CodeContests
DatasetFree13K competitive programming problems from AlphaCode research.
Capabilities8 decomposed
competitive-programming-problem-corpus-with-multi-language-solutions
Medium confidenceProvides 13,328 curated competitive programming problems sourced from Codeforces, AtCoder, and other platforms, each with complete problem statements, reference solutions in multiple programming languages (C++, Python, Java, etc.), and comprehensive test case suites. The dataset is structured with metadata including problem difficulty calibration (median and 95th percentile solution metrics) and both public and hidden test cases, enabling direct evaluation of code generation models against real-world algorithmic challenges without synthetic problem generation.
Curated from real competitive programming platforms (Codeforces, AtCoder) with difficulty calibration via median/95th percentile metrics, rather than synthetic or classroom problems. Includes both public and hidden test cases enabling true generalization evaluation, and was specifically constructed to train AlphaCode, making it the largest real-world algorithmic problem corpus for code generation.
Larger and more algorithmically rigorous than HumanEval or MBPP (which focus on simple utility functions), and more representative of real problem-solving than synthetic benchmarks, while providing standardized difficulty stratification absent from raw Codeforces dumps.
multi-language-reference-solution-extraction
Medium confidenceExtracts and normalizes reference solutions across multiple programming languages (C++, Python, Java, JavaScript, Go, Rust, etc.) for each problem, with language-agnostic problem metadata and test case specifications. Solutions are parsed and validated against test cases to ensure correctness, enabling cross-language comparison of algorithmic approaches and language-specific implementation patterns for the same problem.
Provides solutions in 5+ languages per problem with validation against identical test case suites, enabling direct cross-language comparison. Most code datasets focus on a single language; this enables training models to understand language-agnostic algorithmic reasoning.
Richer than language-specific datasets (e.g., CodeSearchNet for Python only) because it forces models to learn language-independent problem decomposition, and more realistic than synthetic multilingual datasets because solutions come from real competitive programmers.
public-and-hidden-test-case-stratification
Medium confidenceSeparates test cases into public (visible in problem statement) and hidden (used for final evaluation) categories, enabling evaluation of model generalization beyond memorization of example inputs/outputs. Hidden test cases are designed by problem setters to cover edge cases, boundary conditions, and adversarial inputs that public examples may not expose, allowing measurement of true algorithmic correctness vs. overfitting to visible examples.
Explicitly separates public and hidden test cases with both included in the dataset, enabling researchers to measure generalization gap between public example performance and true correctness. Most benchmarks (HumanEval, MBPP) use only public test cases; this enables evaluation methodology matching real competitive programming.
More rigorous than single-test-set benchmarks because it prevents overfitting to visible examples and forces models to learn generalizable algorithmic patterns, matching how competitive programming platforms actually evaluate submissions.
difficulty-calibrated-problem-stratification
Medium confidenceStratifies problems by difficulty using median and 95th percentile solution runtime metrics from real competitive programmers, enabling selection of problems at specific difficulty levels for targeted training or evaluation. Problems are tagged with difficulty ranges (easy, medium, hard, expert) derived from actual submission statistics rather than subjective classification, allowing researchers to study how model performance scales with problem complexity.
Uses empirical runtime metrics (median and 95th percentile from real submissions) to calibrate difficulty rather than subjective classification or problem setter ratings. This grounds difficulty in measurable performance data and enables reproducible difficulty-based dataset splits.
More objective than subjective difficulty labels (e.g., 'hard' vs 'medium') and more granular than binary easy/hard splits, enabling fine-grained curriculum learning studies that other datasets don't support.
problem-statement-parsing-and-normalization
Medium confidenceExtracts and normalizes problem statements from multiple competitive programming platforms (Codeforces, AtCoder, etc.) into a unified format, including problem description, input/output specifications, constraints, and example inputs/outputs. Handles platform-specific formatting (HTML, Markdown, LaTeX mathematical notation) and converts to consistent structured representation, enabling uniform processing across problems from different sources.
