What can MBPP (Mostly Basic Python Problems) do?

python code generation benchmark evaluation, multi-problem code correctness validation, problem categorization and concept mapping, reference solution and test case repository, pass@k metric computation and aggregation, cross-model performance comparison and ranking, problem difficulty and concept coverage analysis, reference solution and test case provision

MBPP (Mostly Basic Python Problems)

DatasetFree

974 basic Python problems complementing HumanEval for code evaluation.

Open Source

/ 100

8 capabilities

Capabilities8 decomposed

python code generation benchmark evaluation

Medium confidence

Provides a standardized dataset of 974 Python programming problems with reference solutions and test cases to measure code generation model accuracy. Each problem includes a natural language task description, a correct implementation function, and three validation test cases that verify functional correctness. Models generate code solutions which are executed against these test cases to compute pass@k metrics (percentage of problems solved within k attempts).

Solves for

Evaluate code generation models on basic programming proficiency tasksCompare model performance across different architectures and training approachesMeasure improvement in code generation capabilities over timeBenchmark LLM code generation on problems requiring string, list, and mathematical operations

Best for

ML researchers evaluating code generation models

Teams building and fine-tuning code LLMs

Organizations comparing commercial vs open-source code models

Requires

Python 3.6+ runtime for executing generated code and test cases

Hugging Face datasets library for loading the benchmark

Code generation model with Python output capability

Limitations

Limited to basic Python problems — does not test advanced concepts like async/await, decorators, metaclasses, or complex OOP patterns

Only 974 problems total — relatively small dataset compared to modern code corpora, may not capture long-tail programming patterns

Test cases are minimal (3 per problem) — may not catch edge cases or robustness issues in generated code

What makes it unique

Curated by Google Research specifically to complement HumanEval by focusing on breadth of basic programming concepts (string manipulation, list operations, mathematical functions, data structures) rather than algorithmic complexity, with human-verified reference solutions and minimal but sufficient test cases per problem

vs alternatives

Broader coverage of basic programming patterns than HumanEval's focus on algorithmic problems, making it better for evaluating practical coding proficiency; smaller and more focused than massive code corpora, enabling faster iteration and clearer signal on fundamental capabilities

multi-problem code correctness validation

Medium confidence

Executes generated Python code against a suite of predefined test cases to determine functional correctness at scale. The validation system runs each generated solution through 3 test cases per problem, capturing execution results, exceptions, and output matching. Supports batch evaluation of multiple model outputs across all 974 problems with aggregation of pass rates and failure analysis.

Solves for

Validate that generated code produces correct outputs for given inputsIdentify which problem categories or types a model struggles withDetect runtime errors, exceptions, and incorrect logic in generated solutionsAggregate correctness metrics across large batches of generated code

Best for

Automated evaluation pipelines for code generation models

Continuous integration systems testing model checkpoints

Researchers analyzing failure modes and error patterns

Requires

Python 3.6+ with ability to execute arbitrary code

Sandboxing mechanism (Docker, subprocess isolation, or similar) for safe code execution

Test harness to parse problem definitions and execute generated code

Limitations

Test cases only verify functional correctness — do not evaluate code efficiency, memory usage, or algorithmic complexity

Execution-based validation requires sandboxing to prevent malicious code — standard approach adds latency and resource overhead

Cannot detect subtle bugs that don't manifest in the 3 provided test cases per problem

What makes it unique

Provides a standardized, reproducible validation harness with 3 test cases per problem that can be applied uniformly across different code generation models, enabling fair comparison; includes reference implementations that serve as ground truth for correctness checking

vs alternatives

More reliable than manual code review for large-scale evaluation; faster than human testing while maintaining sufficient coverage for basic programming problems; standardized test cases ensure consistent evaluation across different models and research groups

problem categorization and concept mapping

Medium confidence

Organizes 974 problems into categories based on programming concepts tested: string manipulation, list operations, mathematical functions, and data structure algorithms. Each problem is tagged with the primary concepts it exercises, enabling filtered evaluation and analysis by concept area. This categorization allows researchers to understand model performance on specific programming domains and identify capability gaps.

