few-shot multidomain knowledge evaluation across 57 subjects, context-aware prompt generation with few-shot examples, context-window-aware prompt truncation via bpe tokenization, multi-level performance aggregation and hierarchical result reporting, model calibration measurement with multiple metrics and binning strategies, flan model evaluation with standardized inference pipeline, structured subject category taxonomy and hierarchical organization

MMLU

BenchmarkFree

57-subject knowledge benchmark — 15K+ questions across STEM, humanities, professional domains.

Open Source

/ 100

7 capabilities

Capabilities7 decomposed

few-shot multidomain knowledge evaluation across 57 subjects

Medium confidence

Evaluates language models on 15,908 multiple-choice questions organized hierarchically across 57 subjects (STEM, humanities, social sciences, professional) using few-shot prompting methodology. The system generates subject-specific prompts by formatting examples and questions, submits them to models, and aggregates accuracy scores at subject and category levels. This approach tests both breadth of knowledge and depth of reasoning across diverse domains without requiring task-specific fine-tuning.

Solves for

Measure a language model's general knowledge across diverse domains to establish baseline capabilityCompare model performance across specific subject areas (e.g., STEM vs humanities) to identify knowledge gapsBenchmark model improvements over time using a standardized, widely-reported evaluation frameworkIdentify which knowledge domains a model struggles with to guide training or fine-tuning efforts

Best for

LLM researchers and practitioners establishing model performance baselines

Teams evaluating proprietary or open-source models against industry standards

Organizations comparing multiple models before production deployment

Requires

Access to model API or local model inference capability (OpenAI, Anthropic, Ollama, or local LLM)

Python 3.7+ for running evaluation scripts

MMLU dataset (15,908 questions in CSV format, ~50MB total)

Limitations

Multiple-choice format may not capture nuanced reasoning or partial credit for partially correct answers

Few-shot prompting performance varies significantly with example selection and ordering (sensitivity to prompt engineering)

No evaluation of reasoning transparency — only final answer correctness is measured

What makes it unique

Organizes 15,908 questions into a hierarchical taxonomy of 57 subjects with explicit category groupings (STEM, humanities, social sciences, professional), enabling fine-grained performance analysis across knowledge domains rather than treating evaluation as a monolithic task. The few-shot evaluation framework uses subject-specific example formatting via format_subject() and format_example() functions to maintain consistency across diverse question types.

vs alternatives

MMLU is the most widely reported general LLM benchmark with standardized evaluation across 57 subjects, making results directly comparable across published papers and model releases, whereas domain-specific benchmarks (SQuAD, MATH, HumanEval) only measure narrow capabilities.

context-aware prompt generation with few-shot examples

Medium confidence

Dynamically constructs evaluation prompts by formatting subject names, selecting few-shot examples from the training set, and assembling them into a coherent prompt structure that fits within model context windows. The gen_prompt() function orchestrates this process by calling format_subject() to normalize subject names and format_example() to structure individual question-answer pairs, then concatenating them with the target question. This ensures consistent prompt formatting across all 57 subjects while maintaining semantic clarity.

Solves for

Generate standardized few-shot prompts that provide consistent context for model evaluation across all subjectsEnsure prompt formatting is identical across different models to enable fair comparisonDynamically select relevant examples that help models understand the task structureMaintain prompt consistency while adapting to different subject domains and question types

Best for

Researchers implementing few-shot evaluation protocols for language models

Teams building custom benchmarks that require consistent prompt formatting across domains

Developers extending MMLU to new subjects or question types

Requires

MMLU dataset with train/dev/test splits

Python 3.7+ with string formatting capabilities

Subject category definitions (categories.py)

Limitations

Few-shot example selection is deterministic (fixed examples per subject) — does not optimize for example relevance or diversity

Prompt formatting is subject-agnostic — does not adapt structure based on question complexity or domain-specific conventions

No automatic prompt optimization or in-context learning strategies (e.g., chain-of-thought, step-by-step reasoning)

What makes it unique

Implements a modular prompt generation pipeline with separate formatting functions (format_subject, format_example, gen_prompt) that maintain consistency across 57 diverse subjects. The architecture allows subject-specific customization while preserving a unified evaluation interface, enabling researchers to modify prompt templates without changing the core evaluation loop.

