datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Long-Horizon-Terminal-Bench
Long-Horizon Terminal-Bench (LHTB)
LHTB is a 46-task benchmark for measuring how well LLM agents sustain useful
work in a containerized terminal over hundreds of steps. Unlike short-horizon
coding benchmarks where an agent writes one artifact and stops, LHTB drops the agent
into a stateful environment and grades it with hidden, rebuild-from-artifact
verifiers — self-reported progress does not count.
📝 Blog: https://zli12321.github.io/LHTB/
🏆 Leaderboard:… See the full description on the dataset page: https://huggingface.co/datasets/IntelligenceLab/Long-Horizon-Terminal-Bench.explicit-edit-benchmark
Explicit Edit Benchmark
226 deterministic exact-edit tasks, run by different agents, harnesses, models and configurations. Every observation records what the harness did and whether the resulting files matched byte for byte.
Source code and benchmark runner: GitHub — Explicit Edit Benchmark
Open the interactive Explorer to compare agents, harnesses, models, versions, reasoning modes, correctness, recovery, time, cost and tokens.
Leaderboard by model route
Score v2… See the full description on the dataset page: https://huggingface.co/datasets/alexshpunt/explicit-edit-benchmark.car-bench-dataset
CAR-Bench Dataset
CAR-Bench is a benchmark for evaluating AI voice assistants in a realistic automotive (car) environment.
It tests an agent's ability to correctly use vehicle control tools, handle disambiguation, and avoid hallucinations.
Dataset Structure
The dataset is organized into task configs and mock data configs:
Tasks
Each task defines a user persona, an instruction, the initial vehicle/environment context, and the ground-truth sequence of tool-call… See the full description on the dataset page: https://huggingface.co/datasets/johanneskirmayr/car-bench-dataset.chi-bench
Clinical Healthcare In-Situ Environment
Task fixtures for a long-horizon, policy-rich healthcare-workflow agent benchmark
What is in this dataset
CHI-Bench evaluates AI agents on end-to-end U.S. healthcare workflows across three long-horizon domains: provider prior authorization, payer utilization management, and population care management. Each task hands the agent a clinical case in a high-fidelity simulator of 20 healthcare apps exposed over MCP, with a 1… See the full description on the dataset page: https://huggingface.co/datasets/actava/chi-bench.migration-bench-java-full
MigrationBench
1. 📖 Overview
🤗 MigrationBench
is a large-scale code migration benchmark dataset at the repository level,
across multiple programming languages.
Current and initial release includes java 8 repositories with the maven build system… See the full description on the dataset page: https://huggingface.co/datasets/AmazonScience/migration-bench-java-full.terminal-bench
Terminal-Bench Dataset
This dataset contains tasks from Terminal-Bench, a benchmark for evaluating AI agents in real terminal environments. Each task is packaged as a complete, self-contained archive that preserves the exact directory structure, binary files, Docker configurations, and test scripts needed for faithful reproduction.
The archive column contains a gzipped tarball of the entire task directory.
Dataset Overview
Terminal-Bench evaluates AI agents on… See the full description on the dataset page: https://huggingface.co/datasets/ia03/terminal-bench.panda-bench
PandaBench
PandaBench is a comprehensive benchmark for evaluating Large Language Model (LLM) safety, focusing on jailbreak attacks, defense mechanisms, and evaluation methodologies.
The PandaGuard framework architecture illustrating the end-to-end pipeline for LLM safety evaluation. The system connects three key components: Attackers, Defenders, and Judges.
Dataset Description
This repository contains the benchmark results from extensive evaluations of various… See the full description on the dataset page: https://huggingface.co/datasets/Beijing-AISI/panda-bench.BenchCAD
BenchCAD
Three-config dataset for CAD evaluation:
edit-bench — held-out CAD edit benchmark.
code_gen — 17,900 synthetic CadQuery samples (compact 12-column variant)
covering 106 mechanical part families. Each row contains the GT CadQuery code
plus 5 normalized renders.