Solves for

Analyze model performance broken down by programming concept or problem categoryIdentify which programming concepts a model struggles with mostSelect subsets of problems for targeted evaluation of specific capabilitiesUnderstand the breadth of programming knowledge a model has acquired

Best for

Researchers analyzing model capabilities across programming domains

Teams evaluating whether models have learned specific programming patterns

Educators using the dataset to understand what concepts models understand

Requires

Access to problem metadata with concept tags

Ability to filter and group problems by category

Evaluation results mapped back to problem categories

Limitations

Categorization is coarse-grained — many problems span multiple concepts but are tagged with only primary concept

No hierarchical taxonomy — cannot distinguish between basic and advanced versions of same concept

Categories are fixed and predefined — cannot dynamically group problems by custom criteria

What makes it unique

Curated categorization by Google Research based on fundamental programming concepts (string, list, math, data structures) rather than algorithmic complexity or problem domain, providing a practical lens for understanding basic coding proficiency across different skill areas

vs alternatives

More granular than treating all problems as a single pool; simpler and more interpretable than complexity-based rankings; directly maps to programming education curricula, making results actionable for model improvement

reference solution and test case repository

Medium confidence

Maintains a curated collection of 974 correct Python implementations paired with their corresponding test cases. Each problem includes a reference solution function that serves as ground truth for correctness evaluation, plus 3 test cases with inputs and expected outputs. This repository enables reproducible evaluation by providing a stable baseline that all generated code is compared against.

Solves for

Provide ground truth implementations for validating generated code correctnessEnable reproducible evaluation across different research groups and time periodsServe as examples of correct Python patterns for specific programming tasksSupport analysis of how generated code differs from reference implementations

Best for

Researchers needing a stable, reproducible evaluation baseline

Teams building code generation evaluation infrastructure

Organizations comparing results across different models and time periods

Requires

Access to the MBPP dataset with reference solutions and test cases

Python 3.6+ to execute reference solutions

Test harness to run reference solutions against test cases

Limitations

Reference solutions are single implementations — may not represent all valid approaches to a problem

Test cases are minimal (3 per problem) — reference solution may pass tests but not be optimal or idiomatic

No alternative correct solutions provided — generated code using different valid approaches may be marked incorrect

What makes it unique

Provides human-verified reference implementations curated by Google Research rather than automatically generated or crowd-sourced solutions, ensuring high quality and correctness; paired with minimal but sufficient test cases that validate the reference solution

vs alternatives

More reliable than crowd-sourced solutions (e.g., from Stack Overflow); more interpretable than learned baselines; enables reproducible evaluation because reference solutions are fixed and publicly available

pass@k metric computation and aggregation

Medium confidence

Computes pass@k metrics by sampling k generated solutions per problem and checking if at least one passes all test cases. Aggregates results across all 974 problems to produce overall pass@1, pass@10, pass@100 statistics. This metric accounts for the fact that code generation models can produce multiple valid solutions and benefits from sampling multiple attempts.

Solves for

Measure code generation model performance using standard industry metricsCompare models fairly by accounting for sampling varianceTrack improvement in model capabilities over training iterationsPublish reproducible results that can be compared across research papers

Best for

ML researchers publishing code generation benchmarks

Teams comparing different model architectures and training approaches

Organizations tracking model improvement over time

Requires

Code generation model capable of producing k samples per problem

Test harness to evaluate each sample against test cases

Aggregation logic to compute pass@k across all problems

Limitations

Pass@k assumes k independent samples — expensive to compute for large k values (k=100 requires 97,400 total generations)

Metric is binary (pass/fail) — does not distinguish between solutions that are close to correct vs completely wrong

Does not account for code quality, efficiency, or style — only functional correctness

What makes it unique

Implements the standard pass@k metric used across code generation research, enabling direct comparison with published results; accounts for sampling variance by checking if any of k attempts solves the problem, reflecting real-world usage where multiple attempts are feasible

vs alternatives

More realistic than pass@1 alone because it accounts for the fact that code generation models can produce multiple solutions; standardized metric enables comparison across papers and research groups; computationally tractable for k up to 100 on 974 problems

cross-model performance comparison and ranking

Medium confidence

Enables systematic comparison of different code generation models by running them all against the same 974 problems with identical test cases and evaluation criteria. Results are aggregated into leaderboard-style rankings showing pass@k metrics for each model. This standardized comparison framework allows researchers to objectively assess which models perform better on basic programming tasks.

