SafetyBench vs cua
Side-by-side comparison to help you choose.
| Feature | SafetyBench | cua |
|---|---|---|
| Type | Dataset | Agent |
| UnfragileRank | 45/100 | 53/100 |
| Adoption | 1 | 1 |
| Quality | 0 | 1 |
| Ecosystem | 0 |
| 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 15 decomposed |
| Times Matched | 0 | 0 |
Provides 11,435 curated multiple-choice questions across 7 safety categories in both Chinese and English, with standardized JSON structure containing question ID, category, question text, 4-option choices, and ground-truth answer mappings (0->A, 1->B, 2->C, 3->D). Data is hosted on Hugging Face and downloadable via shell script or Python datasets library, enabling reproducible safety benchmarking across language variants.
Unique: Combines 11,435 questions across 7 safety categories with explicit bilingual (Chinese/English) support and category-level granularity, rather than single-language or aggregate safety scoring. Includes both full test sets and filtered subsets (test_zh_subset with 300 questions per category) to accommodate different evaluation scales.
vs alternatives: Larger and more category-diverse than most single-language safety benchmarks, with native bilingual support enabling cross-linguistic safety analysis that monolingual datasets cannot provide.
Implements dual evaluation modes (zero-shot and five-shot) with carefully engineered prompt templates that present questions directly or with 5 in-context examples per category. The system constructs prompts, sends them to target models, and extracts predicted answers from model responses using configurable parsing logic. Example implementation provided in evaluate_baichuan.py demonstrates the full pipeline for any model with text generation capability.
Unique: Provides dual evaluation modes with explicit few-shot example sets (5 per category) rather than random in-context learning, enabling controlled comparison of zero-shot vs few-shot safety performance. Includes reference implementation (evaluate_baichuan.py) showing answer extraction patterns for production use.
vs alternatives: More systematic than ad-hoc prompt engineering because it standardizes prompt templates and provides category-specific few-shot examples, enabling reproducible cross-model comparisons that single-prompt benchmarks cannot guarantee.
Organizes 11,435 questions into 7 distinct safety categories, enabling per-category accuracy calculation and comparative analysis of model strengths/weaknesses across harm types. The evaluation pipeline computes metrics at both aggregate and category levels, allowing researchers to identify which safety domains (e.g., illegal activities, violence, bias) a model handles well vs poorly. Leaderboard submission format requires predictions per question ID, enabling automated category-level metric computation.
Unique: Explicitly structures evaluation around 7 safety categories rather than single aggregate score, enabling fine-grained analysis of model safety across specific harm domains. Leaderboard infrastructure supports category-level metric computation from per-question predictions.
vs alternatives: More diagnostic than single-score safety benchmarks because category-level breakdown reveals which specific harm types a model handles poorly, enabling targeted safety improvements rather than generic safety training.
Provides dual download mechanisms (shell script via download_data.sh and Python via download_data.py using Hugging Face datasets library) to retrieve 11,435 questions in both Chinese and English from Hugging Face Hub. Data files include full test sets (test_en.json, test_zh.json), filtered Chinese subset (test_zh_subset.json with 300 questions per category), and few-shot examples (dev_en.json, dev_zh.json). Integration with Hugging Face datasets library enables programmatic access, caching, and version control.
Unique: Provides dual download mechanisms (shell script and Python library) with explicit support for filtered subsets (test_zh_subset.json) and language-specific files, rather than monolithic dataset downloads. Native Hugging Face datasets library integration enables programmatic access and caching.
vs alternatives: More flexible than manual download because it supports both scripted and programmatic access, filtered subsets for smaller evaluations, and Hugging Face caching for faster repeated access compared to static file distribution.
Defines standardized JSON submission format for leaderboard ranking: UTF-8 encoded JSON with question IDs as keys and predicted answer indices (0-3) as values. Submission infrastructure at llmbench.ai/safety accepts formatted results and computes aggregate and category-level metrics for public leaderboard ranking. Standardized format enables automated metric computation and fair cross-model comparison.
Unique: Defines explicit JSON submission format with question ID keys and answer index values (0-3 mapping), enabling automated metric computation and fair leaderboard ranking. Standardized format ensures cross-implementation comparability.
vs alternatives: More rigorous than ad-hoc result reporting because standardized format prevents metric computation errors and enables automated leaderboard updates, whereas free-form submissions require manual validation and metric recalculation.
Provides test_zh_subset.json containing 300 questions per safety category (2,100 total) filtered from full Chinese test set to remove sensitive keywords, enabling smaller-scale safety evaluation for resource-constrained scenarios. Subset maintains category balance and representativeness while reducing evaluation cost by ~82% compared to full 11,435-question dataset. Useful for rapid prototyping, continuous integration, or low-latency evaluation pipelines.
Unique: Provides explicit filtered subset (test_zh_subset.json) with 300 questions per category and sensitive keyword filtering, rather than requiring users to manually sample or filter the full dataset. Enables rapid evaluation while maintaining category balance.
vs alternatives: More efficient than random sampling from full dataset because it provides pre-filtered, category-balanced subset with documented filtering approach, reducing evaluation time by ~82% while maintaining statistical representativeness.
Captures desktop screenshots and feeds them to 100+ integrated vision-language models (Claude, GPT-4V, Gemini, local models via adapters) to reason about UI state and determine appropriate next actions. Uses a unified message format (Responses API) across heterogeneous model providers, enabling the agent to understand visual context and generate structured action commands without brittle selector-based logic.