vs alternatives

Separates prompt formatting logic from evaluation logic, making it easier to experiment with different prompt structures or few-shot strategies compared to monolithic evaluation scripts where formatting is embedded in the main loop.

context-window-aware prompt truncation via bpe tokenization

Medium confidence

Ensures prompts fit within model context windows by tokenizing text using Byte Pair Encoding (BPE), truncating token sequences to a maximum of 2048 tokens, and decoding back to text. The crop.py module implements this via BPE encoder download (if not cached locally), token truncation, and safe decoding that preserves text integrity. This prevents out-of-context errors when evaluating models with limited context windows while maintaining semantic coherence of the prompt.

Solves for

Automatically adapt prompts to fit within model context window constraints without manual truncationPrevent evaluation failures due to context length exceeded errorsMaintain consistent evaluation across models with different context window sizesEnable evaluation of models with limited context (e.g., mobile or edge-deployed models)

Best for

Evaluating models with context windows smaller than typical MMLU prompt lengths (~1500-2000 tokens)

Researchers testing models across different context window sizes

Automated evaluation pipelines that need to handle variable model constraints

Requires

Python 3.7+

tiktoken library (OpenAI's BPE tokenizer)

Network access to download encoder (first run only, then cached locally)

Limitations

Fixed 2048-token limit may be too aggressive for some models or too lenient for others — not adaptive per model

BPE tokenization is OpenAI-specific (uses tiktoken) — not compatible with other tokenization schemes (SentencePiece, WordPiece)

Truncation may remove critical few-shot examples or context, degrading evaluation validity

What makes it unique

Implements automatic context-window management using BPE tokenization with local caching of encoder resources, enabling transparent prompt adaptation without requiring model-specific configuration. The architecture downloads and caches the encoder on first use, avoiding repeated network calls while maintaining compatibility with OpenAI's tokenization standard.

vs alternatives

Provides automatic, transparent context truncation compared to manual prompt engineering or model-specific context management, reducing evaluation setup complexity for researchers testing multiple models with different context constraints.

multi-level performance aggregation and hierarchical result reporting

Medium confidence

Aggregates model accuracy scores across multiple levels of granularity: per-question (binary correct/incorrect), per-subject (e.g., abstract algebra, anatomy), per-category (e.g., STEM, humanities, social sciences), and overall. The evaluation process iterates through all 15,908 questions, computes subject-level accuracy by averaging question results, then aggregates to category and overall scores. This hierarchical structure enables detailed performance analysis and comparison across knowledge domains.

Solves for

Identify which specific subjects a model performs well or poorly on to guide improvement effortsCompare model performance across high-level categories (STEM vs humanities) to understand knowledge distributionGenerate leaderboard-compatible overall accuracy scores for publication and comparisonAnalyze performance trends across related subjects to identify systematic knowledge gaps

Best for

Researchers analyzing model performance patterns across knowledge domains

Teams publishing model results on MMLU leaderboards

Organizations comparing multiple models to identify relative strengths and weaknesses

Requires

Complete evaluation results for all 15,908 questions

Subject-to-category mapping (defined in categories.py)

Python 3.7+ for aggregation logic

Limitations

Aggregation is simple averaging — does not weight subjects by difficulty or importance

No confidence intervals or statistical significance testing — single point estimates only

No per-question analysis of error types or failure modes — only binary correctness

What makes it unique

Implements a three-level aggregation hierarchy (question → subject → category → overall) that maps directly to the MMLU dataset structure, enabling fine-grained performance analysis while maintaining compatibility with published leaderboard results. The architecture separates aggregation logic from evaluation logic, allowing custom analysis without modifying core evaluation code.

vs alternatives

Provides hierarchical result reporting across 57 subjects and 4 categories, enabling detailed performance analysis compared to single-number benchmarks (e.g., overall accuracy only) that obscure domain-specific strengths and weaknesses.

model calibration measurement with multiple metrics and binning strategies

Medium confidence

Measures how well-calibrated model confidence predictions are using multiple calibration metrics (Expected Calibration Error, Static Calibration Error, Root Mean Square Calibration Error, Adaptive Calibration Error, Threshold Adaptive Calibration Error). The calib_tools.py module implements various binning schemes (uniform, adaptive) and normalization methods to compute calibration across prediction classes. This enables analysis of whether model confidence scores accurately reflect prediction correctness, identifying overconfident or underconfident models.