Solves for

Compare performance of different code generation models objectivelyRank models by their ability to solve basic programming problemsIdentify which models have improved most over timeUnderstand relative strengths and weaknesses of different architectures

Best for

Researchers publishing model comparisons and benchmarks

Teams evaluating whether to adopt a new code generation model

Organizations tracking progress in code generation capabilities

Requires

Multiple code generation models to compare

Standardized evaluation harness that runs all models identically

Aggregation and ranking logic

Limitations

Comparison is limited to basic programming problems — does not reflect performance on complex, real-world code

Models may be optimized specifically for MBPP — results may not generalize to other code generation tasks

Comparison does not account for model size, latency, or resource requirements — only raw correctness

What makes it unique

Provides a standardized, reproducible framework for comparing code generation models using identical problems and test cases, enabling fair assessment across different architectures, training approaches, and organizations; results are publicly available and widely cited in research

vs alternatives

More objective than subjective code quality assessments; more standardized than ad-hoc comparisons using different test sets; enables tracking progress over time as models improve

problem difficulty and concept coverage analysis

Medium confidence

Analyzes the distribution of problem difficulty, concept coverage, and solution complexity across the 974 problems. Provides insights into what programming concepts are well-represented in the dataset and which are underrepresented. Enables researchers to understand the breadth and balance of the benchmark and identify potential gaps in coverage.

Solves for

Understand the scope and coverage of programming concepts in the benchmarkIdentify which programming areas are well-tested vs underrepresentedAssess whether the benchmark provides balanced coverage across conceptsDetermine if the benchmark is suitable for evaluating specific programming skills

Best for

Researchers designing new benchmarks or extending MBPP

Teams understanding what programming skills their models have learned

Educators using the dataset to understand concept coverage

Requires

Access to problem metadata with concept tags and difficulty ratings

Statistical analysis tools to compute coverage metrics

Visualization tools to display concept distribution

Limitations

Analysis is limited to metadata provided in the dataset — no automatic difficulty estimation

Concept coverage is based on manual tagging — may be incomplete or inconsistent

No analysis of problem interdependencies — some concepts build on others but are not explicitly linked

What makes it unique

Provides structured analysis of problem distribution across programming concepts, enabling researchers to understand the benchmark's scope and identify coverage gaps; curated by Google Research with explicit categorization of problems by concept type

vs alternatives

More transparent than treating the benchmark as a black box; enables targeted evaluation of specific programming skills; helps researchers understand whether MBPP is suitable for their evaluation needs

reference solution and test case provision

Medium confidence

Includes a correct reference implementation and three test cases for each of the 974 problems, enabling both positive and negative evaluation modes. The reference solutions are hand-written Python functions demonstrating the expected behavior, while test cases cover typical inputs, edge cases, and boundary conditions. This allows evaluation of generated code by comparing outputs to reference solutions or by running test cases directly, supporting both execution-based and semantic-based evaluation approaches.

Solves for

Validate generated code by executing it against reference test casesCompare generated code semantically to reference solutions for style/efficiency analysisUse reference solutions as few-shot examples in prompts to improve model performanceAnalyze how generated code differs from reference implementations (e.g., alternative algorithms)

Best for

Researchers evaluating code generation models on functional correctness

Teams using few-shot prompting to improve code generation quality

Organizations analyzing generated code for efficiency and style

Requires

Problem metadata with 'code' and 'test_list' fields

Python 3.7+ to execute reference solutions and test cases

Limitations

Reference solutions are single implementations — may not represent all correct approaches or optimal algorithms

Test cases are minimal (3 per problem) — may not cover all edge cases or corner cases

No test case difficulty or coverage metrics — unclear which tests are most important

What makes it unique

Provides three test cases per problem (vs. single test in some benchmarks) enabling detection of edge case failures, with hand-written reference solutions demonstrating correct implementations

vs alternatives

More comprehensive than benchmarks with single test cases, as multiple tests catch off-by-one errors and edge case failures that would pass with only one input

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

Artifacts that share capabilities with MBPP (Mostly Basic Python Problems), ranked by overlap. Discovered automatically through the match graph.

Benchmark63

HumanEval

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

hand-crafted programming problem dataset with canonical solutionsproblem-specific test case isolation and executionfunctional correctness testing via unit test execution

3 shared capabilities

Dataset60

APPS (Automated Programming Progress Standard)

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

multi-source coding problem aggregation with standardized test harnessesnatural language to code pipeline evaluation

2 shared capabilities

Benchmark65

LiveCodeBench

Continuously updated coding benchmark — new competitive programming problems, prevents contamination.

multi-scenario-code-capability-evaluationcode-execution-validation-with-test-case-matching

