multi-step mathematical reasoning benchmark evaluation, calculator-integrated solution generation with annotation parsing, solution correctness evaluation via answer extraction and comparison, socratic format problem generation with guided reasoning subquestions, example model solutions dataset with multiple model sizes, json lines dataset loading and preprocessing pipeline, model training and sampling utilities for math reasoning, multi-step reasoning complexity stratification (2-8 steps)

GSM8K

BenchmarkFree

8.5K grade school math problems — multi-step reasoning, verifiable solutions, reasoning benchmark.

Open Source

/ 100

8 capabilities

Capabilities8 decomposed

multi-step mathematical reasoning benchmark evaluation

Medium confidence

Provides a curated dataset of 8,500 grade school math word problems (7,500 training, 1,000 test) requiring 2-8 sequential reasoning steps with basic arithmetic operations. Each problem includes human-written step-by-step solutions with embedded calculation annotations marked by << >> delimiters, enabling models to learn both reasoning patterns and accurate computation. The benchmark is designed to stress-test language models on genuine multi-step mathematical logic rather than pattern matching, with linguistic diversity across problems to prevent overfitting to specific phrasings.

Solves for

Evaluate whether a language model can perform genuine multi-step mathematical reasoning beyond pattern matchingTrain models to decompose complex math problems into sequential calculation stepsBenchmark progress on mathematical reasoning as a core capability gap in LLMsCompare model performance across different architectures and training approaches on a standardized math reasoning task

Best for

AI researchers evaluating LLM mathematical reasoning capabilities

Teams fine-tuning models specifically for math problem-solving

Benchmark authors comparing model families on reasoning tasks

Requires

Python 3.6+ for dataset loading and evaluation scripts

JSON Lines format support for reading .jsonl files

Basic arithmetic evaluation capability (calculator integration or equivalent)

Limitations

Limited to grade school arithmetic operations — does not evaluate advanced mathematics (calculus, linear algebra, abstract reasoning)

Problems are English-language only — no multilingual variants for non-English mathematical reasoning evaluation

Test set is relatively small (1K examples) — may not capture all edge cases or problem variations in production systems

What makes it unique

Combines human-written diverse problem phrasing with embedded calculation annotations (marked via << >> delimiters) that models can learn from during training and leverage during inference, creating a bridge between language understanding and accurate computation that most benchmarks treat separately

vs alternatives

More challenging than simple arithmetic benchmarks (MATH, SVAMP) because it requires genuine multi-step decomposition rather than single-operation problems, yet more accessible than competition-level math datasets, making it ideal for identifying reasoning gaps in production models

calculator-integrated solution generation with annotation parsing

Medium confidence

Implements a calculator system that parses embedded mathematical expressions marked with << expression >> delimiters within solution text, evaluates them accurately during model generation, and replaces placeholders with computed results. This allows language models to generate solutions that include both natural language reasoning and precise arithmetic without relying on the model's own numerical computation capabilities. The system works at both training time (where annotations are treated as normal text for learning) and inference time (where expressions are detected, evaluated, and substituted with results).

Solves for

Enable models to generate mathematically correct intermediate calculations within reasoning chains without hallucinating arithmeticTrain models to recognize when to invoke calculation and how to format calculation requests within generated textEnsure that solution chains contain accurate numeric results even if the model's arithmetic reasoning is weakCreate a standardized interface for models to delegate computation to a reliable calculator during generation

Best for

Teams training models specifically for math problem-solving where arithmetic accuracy is critical

Researchers studying how to integrate external tools (calculators) into language model generation workflows

Production systems where mathematical correctness is non-negotiable and cannot be approximated by the model

Requires

Python 3.6+ with arithmetic evaluation library (built-in eval or safe_eval alternative)

Training data with properly formatted calculation annotations using << >> delimiters

Model architecture that can be fine-tuned to recognize and generate the annotation syntax

Limitations

Requires explicit annotation of calculations in training data — models must learn to recognize and use the << >> syntax

Limited to basic arithmetic operations — does not support symbolic math, calculus, or complex mathematical functions

Adds latency to generation — each calculation annotation requires parsing and evaluation overhead

What makes it unique

Embeds calculator invocation directly within the solution text generation process using a lightweight annotation syntax (<<...>>) rather than requiring separate function-calling APIs or tool-use protocols, making it trainable end-to-end and reducing the cognitive load on models to decide when and how to use external computation

vs alternatives

More integrated than separate tool-calling systems (which require explicit function signatures and API schemas) because the calculator syntax is learned as part of the solution text itself, enabling tighter coupling between reasoning and computation without the overhead of structured function-calling protocols

solution correctness evaluation via answer extraction and comparison

Medium confidence

Implements an automated evaluation system that extracts final numeric answers from generated solutions by parsing the #### delimiter (which marks the final answer in the standard format), compares extracted answers against ground truth values, and computes accuracy metrics. The system handles both exact numeric matching and can be extended to support approximate matching or alternative valid solution paths. This enables rapid evaluation of model performance across the entire benchmark without manual inspection.

