Hand Crafted Programming Problem Dataset With Canonical Solutions

via “competition-mathematics problem dataset loading with multi-subject stratification”

12.5K competition math problems — AMC/AIME/Olympiad level, 7 subjects, standard math benchmark.

Unique: Curates problems exclusively from high-difficulty mathematical competitions (AMC, AIME, Olympiads) rather than generic math word problems, ensuring evaluation on reasoning-intensive problems that require multi-step derivations and deep mathematical understanding. The MATHDataset class implements subject-aware stratification enabling fine-grained evaluation across mathematical domains.

vs others: More rigorous than generic math QA datasets (e.g., MathQA, SVAMP) because problems require genuine mathematical reasoning rather than simple arithmetic, making it the de facto standard for evaluating LLM mathematical capabilities in research.

via “hand-crafted programming problem dataset with canonical solutions”

OpenAI's code generation benchmark — 164 Python problems with unit tests, pass@k evaluation.

Unique: Hand-crafted by OpenAI with deliberate problem diversity covering algorithms, data structures, and edge cases; each problem includes a canonical solution and comprehensive test suite designed to catch subtle correctness issues rather than surface-level syntax errors

vs others: More rigorous and widely-adopted than crowdsourced alternatives because problems were vetted by domain experts and test cases are designed to catch functional bugs, not just runtime errors

via “competitive-programming-problem-corpus-with-multi-language-solutions”

13K competitive programming problems from AlphaCode research.

Unique: 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.

vs others: 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.

via “benchmark dataset for basic python programming problems”

974 basic Python problems complementing HumanEval for code evaluation.

Unique: This dataset focuses on basic programming proficiency rather than complex problem-solving, providing a unique resource for foundational skill evaluation.

vs others: Unlike other datasets that emphasize complexity, MBPP offers a targeted approach to assess basic Python skills effectively.

via “realistic data science coding problem benchmark”

1,000 data science problems across 7 Python libraries.

Unique: This dataset uniquely focuses on realistic coding problems rather than abstract algorithmic challenges, providing practical context for learners.

vs others: Unlike other datasets that may focus on theoretical problems, DS-1000 emphasizes real-world applications and library-specific tasks.

via “multi-source coding problem aggregation with standardized test harnesses”

10K coding problems across 3 difficulty levels with test suites.

Unique: Combines problems from four independent online judge platforms with heterogeneous formats into a single normalized schema with consistent test execution semantics, rather than using a single-source benchmark like HumanEval or MBPP

vs others: 10x larger problem set than HumanEval (10K vs 164 problems) with higher algorithmic complexity and real-world difficulty distribution, making it more representative of production code generation challenges