Solves for

Measure whether a model's confidence scores accurately reflect prediction correctnessIdentify if a model is overconfident (high confidence on incorrect answers) or underconfident (low confidence on correct answers)Compare calibration across different models to select models suitable for high-stakes applicationsAnalyze calibration trends across subjects to identify domains where model confidence is unreliable

Best for

Researchers analyzing model reliability and trustworthiness beyond accuracy metrics

Teams deploying models in high-stakes applications (medical, legal, financial) where confidence calibration matters

Organizations building confidence-based filtering or rejection mechanisms

Requires

Model predictions with confidence scores (logits, probabilities, or log-likelihoods)

Ground truth labels (correct answers)

Python 3.7+ with numpy for metric computation

Limitations

Requires model confidence scores (logits or probabilities) — not all models expose these (e.g., API-only models may only return final answers)

Multiple calibration metrics can produce conflicting signals — no clear guidance on which metric to prioritize

Binning strategies (uniform vs adaptive) significantly affect results — no automatic selection of optimal binning

What makes it unique

Implements five distinct calibration metrics (ECE, SCE, RMSCE, ACE, TACE) with pluggable binning strategies (uniform, adaptive) and normalization methods, enabling comprehensive calibration analysis beyond single-metric approaches. The modular architecture allows researchers to experiment with different calibration definitions and binning strategies without reimplementing core logic.

vs alternatives

Provides multiple calibration metrics and binning strategies compared to single-metric approaches (e.g., ECE only), enabling more nuanced understanding of model confidence reliability and detection of calibration issues that single metrics might miss.

flan model evaluation with standardized inference pipeline

Medium confidence

Implements a complete evaluation pipeline specifically optimized for FLAN (Finetuned LAnguage Net) models, handling model loading, inference, and result collection. The evaluate_flan.py module orchestrates the full evaluation workflow: loading FLAN models, generating subject-specific prompts, executing inference with consistent hyperparameters (temperature, max tokens), collecting predictions, and aggregating results. This standardized pipeline ensures reproducible evaluation across FLAN model variants and versions.

Solves for

Evaluate FLAN model variants (FLAN-T5, FLAN-PaLM, FLAN-Alpaca) using a consistent, reproducible pipelineCompare FLAN model performance across different sizes and training approachesEstablish baseline FLAN performance on MMLU for publication and leaderboard submissionExtend FLAN evaluation to custom datasets or modified prompting strategies

Best for

Researchers evaluating FLAN models or FLAN-derived models

Teams benchmarking instruction-tuned models against base models

Developers implementing FLAN-based systems who need performance baselines

Requires

FLAN model weights (FLAN-T5, FLAN-PaLM, or compatible variant)

Python 3.7+ with transformers library (for FLAN-T5) or access to FLAN API (for FLAN-PaLM)

GPU or TPU for efficient inference (CPU inference is extremely slow for large FLAN models)

Limitations

Pipeline is FLAN-specific — not easily adaptable to other model families (GPT, Claude, Llama) without significant modification

Assumes FLAN models are available locally or via specific APIs — does not abstract over different deployment methods

Fixed hyperparameters (temperature, max tokens) — does not support hyperparameter tuning or sensitivity analysis

What makes it unique

Provides an end-to-end evaluation pipeline specifically optimized for FLAN models, handling model loading, inference, and result aggregation with consistent hyperparameters. The main() function orchestrates the complete workflow, enabling one-command evaluation of FLAN model variants without manual prompt engineering or result processing.

vs alternatives

Offers a standardized FLAN evaluation pipeline compared to generic model evaluation scripts, ensuring reproducible results and enabling fair comparison across FLAN model variants and versions.

structured subject category taxonomy and hierarchical organization

Medium confidence

Defines and maintains a hierarchical taxonomy of 57 subjects organized into 4 high-level categories (STEM, humanities, social sciences, professional). The categories.py module encodes this taxonomy as a structured data structure (likely a dictionary or class hierarchy) that maps subjects to categories, enabling consistent categorization across the evaluation pipeline. This taxonomy is used throughout the evaluation process for subject-level result aggregation, category-level analysis, and leaderboard organization.