Solves for

Automatically score model-generated solutions against ground truth answers without manual reviewCompute aggregate accuracy metrics (overall pass rate, pass@k with multiple generations) across the benchmarkIdentify which problem categories or reasoning patterns cause models to failEnable rapid iteration on model training by providing immediate feedback on reasoning quality

Best for

Researchers running large-scale model evaluations and needing automated scoring

Teams training models and requiring fast feedback loops on reasoning improvements

Benchmark maintainers comparing model performance across different architectures and training approaches

Requires

Generated solution text with #### delimiter marking the final answer

Ground truth answer values in numeric format

Python 3.6+ for answer extraction and comparison logic

Limitations

Requires exact numeric answer matching — does not credit solutions with correct reasoning but arithmetic errors

Does not evaluate solution quality or reasoning clarity — only final answer correctness

Assumes well-formatted solutions with clear #### delimiter — malformed outputs may fail to extract answers

What makes it unique

Uses a simple delimiter-based answer extraction approach (#### marker) rather than complex NLP parsing or regex patterns, making it robust to diverse solution phrasings while remaining easy to implement and debug — the simplicity is intentional to avoid false negatives from overly strict parsing

vs alternatives

More reliable than regex-based answer extraction because it leverages the structured format of the dataset (where all solutions follow the #### convention), avoiding the brittleness of pattern-matching approaches that fail on minor formatting variations

socratic format problem generation with guided reasoning subquestions

Medium confidence

Provides an alternative dataset format (train_socratic.jsonl, test_socratic.jsonl) that augments standard math problems with Socratic-style guided reasoning subquestions. These subquestions decompose the main problem into intermediate reasoning steps, helping models learn to break down complex problems into manageable substeps. The format enables training approaches that use intermediate supervision or chain-of-thought prompting, where models are encouraged to answer subquestions before attempting the final answer.

Solves for

Train models using intermediate supervision where subquestion answers provide additional learning signalsEnable chain-of-thought prompting by providing explicit intermediate reasoning steps that models should learn to generateImprove model performance on multi-step reasoning by teaching explicit problem decompositionEvaluate whether models can follow Socratic reasoning patterns when prompted with guided subquestions

Best for

Researchers studying chain-of-thought prompting and intermediate supervision techniques

Teams training models where explicit reasoning step supervision improves convergence

Educators or content creators building math tutoring systems that guide students through problems

Requires

Python 3.6+ for loading Socratic format data

Training framework that can handle variable-length sequences of subquestions

Model architecture supporting intermediate supervision or chain-of-thought training objectives

Limitations

Subquestions are human-written and may not reflect all valid problem decompositions

Requires models to learn to generate subquestions themselves at inference time — the dataset only provides examples

May introduce bias toward specific reasoning patterns if subquestions are not diverse

What makes it unique

Provides human-written Socratic subquestions as part of the dataset rather than requiring models to generate their own decompositions, enabling direct supervision of intermediate reasoning steps and making it easier to train models on explicit problem-solving strategies

vs alternatives

More structured than generic chain-of-thought datasets because subquestions are specifically designed to decompose each problem into solvable steps, whereas generic CoT datasets may contain arbitrary reasoning that doesn't necessarily improve problem-solving capability

example model solutions dataset with multiple model sizes

Medium confidence

Includes a curated collection of model-generated solutions (example_model_solutions.jsonl) from various model architectures and sizes, showing how different models approach the same problems. This enables analysis of solution quality across model scales, study of failure modes and reasoning patterns, and comparison of how different architectures decompose problems. The dataset serves as both a reference for expected solution quality and a resource for analyzing model behavior.

