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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Evaluates multimodal agents' ability to interact with actual operating system graphical interfaces across Ubuntu, Windows, and macOS by executing tasks that require screenshot understanding, mouse/keyboard simulation, and application navigation. Uses custom execution-based evaluation scripts per task that capture initial OS state, execute agent actions, and verify task completion against ground truth outcomes in real sandboxed environments.
Unique: Executes tasks on actual operating systems (Ubuntu, Windows, macOS) with custom per-task evaluation scripts rather than simulated environments or synthetic UI frameworks. Grounds agent evaluation in real application behavior, file I/O, and OS-level state changes, capturing the complexity of multi-app workflows and GUI grounding that synthetic benchmarks cannot replicate.
vs alternatives: More realistic than simulated GUI benchmarks (e.g., WebShop, MiniWoB) because it tests against actual OS behavior and real applications, but requires significantly more computational infrastructure than synthetic alternatives, making it less accessible for individual researchers.
Distributes 369 benchmark tasks across three operating systems (Ubuntu, Windows, macOS) with OS-specific initial state configurations and evaluation scripts. Each task includes a detailed setup configuration that establishes the OS environment, file structures, and application states before agent execution, enabling reproducible evaluation of agent performance across platform-specific UI paradigms and application ecosystems.
Unique: Includes OS-specific initial state setup configurations and custom evaluation scripts per task, rather than a single generic task definition. This approach captures OS-level differences in file systems, UI paradigms, and application ecosystems, but requires maintaining three parallel task implementations and evaluation harnesses.
vs alternatives: More comprehensive than single-OS benchmarks because it tests cross-platform generalization, but significantly increases benchmark maintenance burden and infrastructure requirements compared to OS-agnostic synthetic benchmarks.
Evaluates agent capability to understand and interact with graphical user interfaces by analyzing screenshots and identifying UI elements, buttons, menus, and text fields. Tests agent ability to visually ground task instructions in the actual UI state, a capability identified as a key limitation in current agents.
Unique: Explicitly evaluates GUI grounding and visual understanding as a core agent capability, identifying it as a key limitation in current agents. This focuses evaluation on a specific bottleneck rather than treating visual understanding as a solved problem.
vs alternatives: More targeted than generic multimodal benchmarks because it focuses on GUI understanding as a specific capability, but may not capture other important agent limitations like operational knowledge or task planning.
Evaluates agent capability to understand how to use applications and perform operations within them, testing knowledge of application-specific workflows, menu structures, keyboard shortcuts, and domain-specific operations. Identified as a key limitation in current agents alongside GUI grounding.
Unique: Explicitly evaluates operational knowledge and application expertise as a core agent capability, identifying it as a key limitation in current agents. This tests agent capability to understand how to use applications, not just how to interact with GUIs.
vs alternatives: More comprehensive than GUI-only benchmarks because it tests both visual understanding and operational knowledge, but harder to diagnose which capability is limiting agent performance.
Implements task-specific evaluation scripts that execute agent actions against real OS state and verify completion by checking file system changes, application state modifications, and other observable outcomes. Each of the 369 tasks includes a custom evaluation script that defines success criteria, captures execution traces, and produces reproducible verdicts independent of agent architecture or implementation details.
Unique: Uses custom per-task evaluation scripts rather than generic scoring functions, enabling task-specific success criteria that capture domain knowledge (e.g., correct file format, application-specific state changes). This approach is more accurate than generic metrics but requires significant engineering effort and domain expertise per task.
vs alternatives: More accurate than generic scoring functions for complex, multi-step tasks, but less scalable and harder to maintain than standardized evaluation metrics used in simpler benchmarks.
Grounds benchmark tasks in real-world computer use cases derived from actual user workflows, file management operations, application usage patterns, and multi-app interactions. Tasks are not synthetic or artificially constructed but represent genuine computer tasks that users perform, including file organization, document editing, web browsing, email management, and cross-application data workflows.
Unique: Tasks are derived from real-world computer use cases rather than synthetic or artificially constructed scenarios, aiming to evaluate agent capability on tasks that users actually perform. This grounds evaluation in practical utility but introduces data contamination risks and makes it harder to control task difficulty and distribution.
vs alternatives: More practically relevant than synthetic benchmarks (e.g., WebShop, MiniWoB) because tasks represent actual user workflows, but less controlled and harder to validate than carefully constructed synthetic tasks with known difficulty and no training data overlap.