Solves for

Organize 57 subjects into meaningful high-level categories for performance analysis and reportingEnable category-level performance comparison (e.g., STEM vs humanities) to identify knowledge distribution patternsMaintain consistent subject-to-category mapping across all evaluation runs and publicationsSupport custom analysis and filtering by category or subject

Best for

Researchers analyzing model performance patterns across knowledge domains

Teams publishing MMLU results with category-level breakdowns

Developers building analysis tools that need subject-to-category mappings

Requires

categories.py file with subject-to-category mappings

Python 3.7+ for importing and using taxonomy

Limitations

Taxonomy is fixed and immutable — cannot add new subjects or reorganize categories without modifying source code

Category definitions are coarse-grained (4 categories) — may obscure fine-grained performance patterns within categories

No weighting or importance ranking — all subjects treated equally in aggregation

What makes it unique

Encodes a structured taxonomy of 57 subjects into 4 categories as a centralized, reusable data structure (categories.py), enabling consistent categorization across all evaluation and analysis code. This separation of taxonomy definition from evaluation logic allows researchers to analyze results at multiple levels of granularity without duplicating category mappings.

vs alternatives

Provides a centralized, version-controlled taxonomy compared to ad-hoc category definitions scattered across analysis scripts, ensuring consistency and enabling reproducible category-level analysis across publications.

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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1 shared capability

Model21

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prompt-optimization-and-few-shot-learning

1 shared capability

Model20

OPT

Open Pretrained Transformers (OPT) by Facebook is a suite of decoder-only pre-trained transformers. [Announcement](https://ai.facebook.com/blog/democratizing-access-to-large-scale-language-models-with-opt-175b/).

prompt-based few-shot learning without fine-tuning

1 shared capability

Model51

Llama-3.2-3B-Instruct

text-generation model by undefined. 36,85,809 downloads.

few-shot learning through in-context examples

1 shared capability

Best For

✓LLM researchers and practitioners establishing model performance baselines
✓Teams evaluating proprietary or open-source models against industry standards
✓Organizations comparing multiple models before production deployment
✓Academic researchers publishing model capabilities in peer-reviewed venues
✓Researchers implementing few-shot evaluation protocols for language models
✓Teams building custom benchmarks that require consistent prompt formatting across domains
✓Developers extending MMLU to new subjects or question types
✓Evaluating models with context windows smaller than typical MMLU prompt lengths (~1500-2000 tokens)

Known Limitations

⚠Multiple-choice format may not capture nuanced reasoning or partial credit for partially correct answers
⚠Few-shot prompting performance varies significantly with example selection and ordering (sensitivity to prompt engineering)
⚠No evaluation of reasoning transparency — only final answer correctness is measured
⚠Subject distribution reflects English-language academic knowledge; limited coverage of non-Western knowledge systems
⚠Static benchmark — does not adapt to model capabilities or provide difficulty scaling
⚠Few-shot example selection is deterministic (fixed examples per subject) — does not optimize for example relevance or diversity

Requirements

Access to model API or local model inference capability (OpenAI, Anthropic, Ollama, or local LLM)Python 3.7+ for running evaluation scriptsMMLU dataset (15,908 questions in CSV format, ~50MB total)Sufficient API quota or compute resources for 15,908+ inference calls per model evaluationMMLU dataset with train/dev/test splitsPython 3.7+ with string formatting capabilitiesSubject category definitions (categories.py)Python 3.7+

Input / Output

Accepts: CSV files containing question, options (A-D), and correct answer, Model identifiers or API endpoints, Configuration parameters (number of few-shot examples, temperature, max tokens), Subject name (string), Question text (string), Multiple-choice options A-D (strings), Number of few-shot examples (integer, typically 5), Prompt text (string), Optional: custom token limit (integer, default 2048), Per-question predictions (model answer, correct answer), Subject labels for each question, Category definitions mapping subjects to high-level groups, Model predictions (integers or strings representing answer choices), Model confidence scores (floats, 0-1 or unnormalized logits), Ground truth labels (integers or strings representing correct answers), FLAN model identifier (e.g., 'google/flan-t5-base'), MMLU dataset path, Evaluation configuration (number of few-shot examples, temperature, max tokens), Subject name (string, e.g., 'abstract_algebra')