Solves for

Compare how different model sizes and architectures approach the same math problemsAnalyze failure modes and reasoning patterns across model familiesEstablish baseline performance expectations for different model scalesStudy the relationship between model size and reasoning capability on multi-step problems

Best for

Researchers analyzing scaling laws and how reasoning capability improves with model size

Teams benchmarking their models against known baselines from multiple architectures

Analysts studying failure modes and error patterns in model reasoning

Requires

Python 3.6+ for loading and analyzing solution data

Evaluation scripts to compare model solutions against ground truth

Limitations

Solutions are from specific model versions — may not reflect current state-of-the-art performance

Limited to models available at the time of dataset creation — does not include newer architectures

Does not include reasoning traces or intermediate steps for all models — only final solutions

What makes it unique

Provides reference solutions from multiple model sizes and architectures as part of the benchmark dataset itself, enabling direct comparison of how different models approach the same problems rather than requiring researchers to generate their own baseline solutions

vs alternatives

More valuable than generic leaderboards because it includes actual solution text and reasoning chains from multiple models, enabling deep analysis of reasoning patterns and failure modes rather than just aggregate accuracy numbers

json lines dataset loading and preprocessing pipeline

Medium confidence

Implements a standardized data loading system (dataset.py) that reads the .jsonl format files, parses problem-solution pairs, handles both standard and Socratic formats, and provides utilities for splitting data into training/validation/test sets. The system abstracts away file I/O and format parsing, providing a clean Python API for accessing problems, solutions, and metadata. This enables researchers to focus on model training rather than data engineering.

Solves for

Load benchmark data efficiently without writing custom JSON parsing codeAccess problems and solutions in a standardized format across different dataset variantsSplit data into train/validation/test sets with consistent random seeds for reproducibilityIterate over problems in batches for model training and evaluation

Best for

Researchers and engineers building models on GSM8K without wanting to implement data loading

Teams integrating GSM8K into existing training pipelines

Educators using GSM8K for teaching or research without deep data engineering expertise

Requires

Python 3.6+

JSON Lines (.jsonl) files in the standard GSM8K format

Read access to the dataset files

Limitations

Requires .jsonl files to be in the exact expected format — no validation or error recovery for malformed data

Limited to in-memory loading — does not support streaming for very large dataset variants

No built-in data augmentation or preprocessing beyond basic parsing

What makes it unique

Provides a unified loading interface for both standard and Socratic dataset formats through a single API, abstracting away format differences and enabling seamless switching between dataset variants without changing downstream code

vs alternatives

More convenient than raw JSON parsing because it handles the specific GSM8K format conventions (calculation annotations, answer delimiters, Socratic subquestions) automatically, reducing boilerplate and potential parsing errors in downstream code

model training and sampling utilities for math reasoning

Medium confidence

Provides training utilities and sampling strategies for fine-tuning language models on GSM8K data, including support for different training objectives (standard next-token prediction, intermediate supervision on subquestions, calculator-aware training). The system handles batching, sampling strategies (e.g., sampling multiple solutions per problem for pass@k evaluation), and integration with common training frameworks. This enables researchers to quickly set up training runs without implementing custom training loops.

Solves for

Fine-tune language models on GSM8K with standard supervised learning objectivesTrain models using intermediate supervision where subquestion answers provide additional learning signalsGenerate multiple solutions per problem for pass@k evaluation metricsIntegrate GSM8K training into existing model training pipelines and frameworks

Best for

Researchers fine-tuning models specifically for mathematical reasoning

Teams implementing training pipelines that need to support multiple training objectives

Organizations evaluating pass@k metrics where multiple generations per problem are needed

Requires

Python 3.6+

Compatible training framework (PyTorch, TensorFlow, or equivalent)

Language model with fine-tuning support

Limitations

Requires models to be compatible with the training framework — may not work with all architectures

Training utilities are specific to GSM8K format — not easily generalizable to other datasets

Multiple sampling per problem increases computational cost significantly for pass@k evaluation

What makes it unique

Integrates calculator-aware training where models learn to recognize and generate calculation annotations, enabling end-to-end training that combines language understanding with accurate arithmetic rather than treating them as separate concerns

vs alternatives

More specialized than generic training utilities because it handles GSM8K-specific requirements (calculation annotations, Socratic subquestions, answer extraction) natively, reducing the need for custom preprocessing and enabling faster experimentation

multi-step reasoning complexity stratification (2-8 steps)

Medium confidence

Problems are explicitly designed to require 2-8 sequential reasoning steps using basic arithmetic operations, with step counts documented in solutions. This stratification enables analysis of how model performance degrades with problem complexity and allows curriculum learning approaches that gradually increase difficulty. The step-by-step solution format makes reasoning complexity transparent and measurable.