Provides standardized evaluation infrastructure for measuring multimodal agent performance (combining vision and language understanding) on computer task completion. Establishes baseline human performance (72.36% success rate) and current state-of-the-art model performance (12.24% success rate), quantifying the gap between human and AI agent capability on real OS tasks.
Unique: Establishes quantified baseline performance (human 72.36% vs SOTA 12.24%) on real OS tasks, creating a measurable target for agent improvement. The large gap indicates substantial room for progress and highlights specific capability gaps (GUI grounding, operational knowledge) that agents need to address.
vs alternatives: More realistic performance measurement than synthetic benchmarks because it uses real OS environments and real-world tasks, but the 60+ percentage point gap between human and SOTA performance suggests the benchmark may be too difficult to provide useful signal for incremental improvements.
Provides a web-based interactive viewer for exploring benchmark tasks, initial states, expected outcomes, and evaluation results. Enables researchers and developers to inspect individual tasks, understand evaluation criteria, and analyze agent performance without requiring local execution of the full benchmark infrastructure.
Unique: Provides interactive web-based exploration of benchmark tasks and results rather than requiring local data access or command-line tools. Lowers barrier to entry for researchers who want to understand benchmark tasks without setting up evaluation infrastructure.
vs alternatives: More accessible than command-line or programmatic data access, but potentially less powerful for bulk analysis or custom queries compared to direct data access.
+4 more capabilities
Provides a standardized evaluation framework for code generation models that accepts generated code in 17 programming languages (C, C++, C#, Java, Kotlin, Go, Rust, Python, Ruby, PHP, JavaScript, Perl, Haskell, OCaml, Scala, D, Pascal) and validates correctness through actual execution against unit tests via the ExecEval Docker-based execution engine. Uses a centralized problem definition model with src_uid foreign keys linking generated code to shared problem descriptions and unittest_db.json, enabling consistent evaluation across language variants of the same problem.
Unique: Combines 25M training examples across 7,500 unique problems with an execution-based evaluation pipeline (ExecEval) that actually runs generated code in Docker containers against unit tests, rather than relying on static analysis or string matching. The src_uid linking system creates a normalized data model where problem descriptions and tests are stored once and referenced by all language variants, eliminating duplication and ensuring consistency.
vs alternatives: Larger scale (25M examples vs typical 10-100K) and true execution-based validation across more languages (17 vs 4-6) than HumanEval or CodeXGLUE, with explicit support for code translation and repair tasks beyond generation.
Implements a foreign key linking system where all task-specific datasets (program synthesis, code translation, APR, retrieval) reference shared problem definitions via src_uid identifiers. Problem descriptions and unit tests are stored once in centralized problem_descriptions.jsonl and unittest_db.json files, then linked by src_uid to avoid duplication. The Hugging Face datasets API automatically resolves these links during data loading, returning enriched DatasetDict objects with problem context pre-joined to task examples.
Unique: Uses a normalized relational data model (src_uid as foreign key) for a code benchmark, treating problem definitions as a separate entity layer rather than embedding them in each task dataset. This is more sophisticated than typical flat-file benchmark structures and enables consistent multi-task evaluation on identical problems.
xCodeEval scores higher at 67/100 vs OSWorld at 64/100.
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vs alternatives: More efficient than duplicating problem descriptions across 7 task datasets (reduces storage by ~30-40%), and enables automatic link resolution via Hugging Face API unlike manual CSV joins in CodeXGLUE or HumanEval variants.
Provides a Python API for loading xCodeEval datasets from Hugging Face Hub (NTU-NLP-sg/xCodeEval) with automatic src_uid-based linking between task datasets and shared problem definitions. The datasets library handles data downloading, caching, and streaming, while the xCodeEval integration automatically joins task examples with problem_descriptions.jsonl and unittest_db.json using src_uid foreign keys. Returns DatasetDict objects with enriched examples ready for model training or evaluation.