QA — CAD question-answering benchmark.
code_gen schema (12 columns)
Column
Type
Description
stem
string
unique sample identifier
family
string
mechanical part family (106 distinct)… See the full description on the dataset page: https://huggingface.co/datasets/BenchCAD/BenchCAD.SWE-Bench-ProMax
🚀 SWE-Bench-ProMax
A Contamination-Resistant, Multi-Language Benchmark for Repository-Level Issue Resolution
📄 Paper ·
📊 Dataset ·
⚙️ Evaluation ·
📬 Contact
SWE-Bench-ProMax is a multi-language benchmark for repository-level software issue resolution, accepted to COLM 2026. All task instances are collected from real-world GitHub issues created after 2025, substantially reducing the risk of training-data contamination for contemporary language models. Each instance provides a task… See the full description on the dataset page: https://huggingface.co/datasets/swe-bench-promax/SWE-Bench-ProMax.LEXam
LEXam: Benchmarking Legal Reasoning on 340 Law Exams
A diverse, rigorous evaluation suite for legal AI from Swiss, EU, and international law examinations.
Paper | Website & Leaderboard | GitHub Repository
🔥 News
[2026/01] Our paper has been accepted to ICLR 2026!
[2025/12] We reorganized all multiple-choice questions into four separate files, mcq_4_choices (n = 1,655), mcq_8_choices (n = 1,463), mcq_16_choices (n = 1,028), and mcq_32_choices (n = 550), all… See the full description on the dataset page: https://huggingface.co/datasets/LEXam-Benchmark/LEXam.hwe-bench
HWE-bench
HWE-bench is a benchmark for evaluating LLM agents on real-world hardware bug repair tasks. It contains 417 cases from six open-source hardware repositories covering Verilog, SystemVerilog, and Chisel projects.
Each case is a fail-to-pass task: the provided test fails on the buggy baseline and passes after the ground-truth fix. Evaluation scripts, Docker image instructions, and agent-running code are available in the project repository.
Files
The dataset is… See the full description on the dataset page: https://huggingface.co/datasets/henryen/hwe-bench.rtx-5090-benchmarks
RTX 5090 LLM Benchmarks
Speed and quality benchmarks for quantized LLMs on NVIDIA RTX 5090 32GB, measured with llm-bench-rig.
Quality Benchmarks
Generative evaluation through llama-server chat completions. Replicates standard benchmark methodology using custom evaluators — no lm-evaluation-harness dependency.
Results are split by reasoning mode: comparing a thinking-on (reasoning) model's quality against a thinking-off model is apples-to-oranges, so the two groups… See the full description on the dataset page: https://huggingface.co/datasets/witcheer/rtx-5090-benchmarks.b3-agent-security-benchmark-weak[paper] [blogpost] [game]
b3 AI Security Benchmark: Breaking Agent Backbones
Highly contextalized prompt injections crowd-sourced during the Gandalf Agent Breaker Challenge.
This is a low-quality version of the data behind Breaking Agent Backbones: Evaluating the Security
of Backbone LLMs in AI Agents.
The high quality dataset was used to evaluate the security of more than 30 LLMs.
Dataset Summary
Purpose: This dataset contains crowdsourced adversarial attacks… See the full description on the dataset page: https://huggingface.co/datasets/Lakera/b3-agent-security-benchmark-weak.migration-bench-java-selected
MigrationBench
1. 📖 Overview
🤗 MigrationBench
is a large-scale code migration benchmark dataset at the repository level,
across multiple programming languages.
Current and initial release includes java 8 repositories with the maven build system… See the full description on the dataset page: https://huggingface.co/datasets/AmazonScience/migration-bench-java-selected.ALL-Bench-Leaderboard
🏆 ALL Bench Leaderboard 2026
The only AI benchmark dataset covering LLM · VLM · Agent · Image · Video · Music in a single unified file.
Dataset Summary
ALL Bench Leaderboard aggregates and cross-verifies benchmark scores for 90+ AI models across 6 modalities. Every numerical score is tagged with a confidence level (cross-verified, single-source, or self-reported) and its original source. The dataset is designed for researchers, developers, and… See the full description on the dataset page: https://huggingface.co/datasets/FINAL-Bench/ALL-Bench-Leaderboard.aeslides-reward-bench
AeSlides-Reward-Bench
This dataset is part of the work presented in the paper AeSlides: Incentivizing Aesthetic Layout in LLM-Based Slide Generation via Verifiable Rewards.
AeSlides is a reinforcement learning framework with verifiable rewards for aesthetic layout supervision in slide generation. This benchmark focuses on quantifying slide layout quality through verifiable metrics like aspect ratio compliance, whitespace reduction, and visual balance.