Unique: Implements a unified Responses API message format abstraction layer that normalizes outputs from 100+ heterogeneous VLM providers (native computer-use models like Claude, composed models via grounding adapters, and local model adapters), eliminating provider-specific parsing logic and enabling seamless model swapping without agent code changes.
vs alternatives: Broader model coverage and provider flexibility than Anthropic's native computer-use API alone, with explicit support for local/open-source models and a standardized message format that decouples agent logic from model implementation details.
Provisions isolated execution environments across macOS (via Lume VMs), Linux (Docker), Windows (Windows Sandbox), and host OS, with unified provider abstraction. Handles VM/container lifecycle (creation, snapshot management, cleanup), resource allocation, and OS-specific action handlers (keyboard/mouse events, clipboard, file system access) through a pluggable provider architecture that abstracts platform differences.
Unique: Implements a pluggable provider architecture with unified Computer interface that abstracts OS-specific action handlers (macOS native events via Lume, Linux X11/Wayland via Docker, Windows input simulation via Windows Sandbox API), enabling single agent code to target multiple platforms. Includes Lume VM management with snapshot/restore capabilities for deterministic testing.
vs alternatives: More comprehensive OS coverage than single-platform solutions; Lume provider offers native macOS VM support with snapshot capabilities unavailable in Docker-only alternatives, while unified provider abstraction reduces code duplication vs. platform-specific agent implementations.
cua scores higher at 53/100 vs SafetyBench at 45/100. SafetyBench leads on adoption, while cua is stronger on quality and ecosystem.
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Provides Lume provider for provisioning and managing macOS virtual machines with native support for snapshot creation, restoration, and cleanup. Handles VM lifecycle (boot, shutdown, resource allocation) with optimized startup times. Integrates with image registry for VM image management and caching. Supports both Apple Silicon and Intel Macs. Enables deterministic testing through snapshot-based environment reset between agent runs.
Unique: Implements Lume provider with native macOS VM management including snapshot/restore capabilities for deterministic testing, optimized startup times, and image registry integration. Supports both Apple Silicon and Intel Macs with unified provider interface.
vs alternatives: More efficient than Docker for macOS because Lume uses native virtualization (Virtualization Framework) vs. Docker's slower emulation; snapshot/restore enables faster environment reset vs. full VM recreation.
Provides command-line interface (CLI) for quick-start agent execution, configuration, and testing without writing code. Includes Gradio-based web UI for interactive agent control, real-time monitoring, and trajectory visualization. CLI supports task specification, model selection, environment configuration, and result export. Web UI enables non-technical users to run agents and view execution traces with HUD visualization.
Unique: Implements both CLI and Gradio web UI for agent execution, with CLI supporting quick-start scenarios and web UI enabling interactive control and real-time monitoring with HUD visualization. Reduces barrier to entry for non-technical users.
vs alternatives: More accessible than SDK-only frameworks because CLI and web UI enable non-developers to run agents; Gradio integration provides quick UI prototyping vs. custom web development.
Implements Docker provider for running agents in containerized Linux environments with full isolation. Handles container lifecycle (creation, cleanup), image management, and volume mounting for persistent storage. Supports custom Dockerfiles for environment customization. Provides X11/Wayland display server integration for GUI application interaction. Enables reproducible agent execution across different host systems.
Unique: Implements Docker provider with X11/Wayland display server integration for GUI application interaction, container lifecycle management, and custom Dockerfile support. Enables reproducible agent execution across different host systems with container isolation.
vs alternatives: More lightweight than VMs because Docker uses container isolation vs. full virtualization; X11 integration enables GUI application support vs. headless-only alternatives.
Implements Windows Sandbox provider for isolated agent execution on Windows 10/11 Pro/Enterprise, and host provider for direct OS execution. Windows Sandbox provider creates ephemeral sandboxed environments with automatic cleanup. Host provider enables direct agent execution on live Windows system without isolation. Both providers support native Windows input simulation (SendInput API) and clipboard operations. Handles Windows-specific action execution (window management, registry access).
Unique: Implements both Windows Sandbox provider (ephemeral isolated environments with automatic cleanup) and host provider (direct OS execution) with native Windows input simulation (SendInput API) and clipboard support. Handles Windows-specific action execution including window management.
vs alternatives: Windows Sandbox provides better isolation than host execution while avoiding VM overhead; native SendInput API enables more reliable input simulation than generic input methods.
Implements comprehensive telemetry and logging infrastructure capturing agent execution metrics (latency, token usage, action success rate), errors, and performance data. Supports structured logging with contextual information (task ID, agent ID, timestamp). Integrates with external monitoring systems (e.g., Datadog, CloudWatch) for centralized observability. Provides error categorization and automatic error recovery suggestions. Enables debugging through detailed execution logs with configurable verbosity levels.
Unique: Implements structured telemetry and logging system with contextual information (task ID, agent ID, timestamp), error categorization, and automatic error recovery suggestions. Integrates with external monitoring systems for centralized observability.
vs alternatives: More comprehensive than basic logging because it captures metrics and structured context; integration with external monitoring enables centralized observability vs. log file analysis.
Implements the core agent loop (screenshot → LLM reasoning → action execution → repeat) via the ComputerAgent class, with pluggable callback system and custom loop support. Developers can override loop behavior at multiple extension points: custom agent loops (modify reasoning/action selection), custom tools (add domain-specific actions), and callback hooks (inject monitoring/logging). Supports both synchronous and asynchronous execution patterns.
Unique: Provides a callback-based extension system with multiple hook points (pre/post action, loop iteration, error handling) and explicit support for custom agent loop subclassing, allowing developers to override core loop logic without forking the framework. Supports both native computer-use models and composed models with grounding adapters.
vs alternatives: More flexible than frameworks with fixed loop logic; callback system enables non-invasive monitoring/logging vs. requiring loop subclassing, while custom loop support accommodates novel agent architectures that standard loops cannot express.
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