2 shared capabilities

Benchmark43

MBPP

Mostly Basic Programming Problems (beginner-friendly code)

python programming problem evaluation

1 shared capability

Benchmark47

HumanEval

OpenAI's standard for evaluating code generation models

unit test-driven code evaluation

1 shared capability

Model33

CodeGeeX

CodeGeeX: An Open Multilingual Code Generation Model (KDD 2023)

humaneval-x multilingual code generation benchmark with 820 problems

1 shared capability

Best For

✓ML researchers evaluating code generation models
✓Teams building and fine-tuning code LLMs
✓Organizations comparing commercial vs open-source code models
✓Researchers studying code generation on basic algorithmic problems
✓Automated evaluation pipelines for code generation models
✓Continuous integration systems testing model checkpoints
✓Researchers analyzing failure modes and error patterns
✓Teams establishing baseline performance metrics for code models

Known Limitations

⚠Limited to basic Python problems — does not test advanced concepts like async/await, decorators, metaclasses, or complex OOP patterns
⚠Only 974 problems total — relatively small dataset compared to modern code corpora, may not capture long-tail programming patterns
⚠Test cases are minimal (3 per problem) — may not catch edge cases or robustness issues in generated code
⚠No evaluation of code quality metrics like readability, efficiency, or style — only functional correctness
⚠Python-only — cannot evaluate code generation for other languages
⚠Test cases only verify functional correctness — do not evaluate code efficiency, memory usage, or algorithmic complexity

Requirements

Python 3.6+ runtime for executing generated code and test casesHugging Face datasets library for loading the benchmarkCode generation model with Python output capabilityTest harness to execute generated code and compare against expected outputsPython 3.6+ with ability to execute arbitrary codeSandboxing mechanism (Docker, subprocess isolation, or similar) for safe code executionTest harness to parse problem definitions and execute generated codeTimeout mechanism to prevent hanging on infinite loops

Input / Output

Accepts: natural language task descriptions, generated Python code strings, generated Python function code as strings, problem definitions with test case inputs and expected outputs, problem definitions with concept tags, problem descriptions and test case specifications, k generated code samples per problem, test case definitions and expected outputs, code generation model outputs for all 974 problems, test case definitions, problem definitions with concept tags and metadata, problem ID or description, generated code (as string)

Produces: pass/fail test results per problem, pass@k metrics (pass@1, pass@10, etc.), execution logs and error traces, boolean pass/fail per test case, execution errors and exception traces, actual vs expected output comparisons, aggregated pass rates and statistics, grouped evaluation results by concept, per-category pass rates and statistics, concept-specific failure analysis, Python function implementations, test case inputs and expected outputs, execution results from reference solutions, pass@1, pass@10, pass@100 metrics, per-problem pass rates, aggregated statistics and confidence intervals, ranked list of models by pass@k metrics, comparative performance tables, per-problem performance breakdowns by model, concept coverage statistics, difficulty distribution analysis, concept-specific problem counts, coverage gap identification, reference solution (Python function code), test cases (list of dicts with 'input' and 'output' keys), pass/fail results for each test case

UnfragileRank

Adoption70%(30% weight)

Quality85%(25% weight)

Ecosystem50%(10% weight)

Match Graph25%(30% weight)

Freshness100%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: Dataset

8 capabilities

Visit MBPP (Mostly Basic Python Problems)→

About

Google's benchmark of 974 Python programming problems designed to test basic programming proficiency. Each problem includes a task description, solution function, and three test cases. Covers common programming concepts: string manipulation, list operations, mathematical functions, and data structure algorithms. Complements HumanEval by testing breadth of basic coding knowledge rather than complexity. Widely used alongside HumanEval for holistic code generation evaluation.

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Capabilities8 decomposed

python code generation benchmark evaluation

Medium confidence

Solves for

Best for

ML researchers evaluating code generation models

Teams building and fine-tuning code LLMs

Organizations comparing commercial vs open-source code models

Requires

Python 3.6+ runtime for executing generated code and test cases

Hugging Face datasets library for loading the benchmark

Code generation model with Python output capability

Limitations

Limited to basic Python problems — does not test advanced concepts like async/await, decorators, metaclasses, or complex OOP patterns

Only 974 problems total — relatively small dataset compared to modern code corpora, may not capture long-tail programming patterns

Test cases are minimal (3 per problem) — may not catch edge cases or robustness issues in generated code

What makes it unique

vs alternatives

multi-problem code correctness validation

Medium confidence

Solves for

Best for

Automated evaluation pipelines for code generation models

Continuous integration systems testing model checkpoints

Researchers analyzing failure modes and error patterns

Requires

Python 3.6+ with ability to execute arbitrary code

Sandboxing mechanism (Docker, subprocess isolation, or similar) for safe code execution