GitHub Repository:… See the full description on the dataset page: https://huggingface.co/datasets/ympan/aeslides-reward-bench.Auto-Fill-Benchmark
Auto-Fill Benchmark
Benchmark for predicting missing cell values in real-world tables, introduced in
Auto-Fill: Learning to Predict Missing Values Accurately with Specialist Language Models
(PVLDB 19(11), 2026 — arXiv:2607.19847).
Each case is a real table in which exactly one cell is replaced by [MISSING], together with the ground-truth value.
Code: https://github.com/lyrain2001/auto-fill
Models: Auto-Fill-Qwen3-8B-Knowledge ·
Auto-Fill-Qwen3-8B-Reasoning ·… See the full description on the dataset page: https://huggingface.co/datasets/lyrain2001/Auto-Fill-Benchmark.sovereign-shadow-inference-bench
Sovereign Shadow Inference Bench
A public, versioned evidence surface for independent Hugging Face shadow inference beside Sovereign's primary OpenRouter/Revolver route.
What this dataset proves
The seed record in data/shadow_receipts.jsonl was produced by one real Hugging Face Inference Providers request. It records provider/model identity, request bounds, latency, hashes, literal-match outcome, source revision, and an immutable receipt hash.
What it… See the full description on the dataset page: https://huggingface.co/datasets/Thorsu/sovereign-shadow-inference-bench.aacr-bench
Dataset for Running AACR-Bench
English | 简体中文
This is a test set designed for automated code review reflection models, primarily aiming to evaluate the extent to which a model can intercept low-quality review comments. The dataset contains 2,145 code review comments, consisting of 1,505 expert-verified correct comments and 640 incorrect comments.
This data is part of the AACR-Bench project and is provided by the Alibaba Aone team.
Data Sample
Each sample in the… See the full description on the dataset page: https://huggingface.co/datasets/Alibaba-Aone/aacr-bench.R2-Bench
R2-Bench
R2-Bench is a benchmark dataset for evaluating LLM routing with joint model and token budget optimization. It contains 30,968 queries evaluated across 10 LLMs at 16 token budget levels, with LLM-judge quality scores.
Associated with R2-Router (code), under review at ICML 2026.
Dataset Structure
data/
├── meta-llama/
│ ├── Llama-3.1-70B-Instruct/
│ │ ├── 10_judge.csv
│ │ ├── 20_judge.csv
│ │ ├── ...
│ │ └── 8000_judge.csv
│ └──… See the full description on the dataset page: https://huggingface.co/datasets/JiaqiXue/R2-Bench.legal-llm-benchmark
Legal LLM Benchmark Dataset
Safety-Utility Trade-offs in Legal AI: An LLM Evaluation Across 12 Models
Quick Start
from datasets import load_dataset
# Core datasets
questions = load_dataset("marvintong/legal-llm-benchmark", "questions")
phase1_evals = load_dataset("marvintong/legal-llm-benchmark", "phase1_evaluations")
phase3_evals = load_dataset("marvintong/legal-llm-benchmark", "phase3_evaluations")
# Additional helpful datasets
contracts =… See the full description on the dataset page: https://huggingface.co/datasets/marvintong/legal-llm-benchmark.CC-Bench-trajectories
CC-Bench Trajectories Overview
To evaluate GLM-4.6's agentic coding capabilities in real-world scenarios, we developed CC-Bench-V1.1 using Claude Code as the agentic coding testbed. Building on CC-Bench-V1.0, we added 22 more challenging coding tasks and conducted comprehensive evaluations against Claude-Sonnet-4, GLM-4.5, Kimi-K2-0905, and DeepSeek-V3.1-Terminus. The benchmark comprises 74 coding tasks spanning frontend development, tool development, data analysis, testing, and… See the full description on the dataset page: https://huggingface.co/datasets/zai-org/CC-Bench-trajectories.ALL-Bench-Leaderboard
🏆 ALL Bench Leaderboard 2026
The only AI benchmark dataset covering LLM · VLM · Agent · Image · Video · Music in a single unified file.
