BIG-Bench Hard (BBH) vs cua
Side-by-side comparison to help you choose.
| Feature | BIG-Bench Hard (BBH) | cua |
|---|---|---|
| Type | Dataset | Agent |
| UnfragileRank | 46/100 | 53/100 |
| Adoption | 1 | 1 |
| Quality | 0 | 1 |
| Ecosystem |
| 0 |
| 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 15 decomposed |
| Times Matched | 0 | 0 |
Filters 23 challenging tasks from the original 200+ BIG-Bench tasks using a selection criterion: tasks where language models initially scored below average human rater performance. This curation approach identifies reasoning bottlenecks rather than knowledge gaps, enabling targeted evaluation of model reasoning capabilities. The selection process creates a focused benchmark that isolates genuine reasoning difficulty from task ambiguity or knowledge requirements.
Unique: Uses human performance as the filtering criterion rather than task complexity metrics or synthetic difficulty scores. This ensures the benchmark captures tasks where models genuinely underperform humans, not just tasks that are theoretically hard.
vs alternatives: More aligned with real model limitations than generic 'hard task' benchmarks because it filters by actual human-vs-model performance gap rather than task designer intuition
Provides 2-8 few-shot examples per task that demonstrate chain-of-thought (CoT) reasoning patterns — showing intermediate reasoning steps rather than just input-output pairs. These exemplars are structured to guide models toward step-by-step decomposition of reasoning problems. The exemplars are manually curated to illustrate the reasoning strategy most effective for each task type (e.g., breaking arithmetic into sub-steps, listing logical premises before deduction).
Unique: Exemplars are task-specific and manually validated for reasoning quality rather than automatically generated or randomly sampled. Each task's exemplars are designed to illustrate the particular decomposition strategy most effective for that reasoning type.
vs alternatives: More effective than generic few-shot templates because exemplars are tailored to each task's reasoning structure, reducing the need for prompt engineering and enabling fairer cross-model comparison
Aggregates 23 tasks spanning distinct reasoning domains: algorithmic reasoning (e.g., sorting, graph traversal), multi-step arithmetic, logical deduction, causal judgment, and spatial reasoning. Each domain tests different cognitive capabilities, enabling diagnostic evaluation of which reasoning types models struggle with. The task distribution is designed to avoid clustering in a single reasoning modality, providing a balanced assessment across reasoning categories.
Unique: Explicitly structures tasks across five distinct reasoning domains rather than treating reasoning as monolithic. This enables diagnostic analysis of which cognitive capabilities models lack, not just overall reasoning performance.
vs alternatives: More diagnostic than single-domain benchmarks because it reveals which reasoning types are model bottlenecks, enabling targeted improvements rather than generic reasoning optimization
Includes human rater performance scores for each task, enabling direct comparison of model outputs against human reasoning ability. The baseline is computed from multiple human annotators per task, providing a reference point for what constitutes 'solved' reasoning. Models are evaluated on whether they meet, exceed, or fall short of human performance, creating a human-anchored evaluation framework rather than absolute accuracy metrics.
Unique: Uses human performance as the primary evaluation anchor rather than absolute accuracy or comparison to prior models. This grounds evaluation in human-level reasoning capability rather than relative model rankings.
vs alternatives: More interpretable than accuracy-only metrics because human baselines provide context for what performance means in practice, enabling stakeholders to assess whether models are approaching human-level reasoning
Explicitly excludes tasks that primarily test knowledge retrieval, factual recall, or domain-specific expertise. The filtering process identifies tasks where reasoning ability is the bottleneck, not training data coverage. This is achieved by selecting tasks where model performance correlates with reasoning capability rather than knowledge base size, ensuring the benchmark isolates reasoning from memorization.
Unique: Explicitly filters out knowledge-retrieval tasks rather than treating all BIG-Bench tasks equally. This design choice prioritizes reasoning capability assessment over knowledge coverage, creating a reasoning-specific benchmark.
vs alternatives: More focused on reasoning than generic benchmarks because it removes knowledge-based tasks that would inflate scores for models with larger training corpora, enabling fairer comparison of reasoning ability
Provides all 23 tasks in a consistent JSON format with structured fields: task description, few-shot examples, test instances, expected outputs, and evaluation metrics. This standardization enables programmatic task loading, automated evaluation pipelines, and consistent metric computation across all tasks. The structured format reduces parsing overhead and enables batch evaluation of multiple models against the same task instances.
Unique: Uses a consistent JSON schema across all 23 tasks rather than task-specific formats or free-form descriptions. This enables programmatic evaluation without custom parsing logic per task.
vs alternatives: More automation-friendly than unstructured benchmarks because standardized JSON format enables batch evaluation pipelines, reducing manual effort and improving reproducibility
Distributes the benchmark as a Hugging Face Dataset, enabling seamless integration with the HF ecosystem (transformers, datasets, evaluate libraries). The dataset is versioned, cached locally after first download, and supports streaming for large-scale evaluation. Integration with HF enables one-line loading in Python and automatic compatibility with HF evaluation frameworks, reducing setup friction for researchers.
Unique: Leverages Hugging Face Dataset infrastructure for distribution and versioning rather than hosting tasks on a custom server. This provides automatic caching, versioning, and ecosystem integration without custom infrastructure.
vs alternatives: More accessible than custom-hosted benchmarks because HF integration enables one-line loading and automatic compatibility with popular evaluation tools, reducing setup friction
Provides multiple test instances per task (typically 10-100 examples) rather than single-instance evaluation. This enables statistical significance testing and variance analysis across instances, reducing noise from individual task variations. Batch evaluation allows researchers to compute confidence intervals on model performance and detect whether improvements are statistically significant or within noise margins.
Unique: Provides multiple test instances per task rather than single-instance evaluation, enabling statistical analysis of performance variance. This design choice prioritizes statistical rigor over evaluation efficiency.
vs alternatives: More statistically rigorous than single-instance benchmarks because multiple instances enable confidence interval computation and significance testing, reducing noise from task-specific variations
+1 more capabilities
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 BIG-Bench Hard (BBH) at 46/100. BIG-Bench Hard (BBH) 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.
+7 more capabilities