Normalizes problem statements from multiple competitive programming platforms (Codeforces, AtCoder, etc.) into a unified structured format, handling platform-specific HTML/Markdown formatting and mathematical notation. Most datasets use problems from a single platform; this enables cross-platform aggregation.
More comprehensive than platform-specific datasets because it handles heterogeneous problem statement formats and enables unified processing, while providing more structured problem representation than raw problem text dumps.
test-case-execution-and-validation-framework
Medium confidenceProvides infrastructure for executing generated code against test cases with resource limits (timeout, memory), capturing execution results (pass/fail, runtime, memory usage), and validating output correctness. Supports multiple programming languages and handles I/O redirection, standard output comparison, and floating-point tolerance for numerical problems, enabling automated evaluation of code generation model outputs.
Provides test case execution framework supporting multiple languages with resource limits and structured result capture, enabling safe evaluation of generated code. The dataset includes test case infrastructure designed for AlphaCode evaluation, not just problem data.
More complete than raw test case files because it includes execution framework and resource limit handling, enabling end-to-end evaluation without requiring researchers to build custom test runners.
source-platform-and-problem-metadata-tracking
Medium confidenceMaintains metadata for each problem including source platform (Codeforces, AtCoder, etc.), problem ID, submission date, problem tags (algorithm type, data structure, etc.), and contest context. This enables filtering and analysis by platform, time period, or problem category, and allows tracing problems back to original sources for additional context or updates.
Preserves source platform and problem metadata (Codeforces problem ID, AtCoder contest, submission date, problem tags) enabling filtering by platform, time period, and algorithmic category. Most aggregated datasets lose this metadata; preserving it enables platform-specific and temporal analysis.
More useful for analysis and filtering than datasets that strip metadata, and enables reproducibility by allowing problems to be traced back to original sources.
large-scale-algorithmic-problem-distribution-analysis
Medium confidenceEnables statistical analysis of the 13,328-problem corpus to understand problem distribution across algorithmic categories, difficulty levels, languages, and platforms. Provides aggregate statistics (e.g., percentage of problems requiring dynamic programming, distribution of problem difficulty, language coverage per problem) enabling researchers to characterize the dataset and identify coverage gaps.
Provides large-scale corpus of 13,328 problems enabling statistical analysis of problem distribution across algorithms, difficulty, and platforms. Most datasets are smaller or don't provide distribution analysis; this scale enables robust statistical characterization.
Larger and more diverse than smaller benchmarks (HumanEval: 164 problems, MBPP: 974 problems), enabling more robust statistical analysis and better representation of real problem diversity.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓ML researchers training or evaluating code generation models (e.g., AlphaCode-style systems)
- ✓Teams building competitive programming assistants or AI tutoring systems
- ✓Researchers studying algorithmic reasoning in large language models
- ✓Organizations benchmarking code LLMs on standardized, difficulty-stratified problems
- ✓Multilingual code generation model developers training on language-diverse datasets
- ✓Researchers studying how algorithmic complexity translates across programming languages
- ✓Teams building polyglot code generation systems or language-agnostic code synthesis
- ✓Researchers evaluating code generation model generalization and robustness
Known Limitations
- ⚠Problems are primarily algorithmic/mathematical in nature — limited coverage of systems programming, web development, or domain-specific code
- ⚠Solutions are reference implementations only; no coverage of alternative approaches or trade-offs for the same problem
- ⚠Dataset is static and does not update with new competitive programming problems or platforms
- ⚠Test cases are deterministic and may not cover edge cases or adversarial inputs beyond original problem setters' intent
- ⚠Language coverage varies — not all problems have solutions in all major languages
- ⚠Not all problems have solutions in all languages — language coverage varies per problem
Requirements
Input / Output
UnfragileRank
UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.
About
Google DeepMind's dataset of competitive programming problems used to train and evaluate AlphaCode. Contains 13,328 problems from Codeforces, AtCoder, and other competitive programming platforms with full problem statements, solutions in multiple languages, and extensive test cases (both public and hidden). Problems range from easy to extremely hard, requiring advanced algorithmic knowledge. Each problem includes median and 95th percentile correct solutions for calibrating difficulty.
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