Produces: Accuracy scores (overall, per-subject, per-category), Structured results JSON with per-question predictions and correctness, Leaderboard-compatible performance metrics, Formatted prompt string ready for model inference, Prompt with embedded few-shot examples and target question, Truncated prompt text (string), Guaranteed to fit within specified token limit, Overall accuracy (single float, 0-100%), Per-subject accuracy (57 floats), Per-category accuracy (4 floats: STEM, humanities, social sciences, professional), Structured results JSON with hierarchical accuracy breakdown, Calibration metrics (floats, 0-1 scale): ECE, SCE, RMSCE, ACE, TACE, Calibration curves (confidence vs accuracy plots), Per-bin calibration analysis (confidence bins with accuracy and count), Per-subject accuracy scores, Overall accuracy and category-level breakdown, Structured results JSON with per-question predictions, Category name (string, e.g., 'STEM'), List of subjects in a category, Complete taxonomy as structured data

UnfragileRank

Adoption70%(25% weight)

Quality23%(35% weight)

Ecosystem30%(25% weight)

Match Graph10%(10% weight)

Freshness100%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: Benchmark

7 capabilities

Visit MMLU→

About

Massive Multitask Language Understanding. 15,908 questions across 57 subjects (STEM, humanities, social sciences, professional). Tests broad knowledge and problem-solving. The most widely reported general LLM benchmark.

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Capabilities7 decomposed

few-shot multidomain knowledge evaluation across 57 subjects

Medium confidence

Solves for

Best for

LLM researchers and practitioners establishing model performance baselines

Teams evaluating proprietary or open-source models against industry standards

Organizations comparing multiple models before production deployment

Requires

Access to model API or local model inference capability (OpenAI, Anthropic, Ollama, or local LLM)

Python 3.7+ for running evaluation scripts

MMLU dataset (15,908 questions in CSV format, ~50MB total)

Limitations

Multiple-choice format may not capture nuanced reasoning or partial credit for partially correct answers

Few-shot prompting performance varies significantly with example selection and ordering (sensitivity to prompt engineering)

No evaluation of reasoning transparency — only final answer correctness is measured

What makes it unique

vs alternatives

context-aware prompt generation with few-shot examples

Medium confidence

Solves for

Best for

Researchers implementing few-shot evaluation protocols for language models

Teams building custom benchmarks that require consistent prompt formatting across domains

Developers extending MMLU to new subjects or question types

Requires

MMLU dataset with train/dev/test splits

Python 3.7+ with string formatting capabilities

Subject category definitions (categories.py)

Limitations

Few-shot example selection is deterministic (fixed examples per subject) — does not optimize for example relevance or diversity

Prompt formatting is subject-agnostic — does not adapt structure based on question complexity or domain-specific conventions

No automatic prompt optimization or in-context learning strategies (e.g., chain-of-thought, step-by-step reasoning)

What makes it unique

vs alternatives

context-window-aware prompt truncation via bpe tokenization

Medium confidence

Solves for

Best for

Evaluating models with context windows smaller than typical MMLU prompt lengths (~1500-2000 tokens)

Researchers testing models across different context window sizes

Automated evaluation pipelines that need to handle variable model constraints

Requires

Python 3.7+

tiktoken library (OpenAI's BPE tokenizer)

Network access to download encoder (first run only, then cached locally)

Limitations

Fixed 2048-token limit may be too aggressive for some models or too lenient for others — not adaptive per model

BPE tokenization is OpenAI-specific (uses tiktoken) — not compatible with other tokenization schemes (SentencePiece, WordPiece)

Truncation may remove critical few-shot examples or context, degrading evaluation validity

What makes it unique

vs alternatives

multi-level performance aggregation and hierarchical result reporting

Medium confidence

Solves for

Best for

Researchers analyzing model performance patterns across knowledge domains

Teams publishing model results on MMLU leaderboards

Organizations comparing multiple models to identify relative strengths and weaknesses

Requires

Complete evaluation results for all 15,908 questions

Subject-to-category mapping (defined in categories.py)

