity metrics, and data analysis evaluator checks output format and data accuracy. Each evaluator is independently versioned and can be updated without affecting others.
Implements independent, versioned evaluators per domain with execution-based validation for code (sandboxed execution) and semantic metrics for language, rather than uniform token-matching or regex-based evaluation
Provides more accurate capability assessment than generic benchmarks using execution-based code evaluation and semantic similarity for language, catching correctness nuances that simple string matching misses
temporal metadata tracking and contamination risk reporting
Medium confidenceRecords publication dates, source URLs, and model training cutoff dates for all benchmark questions and submissions. Generates contamination risk reports by comparing question publication dates against model training cutoffs, flagging potential data leakage when questions were published before training data collection ended. Provides transparency into which results are reliable based on temporal alignment.
Implements comprehensive temporal metadata tracking with automated contamination risk reporting that flags model-question pairs where publication dates precede training cutoffs, providing transparent data leakage assessment
Provides explicit contamination risk visibility that static benchmarks lack, enabling researchers to filter results by contamination status and make evidence-based decisions about model comparisons
open-source benchmark infrastructure and reproducibility support
Medium confidencePublishes benchmark questions, evaluation code, and leaderboard data as open-source artifacts, enabling external researchers to reproduce results, audit evaluation logic, and extend the benchmark. Provides version control for questions and evaluators, allowing tracking of changes and reproducibility across benchmark versions.
Releases benchmark questions, evaluation code, and infrastructure as open-source with version control, enabling external audit and reproduction rather than treating benchmark as a black box
Provides full transparency and reproducibility that proprietary benchmarks lack, allowing researchers to verify evaluation fairness and extend the benchmark for custom use cases
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
Artifacts that share capabilities with LiveBench, ranked by overlap. Discovered automatically through the match graph.
open_llm_leaderboard
open_llm_leaderboard — AI demo on HuggingFace
Open LLM Leaderboard
Hugging Face open-source LLM leaderboard — standardized benchmarks, automatic evaluation.
MATH Benchmark
12.5K competition math problems — AMC/AIME/Olympiad level, 7 subjects, standard math benchmark.
TrustLLM
8-dimension trustworthiness benchmark for LLMs.
Humanity's Last Exam
Hardest exam questions from thousands of experts.
MT-Bench
Multi-turn conversation benchmark — 80 questions, 8 categories, GPT-4 as judge.
Best For
- ✓LLM researchers validating model generalization on current information
- ✓Organizations comparing multiple LLM providers without contamination concerns
- ✓Model developers ensuring their training data doesn't overlap with evaluation sets
- ✓Model researchers analyzing capability profiles across different LLM architectures
- ✓Teams selecting models for specific applications requiring particular strengths
- ✓Benchmark designers studying how different domains correlate in model performance
- ✓Model developers monitoring their model's competitive position
- ✓Researchers comparing published models on a single standardized benchmark
Known Limitations
- ⚠Requires reliable publication date metadata from sources — unreliable timestamps can introduce contamination
- ⚠Cannot retroactively verify if models were trained on data after their official cutoff date
- ⚠Limited to domains with clear publication dates (excludes some proprietary or internal knowledge)
- ⚠Domain-specific scoring may not capture cross-domain reasoning that combines multiple capabilities
- ⚠Weighting between domains is fixed rather than customizable per use case
- ⚠Some questions may test multiple domains simultaneously, making attribution ambiguous
Requirements
Input / Output
UnfragileRank
UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.
About
Contamination-free LLM benchmark that continuously updates with new questions from recent information sources, preventing data leakage while evaluating math, coding, reasoning, language, and data analysis capabilities.
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