Dataset Summary
ALL Bench Leaderboard aggregates and cross-verifies benchmark scores for 90+ AI models across 6 modalities. Every numerical score is tagged with a confidence level (cross-verified, single-source, or self-reported) and its original source. The dataset is designed for researchers, developers, and… See the full description on the dataset page: https://huggingface.co/datasets/youssef3146/ALL-Bench-Leaderboard.H2EPR-Bench
H²EPR-Bench
An Evidence-Traceable Benchmark for Event-Process Reconstruction
H²EPR-Bench asks a demanding question: can a model reconstruct how a complex
real-world event unfolded, rather than merely summarize what happened? Given an
event specification and fixed multi-source evidence, a system produces a
hierarchical heterogeneous Event-Process Graph (EPG) that makes stages,
episodes, participants, actions, outcomes, relations, and evidence support
explicit.… See the full description on the dataset page: https://huggingface.co/datasets/AgenticFinLab/H2EPR-Bench.roleplay-bench
RP-Bench: Roleplay Quality Benchmark for LLMs
A multi-dimensional evaluation framework for measuring how well LLMs perform in roleplay scenarios — not just writing quality, but character consistency, user agency respect, lorebook integration, temporal reasoning, and genre-specific craft.
The LLM-as-judge signals in this benchmark disagree with real users about half the time. We're calibrating against human preferences via a public blind-arena. Help out at arena.l3vi4th4n.ai — each… See the full description on the dataset page: https://huggingface.co/datasets/lazyweasel/roleplay-bench.anamnesis-bench
AnamnesisBench
AnamnesisBench is an evaluation benchmark for numerical reliability in LLM research agents.
It focuses on a practical failure mode: an agent writes or accepts a financial research artifact that
looks plausible, but contains a wrong, unsupported, or misattributed number.
The benchmark is not intended as training data. It is a set of test cases, source packets, expected
truth values, and deterministic scoring scripts. You run your own model or verifier, then score… See the full description on the dataset page: https://huggingface.co/datasets/pppop7/anamnesis-bench.role-play-bench
Role-play Benchmark
A comprehensive benchmark for evaluating Role-play Agents in Chinese and English scenarios.
Dataset Summary
Role-play Benchmark is designed to evaluate Role-play Agents' ability to deliver immersive role-play experiences through Situated Reenactment. Unlike traditional benchmarks with verifiable answers, Role-play is fundamentally non-verifiable, e.g., there's no single "correct" response when a tsundere character is asked "Do you like me?".… See the full description on the dataset page: https://huggingface.co/datasets/MiniMaxAI/role-play-bench.local-llm-benchmark
Local LLM Benchmark — Technical and Uncensored Behavior (NVIDIA RTX 5070 Ti 16GB)
English | 简体中文 | 繁體中文 | 한국어 | Español | 日本語 | हिन्दी | Русский | Português | తెలుగు | Français | Deutsch | Italiano | Tiếng Việt | العربية | اردو | বাংলা | فارسی | Română | Türkçe
Manual evaluation results of local GGUF model variants on a single consumer machine,
combining two fully independent benchmarks:
technical/
uncensored/
Measures
capability: coding, systems, networking, DB, agents… See the full description on the dataset page: https://huggingface.co/datasets/nanimani/local-llm-benchmark.R-3-Bench R3-Bench: LLMs Struggle with Resource-Rational Reasoning under Shared Budgets
Overview
R3-Bench evaluates resource-rational reasoning when multiple problems share a
limited resource budget. This release contains the frozen benchmark data used
by the paper across three domains:
Config
Problems
Suites
Difficulty counts
math
300
50
150 easy / 100 medium / 50 hard
coding
300
50
150 easy / 100 medium / 50 hard
abstract_reasoning
300
50
150 easy /… See the full description on the dataset page: https://huggingface.co/datasets/R-3-Bench/R-3-Bench.peerreview-bench
PeerReview Bench
CMU Paper Reviewer:https://prometheus-eval.github.io/cmu-paper-reviewer/
Repository:https://github.com/prometheus-eval/cmu-paper-reviewer
Paper:https://arxiv.org/abs/2605.20668
Point of Contact:seungone@kaist.ac.kr
Expert-annotated review items from scientific papers, organized for three
complementary evaluation tasks. All data in this dataset is intended
for evaluation, not training. All configs reference a shared, deduplicated
file store (submitted_papers)… See the full description on the dataset page: https://huggingface.co/datasets/prometheus-eval/peerreview-bench.