Unique: Integrates xCodeEval with Hugging Face datasets library, providing automatic src_uid resolution and streaming support. Treats data loading as a first-class concern with built-in linking logic, rather than requiring manual JSON parsing.
vs alternatives: More convenient than manual Git LFS downloads because it handles caching and automatic linking, and integrates seamlessly with Hugging Face training pipelines vs custom data loaders.
Provides an alternative data access method using Git LFS for users who prefer direct file access or need selective dataset downloads. Supports cloning the repository with LFS disabled, then pulling specific task files or problem definitions on demand. Useful for custom processing pipelines or environments where Python/Hugging Face is not available, though requires manual src_uid linking to join task examples with problem definitions.
Unique: Provides Git LFS-based alternative to Hugging Face API, enabling direct file access and selective downloads. Requires manual src_uid linking but offers more control over data access patterns.
vs alternatives: More flexible than Hugging Face API for selective downloads and custom pipelines, but requires more manual work for src_uid linking and lacks automatic caching/streaming.
Implements a standardized three-phase evaluation pipeline (Phase 1: Generation, Phase 2: Execution, Phase 3: Metrics) that applies consistently across all 7 tasks (program synthesis, code translation, APR, tag classification, code compilation, NL-code retrieval, code-code retrieval). Phase 1 generates or retrieves code, Phase 2 executes it via ExecEval or computes retrieval metrics, and Phase 3 aggregates results into pass@k, MRR, NDCG, or other task-specific metrics. Enables direct comparison of model performance across tasks.
Unique: Defines a unified three-phase evaluation pipeline that applies to all 7 tasks, treating generation, execution, and metric computation as separate concerns. Enables consistent evaluation methodology across diverse task types (generation, translation, retrieval, classification).
vs alternatives: More comprehensive than task-specific evaluation scripts because it provides a unified framework for all 7 tasks, and enables direct comparison of model performance across different task types.
Evaluates code generation models on the program synthesis task by accepting natural language problem descriptions and generating code solutions in any of 17 languages. The evaluation pipeline (Phase 1: Generation, Phase 2: Execution, Phase 3: Metrics) runs generated code against unit tests via ExecEval, computing pass@k metrics (pass@1, pass@10, etc.) that measure the probability of finding a correct solution within k samples. Supports both single-solution and multi-sample evaluation modes for assessing model reliability.
Unique: Implements a three-phase evaluation pipeline (Generation → Execution → Metrics) with explicit pass@k computation that measures the probability of finding a correct solution within k attempts, rather than just binary pass/fail. Supports multi-sample evaluation across 17 languages with language-specific compiler configurations and timeout handling.
vs alternatives: More rigorous than HumanEval's simple pass@k because it handles language-specific compilation errors and timeouts explicitly, and scales to 25M training examples vs HumanEval's 164 problems.
Evaluates code translation models by accepting source code in one language and generated translations in a target language, then validating functional equivalence through execution against shared unit tests. The translation evaluation pipeline compiles and executes both source and translated code against the same unittest_db.json test cases, comparing outputs to detect translation errors. Supports all 17 language pairs (though not all pairs may have training data) and uses language-specific compiler mappings to handle syntax differences.
Unique: Validates code translation by executing both source and target code against identical unit tests and comparing outputs, ensuring functional equivalence rather than syntactic similarity. Uses language-specific compiler mappings to handle the complexity of 17 different compilation environments and their idiosyncrasies.
vs alternatives: More rigorous than BLEU-score-based translation metrics because it validates actual functional correctness through execution, and covers more language pairs (17 vs typical 2-4) with explicit compiler integration.
Evaluates program repair models by providing buggy code snippets and expecting corrected versions that pass unit tests. The APR evaluation pipeline executes repaired code against unittest_db.json test cases, measuring whether the repair successfully fixes the bug without introducing new failures. Supports repairs across all 17 languages and uses the same execution-based validation as program synthesis, enabling direct comparison of repair quality.
Unique: Treats program repair as an executable task where success is measured by unit test passage, rather than syntactic similarity to reference repairs. Integrates with the same ExecEval pipeline as program synthesis, enabling direct performance comparison between generation and repair models.
vs alternatives: More comprehensive than traditional APR benchmarks (Defects4J, QuixBugs) because it covers 17 languages and 7,500 problems vs 395 Java bugs, and uses consistent execution-based metrics across all repair types.
+5 more capabilities