Solves for

Analyze how model performance degrades as problem complexity increasesImplement curriculum learning by training on simpler problems before complex onesIdentify at which complexity level models begin to failEvaluate whether models can maintain reasoning accuracy across longer chains

Best for

Researchers studying scaling laws for reasoning complexity

Teams implementing curriculum learning strategies

Analysis of model failure modes as problem difficulty increases

Requires

Access to solution files with step-by-step reasoning

Ability to parse and count reasoning steps

Limitations

Step count is not formally defined — different annotators may count steps differently

No explicit complexity metrics beyond step count — does not account for arithmetic difficulty or problem structure

All problems use only basic arithmetic — does not test reasoning with more complex mathematical operations

What makes it unique

Provides explicit step-by-step solutions that make reasoning complexity transparent and measurable, enabling direct analysis of how models handle multi-step chains, whereas most benchmarks only provide final answers without intermediate reasoning visibility

vs alternatives

More analytically useful than single-answer benchmarks because step-level granularity reveals where reasoning breaks down, and more practical than synthetic complexity metrics because steps are human-authored and reflect actual problem structure

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 GSM8K, ranked by overlap. Discovered automatically through the match graph.

Dataset46

MATH

12.5K competition math problems across 7 subjects and 5 difficulty levels.

step-by-step solution reference generation and validationcompetition-mathematics problem benchmark evaluation

2 shared capabilities

Dataset26

gsm8k

Dataset by openai. 8,22,680 downloads.

grade-school math word problem benchmark datasetcrowdsourced problem-solution annotation pipeline

2 shared capabilities

Model21

DeepSeek: R1

DeepSeek R1 is here: Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active in an inference pass....

mathematical problem solving with step-by-step verificationmulti-step problem solving with extended context windows

2 shared capabilities

Benchmark39

MATH Benchmark

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

solution step extraction and step-by-step reasoning evaluation

1 shared capability

Model45

DeepSeek V3

671B MoE model matching GPT-4o at fraction of training cost.

mathematical reasoning and problem-solving with 90.2% math benchmark performance

1 shared capability

Model44

o3-mini

Cost-efficient reasoning model with configurable effort levels.

mathematical problem solving with step-by-step reasoning

1 shared capability

Best For

✓AI researchers evaluating LLM mathematical reasoning capabilities
✓Teams fine-tuning models specifically for math problem-solving
✓Benchmark authors comparing model families on reasoning tasks
✓Organizations assessing whether their models can handle real-world quantitative reasoning
✓Teams training models specifically for math problem-solving where arithmetic accuracy is critical
✓Researchers studying how to integrate external tools (calculators) into language model generation workflows
✓Production systems where mathematical correctness is non-negotiable and cannot be approximated by the model
✓Researchers running large-scale model evaluations and needing automated scoring

Known Limitations

⚠Limited to grade school arithmetic operations — does not evaluate advanced mathematics (calculus, linear algebra, abstract reasoning)
⚠Problems are English-language only — no multilingual variants for non-English mathematical reasoning evaluation
⚠Test set is relatively small (1K examples) — may not capture all edge cases or problem variations in production systems
⚠Requires exact numeric answer matching for evaluation — does not credit partial credit or alternative valid solution paths
⚠Requires explicit annotation of calculations in training data — models must learn to recognize and use the << >> syntax
⚠Limited to basic arithmetic operations — does not support symbolic math, calculus, or complex mathematical functions

Requirements

Python 3.6+ for dataset loading and evaluation scriptsJSON Lines format support for reading .jsonl filesBasic arithmetic evaluation capability (calculator integration or equivalent)Python 3.6+ with arithmetic evaluation library (built-in eval or safe_eval alternative)Training data with properly formatted calculation annotations using << >> delimitersModel architecture that can be fine-tuned to recognize and generate the annotation syntaxGenerated solution text with #### delimiter marking the final answerGround truth answer values in numeric format

Input / Output

Accepts: JSON Lines (.jsonl) formatted problem-solution pairs, Natural language math word problems (text), Step-by-step solution chains with embedded calculations, Solution text with embedded calculation expressions in << expression >> format, Mathematical expressions using basic operators (+, -, *, /, %), Natural language reasoning text interspersed with calculation annotations, Model-generated solution text with #### final_answer format, Ground truth answer values (numeric), Solution chains with intermediate reasoning steps, JSON Lines format with main problem, subquestions, and final answer, Natural language subquestions decomposing the main problem, Answers to each subquestion in sequence, JSON Lines format with model-generated solutions, Problem IDs linking solutions to original benchmark problems, Model metadata (architecture, size, training approach), JSON Lines (.jsonl) files with problem-solution pairs, File paths to dataset files, GSM8K dataset in standard or Socratic format, Model checkpoints and configuration, Training hyperparameters (learning rate, batch size, epochs), step-by-step solutions with clear step boundaries