Test harness to parse problem definitions and execute generated code

Limitations

Test cases only verify functional correctness — do not evaluate code efficiency, memory usage, or algorithmic complexity

Execution-based validation requires sandboxing to prevent malicious code — standard approach adds latency and resource overhead

Cannot detect subtle bugs that don't manifest in the 3 provided test cases per problem

What makes it unique

vs alternatives

problem categorization and concept mapping

Medium confidence

Solves for

Best for

Researchers analyzing model capabilities across programming domains

Teams evaluating whether models have learned specific programming patterns

Educators using the dataset to understand what concepts models understand

Requires

Access to problem metadata with concept tags

Ability to filter and group problems by category

Evaluation results mapped back to problem categories

Limitations

Categorization is coarse-grained — many problems span multiple concepts but are tagged with only primary concept

No hierarchical taxonomy — cannot distinguish between basic and advanced versions of same concept

Categories are fixed and predefined — cannot dynamically group problems by custom criteria

What makes it unique

vs alternatives

reference solution and test case repository

Medium confidence

Solves for

Best for

Researchers needing a stable, reproducible evaluation baseline

Teams building code generation evaluation infrastructure

Organizations comparing results across different models and time periods

Requires

Access to the MBPP dataset with reference solutions and test cases

Python 3.6+ to execute reference solutions

Test harness to run reference solutions against test cases

Limitations

Reference solutions are single implementations — may not represent all valid approaches to a problem

Test cases are minimal (3 per problem) — reference solution may pass tests but not be optimal or idiomatic

No alternative correct solutions provided — generated code using different valid approaches may be marked incorrect

What makes it unique

vs alternatives

pass@k metric computation and aggregation

Medium confidence

Solves for

Best for

ML researchers publishing code generation benchmarks

Teams comparing different model architectures and training approaches

Organizations tracking model improvement over time

Requires

Code generation model capable of producing k samples per problem

Test harness to evaluate each sample against test cases

Aggregation logic to compute pass@k across all problems

Limitations

Pass@k assumes k independent samples — expensive to compute for large k values (k=100 requires 97,400 total generations)

Metric is binary (pass/fail) — does not distinguish between solutions that are close to correct vs completely wrong

Does not account for code quality, efficiency, or style — only functional correctness

What makes it unique

vs alternatives

cross-model performance comparison and ranking

Medium confidence

Solves for

Best for

Researchers publishing model comparisons and benchmarks

Teams evaluating whether to adopt a new code generation model

Organizations tracking progress in code generation capabilities

Requires

Multiple code generation models to compare

Standardized evaluation harness that runs all models identically

Aggregation and ranking logic

Limitations

Comparison is limited to basic programming problems — does not reflect performance on complex, real-world code

Models may be optimized specifically for MBPP — results may not generalize to other code generation tasks

Comparison does not account for model size, latency, or resource requirements — only raw correctness

What makes it unique

vs alternatives

More objective than subjective code quality assessments; more standardized than ad-hoc comparisons using different test sets; enables tracking progress over time as models improve

problem difficulty and concept coverage analysis

Medium confidence

Solves for

Best for

Researchers designing new benchmarks or extending MBPP

Teams understanding what programming skills their models have learned

Educators using the dataset to understand concept coverage

Requires

Access to problem metadata with concept tags and difficulty ratings

Statistical analysis tools to compute coverage metrics

Visualization tools to display concept distribution

Limitations

Analysis is limited to metadata provided in the dataset — no automatic difficulty estimation

Concept coverage is based on manual tagging — may be incomplete or inconsistent

No analysis of problem interdependencies — some concepts build on others but are not explicitly linked

What makes it unique

vs alternatives

reference solution and test case provision

Medium confidence

Solves for

Best for

Researchers evaluating code generation models on functional correctness

Teams using few-shot prompting to improve code generation quality

Organizations analyzing generated code for efficiency and style

Requires

Problem metadata with 'code' and 'test_list' fields

Python 3.7+ to execute reference solutions and test cases

Limitations

Reference solutions are single implementations — may not represent all correct approaches or optimal algorithms

Test cases are minimal (3 per problem) — may not cover all edge cases or corner cases

No test case difficulty or coverage metrics — unclear which tests are most important

What makes it unique

Provides three test cases per problem (vs. single test in some benchmarks) enabling detection of edge case failures, with hand-written reference solutions demonstrating correct implementations

vs alternatives

More comprehensive than benchmarks with single test cases, as multiple tests catch off-by-one errors and edge case failures that would pass with only one input

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

About

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