BIG-Bench Hard (BBH) vs cua
Side-by-side comparison to help you choose.
| Feature | BIG-Bench Hard (BBH) | cua |
|---|---|---|
| Type | Dataset | Agent |
| UnfragileRank | 45/100 | 50/100 |
| Adoption | 1 | 1 |
| Quality | 0 | 1 |
| Ecosystem |
| 0 |
| 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 15 decomposed |
| Times Matched | 0 | 0 |
Provides curated few-shot chain-of-thought (CoT) exemplars for 23 hard reasoning tasks, enabling models to learn structured step-by-step problem decomposition through in-context learning. Each task includes 3-5 hand-crafted examples showing intermediate reasoning steps, allowing models to adopt explicit reasoning patterns without fine-tuning. The dataset leverages prompt engineering patterns where models observe reasoning trajectories before solving novel instances.
Unique: Curated subset specifically filtered to tasks where models initially underperformed humans (below 50th percentile), creating a hard-mode benchmark rather than a balanced difficulty distribution. This selection strategy focuses evaluation on frontier model improvements rather than general capability assessment.
vs alternatives: Harder and more reasoning-focused than general benchmarks like MMLU or HellaSwag; includes explicit CoT examples unlike raw BIG-Bench, making it more suitable for prompt engineering evaluation than raw task suites.
Organizes 23 tasks across distinct reasoning domains (algorithmic, arithmetic, logical, causal, spatial) with consistent evaluation structure, enabling fine-grained analysis of model strengths and weaknesses by reasoning type. Each task is independently evaluable with its own test set and metrics, allowing researchers to identify which reasoning modalities their models excel or fail at. The stratification enables targeted model development and capability analysis.
Unique: Explicitly stratifies tasks by reasoning modality (algorithmic, arithmetic, logical, causal, spatial) rather than treating all hard tasks as monolithic, enabling domain-specific capability assessment. This structure allows researchers to correlate model architecture choices with specific reasoning strengths.
vs alternatives: More analytically useful than generic hard task collections because stratification enables root-cause analysis of reasoning failures; more focused than full BIG-Bench which lacks explicit domain organization.
Designed specifically to evaluate frontier language models (GPT-4, Claude, Llama 2+, etc.) on hard reasoning tasks where initial model performance was below human level, enabling measurement of model improvement over time and comparison of frontier model capabilities. The dataset enables researchers to track whether new model releases improve on hard reasoning and to identify reasoning capabilities that remain unsolved. Results are directly comparable across models because of standardized evaluation infrastructure.
Unique: Explicitly designed for frontier model evaluation by selecting tasks where initial models underperformed humans, creating a benchmark that remains challenging as models improve. This selection strategy ensures the benchmark is useful for measuring frontier model progress rather than becoming trivial.
vs alternatives: More suitable for frontier model evaluation than general benchmarks because it focuses on hard reasoning tasks; more challenging than benchmarks where models already exceed human performance, which may not drive model improvement.
Enables reproducible evaluation across different models and research groups by providing standardized task definitions, test sets, evaluation metrics, and result aggregation. The dataset structure ensures that different teams can run identical evaluations and compare results directly, reducing evaluation variance and enabling fair model comparison. Standardized evaluation infrastructure supports publishing reproducible results and enables meta-analysis across multiple model evaluations.
Unique: Provides standardized evaluation infrastructure that enables reproducible results across different models and research groups, reducing evaluation variance and enabling fair model comparison. The dataset structure enforces consistent task definitions and metrics.
vs alternatives: More reproducible than ad-hoc evaluation because it enforces standardized task definitions and metrics; more comparable than benchmarks without standardized infrastructure because it enables direct result comparison across models.
Includes human rater performance data for all 23 tasks, establishing ground-truth difficulty calibration and enabling measurement of model-vs-human performance gaps. Tasks were specifically selected where initial model performance fell below human median (50th percentile), creating a calibrated hard benchmark. Human baselines enable researchers to quantify progress toward human-level reasoning and identify tasks where models have surpassed human performance.
Unique: Explicitly selected tasks where models underperformed humans at time of curation, creating a self-calibrated hard benchmark where human performance is the reference point rather than an afterthought. This selection strategy ensures the benchmark remains challenging as models improve.
vs alternatives: More rigorous than benchmarks without human baselines because it enables quantitative model-vs-human comparison; more meaningful than benchmarks where humans outperform models by large margins, which may indicate task misalignment rather than genuine reasoning difficulty.
Provides consistent evaluation infrastructure across 23 heterogeneous reasoning tasks with unified input/output schemas, metrics computation, and result aggregation. Each task includes standardized test sets, answer formats, and evaluation functions, enabling researchers to run comprehensive benchmarks with a single evaluation script. The harness abstracts task-specific complexity and enables reproducible, comparable results across models and research groups.
Unique: Provides unified evaluation infrastructure across heterogeneous task types (arithmetic, logic, spatial, causal) with consistent metrics and result aggregation, rather than requiring task-specific evaluation code. This standardization enables reproducible cross-model comparison and reduces evaluation implementation burden.
vs alternatives: More reproducible than ad-hoc evaluation because it enforces consistent metrics and input/output handling; more comprehensive than single-task benchmarks because it enables multi-domain capability assessment in one evaluation run.
Includes algorithmic reasoning tasks (e.g., sorting, graph traversal, dynamic programming) that test whether models can learn and apply computational algorithms through few-shot examples. Tasks present problem descriptions and expect models to reason through algorithmic steps, testing whether models can generalize algorithmic patterns beyond memorized examples. This capability isolates algorithmic reasoning from knowledge retrieval or common-sense reasoning.
Unique: Isolates algorithmic reasoning as a distinct capability by presenting algorithm problems in natural language with few-shot examples, testing whether models can learn algorithmic patterns without explicit training. This approach measures algorithmic reasoning generalization rather than memorization.
vs alternatives: More focused on algorithmic reasoning than general reasoning benchmarks; more accessible than formal algorithm verification tasks because it uses natural language rather than pseudocode or formal logic.
Includes multi-step arithmetic and mathematical reasoning tasks (e.g., word problems, numerical reasoning, mathematical deduction) that test whether models can perform accurate calculations and apply mathematical reasoning through few-shot examples. Tasks range from basic arithmetic to more complex mathematical inference, isolating numerical reasoning from language understanding. Evaluation measures both intermediate calculation accuracy and final answer correctness.
Unique: Focuses specifically on multi-step arithmetic and mathematical reasoning through few-shot examples, isolating numerical reasoning capability from general language understanding. Tasks test both calculation accuracy and mathematical inference patterns.
vs alternatives: More focused on mathematical reasoning than general reasoning benchmarks; more accessible than formal mathematics verification because it uses natural language problem statements rather than symbolic notation.
+4 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 50/100 vs BIG-Bench Hard (BBH) at 45/100. 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