Python 3.7+ for aggregation logic

Limitations

Aggregation is simple averaging — does not weight subjects by difficulty or importance

No confidence intervals or statistical significance testing — single point estimates only

No per-question analysis of error types or failure modes — only binary correctness

What makes it unique

vs alternatives

model calibration measurement with multiple metrics and binning strategies

Medium confidence

Solves for

Best for

Researchers analyzing model reliability and trustworthiness beyond accuracy metrics

Teams deploying models in high-stakes applications (medical, legal, financial) where confidence calibration matters

Organizations building confidence-based filtering or rejection mechanisms

Requires

Model predictions with confidence scores (logits, probabilities, or log-likelihoods)

Ground truth labels (correct answers)

Python 3.7+ with numpy for metric computation

Limitations

Requires model confidence scores (logits or probabilities) — not all models expose these (e.g., API-only models may only return final answers)

Multiple calibration metrics can produce conflicting signals — no clear guidance on which metric to prioritize

Binning strategies (uniform vs adaptive) significantly affect results — no automatic selection of optimal binning

What makes it unique

vs alternatives

flan model evaluation with standardized inference pipeline

Medium confidence

Solves for

Best for

Researchers evaluating FLAN models or FLAN-derived models

Teams benchmarking instruction-tuned models against base models

Developers implementing FLAN-based systems who need performance baselines

Requires

FLAN model weights (FLAN-T5, FLAN-PaLM, or compatible variant)

Python 3.7+ with transformers library (for FLAN-T5) or access to FLAN API (for FLAN-PaLM)

GPU or TPU for efficient inference (CPU inference is extremely slow for large FLAN models)

Limitations

Pipeline is FLAN-specific — not easily adaptable to other model families (GPT, Claude, Llama) without significant modification

Assumes FLAN models are available locally or via specific APIs — does not abstract over different deployment methods

Fixed hyperparameters (temperature, max tokens) — does not support hyperparameter tuning or sensitivity analysis

What makes it unique

vs alternatives

Offers a standardized FLAN evaluation pipeline compared to generic model evaluation scripts, ensuring reproducible results and enabling fair comparison across FLAN model variants and versions.

structured subject category taxonomy and hierarchical organization

Medium confidence

Solves for

Best for

Researchers analyzing model performance patterns across knowledge domains

Teams publishing MMLU results with category-level breakdowns

Developers building analysis tools that need subject-to-category mappings

Requires

categories.py file with subject-to-category mappings

Python 3.7+ for importing and using taxonomy

Limitations

Taxonomy is fixed and immutable — cannot add new subjects or reorganize categories without modifying source code

Category definitions are coarse-grained (4 categories) — may obscure fine-grained performance patterns within categories

No weighting or importance ranking — all subjects treated equally in aggregation

What makes it unique

vs alternatives

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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MMLU

Capabilities7 decomposed

few-shot multidomain knowledge evaluation across 57 subjects

context-aware prompt generation with few-shot examples

context-window-aware prompt truncation via bpe tokenization

multi-level performance aggregation and hierarchical result reporting

model calibration measurement with multiple metrics and binning strategies

flan model evaluation with standardized inference pipeline

structured subject category taxonomy and hierarchical organization

Related Artifactssharing capabilities

Qwen3-8B

Qwen: Qwen3 32B

OpenAI: GPT-5.2

MiniMax: MiniMax M2.1

OPT

Llama-3.2-3B-Instruct

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to MMLU

Are you the builder of MMLU?

Get the weekly brief

Data Sources

MMLU

Capabilities7 decomposed

few-shot multidomain knowledge evaluation across 57 subjects

context-aware prompt generation with few-shot examples

context-window-aware prompt truncation via bpe tokenization

multi-level performance aggregation and hierarchical result reporting

model calibration measurement with multiple metrics and binning strategies

flan model evaluation with standardized inference pipeline

structured subject category taxonomy and hierarchical organization

Related Artifactssharing capabilities

Qwen3-8B

Qwen: Qwen3 32B

OpenAI: GPT-5.2

MiniMax: MiniMax M2.1

OPT

Llama-3.2-3B-Instruct

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to MMLU

Are you the builder of MMLU?

Get the weekly brief

Data Sources