Produces: Structured evaluation metrics (accuracy, pass@k rates), Extracted final numeric answers for comparison, Model-generated solution chains with intermediate reasoning steps, Solution text with calculated results substituted for << >> placeholders, Extracted numeric results from evaluated expressions, Validated solution chains with confirmed arithmetic accuracy, Boolean correctness labels (correct/incorrect per problem), Extracted numeric answers from generated solutions, Aggregate accuracy metrics (pass rate, pass@k, per-category performance), Model-generated subquestions and intermediate answers, Final numeric answer derived from subquestion answers, Reasoning chains showing explicit problem decomposition, Extracted solutions and final answers from multiple models, Comparative accuracy metrics across model families, Analysis of reasoning patterns and failure modes, Parsed problem-solution dictionaries, Iterable dataset objects for training loops, Train/validation/test split indices, Fine-tuned model checkpoints, Training loss curves and metrics, Generated solutions for evaluation, Pass@k metrics from multiple samples per problem, step count per problem, accuracy stratified by complexity level, complexity-based performance curves

UnfragileRank

Adoption70%(25% weight)

Quality23%(35% weight)

Ecosystem30%(25% weight)

Match Graph10%(10% 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: Benchmark

8 capabilities

Visit GSM8K→

About

8,500 grade school math word problems requiring multi-step reasoning. Each problem has 2-8 reasoning steps. Created by OpenAI. Simple enough to verify but requires genuine mathematical reasoning.

Alternatives to GSM8K

promptfoo44Model

Test your prompts, agents, and RAGs. Red teaming/pentesting/vulnerability scanning for AI. Compare performance of GPT, Claude, Gemini, Llama, and more. Simple declarative configs with command line and CI/CD integration. Used by OpenAI and Anthropic.

Compare →

mlflow43Prompt

The open source AI engineering platform for agents, LLMs, and ML models. MLflow enables teams of all sizes to debug, evaluate, monitor, and optimize production-quality AI applications while controlling costs and managing access to models and data.

Compare →

promptflow41Model

Build high-quality LLM apps - from prototyping, testing to production deployment and monitoring.

Compare →

amplication43Workflow

Amplication brings order to the chaos of large-scale software development by creating Golden Paths for developers - streamlined workflows that drive consistency, enable high-quality code practices, simplify onboarding, and accelerate standardized delivery across teams.

Compare →

Are you the builder of GSM8K?

Claim this artifact to get a verified badge, access match analytics, see which intents users search for, and manage your listing.

Claim this artifact →Verification via email

Get the weekly brief

New tools, rising stars, and what's actually worth your time. No spam.

Data Sources

seed developer essentials

Looking for something else?

Search →

Capabilities8 decomposed

multi-step mathematical reasoning benchmark evaluation

Medium confidence

Solves for

Best for

AI researchers evaluating LLM mathematical reasoning capabilities

Teams fine-tuning models specifically for math problem-solving

Benchmark authors comparing model families on reasoning tasks

Requires

Python 3.6+ for dataset loading and evaluation scripts

JSON Lines format support for reading .jsonl files

Basic arithmetic evaluation capability (calculator integration or equivalent)

Limitations

Limited to grade school arithmetic operations — does not evaluate advanced mathematics (calculus, linear algebra, abstract reasoning)

Problems are English-language only — no multilingual variants for non-English mathematical reasoning evaluation

Test set is relatively small (1K examples) — may not capture all edge cases or problem variations in production systems

What makes it unique

vs alternatives

calculator-integrated solution generation with annotation parsing

Medium confidence

Solves for

Best for

Teams training models specifically for math problem-solving where arithmetic accuracy is critical

Researchers studying how to integrate external tools (calculators) into language model generation workflows

Production systems where mathematical correctness is non-negotiable and cannot be approximated by the model

Requires

Python 3.6+ with arithmetic evaluation library (built-in eval or safe_eval alternative)

Training data with properly formatted calculation annotations using << >> delimiters

Model architecture that can be fine-tuned to recognize and generate the annotation syntax

Limitations

Requires explicit annotation of calculations in training data — models must learn to recognize and use the << >> syntax

Limited to basic arithmetic operations — does not support symbolic math, calculus, or complex mathematical functions

Adds latency to generation — each calculation annotation requires parsing and evaluation overhead

What makes it unique

vs alternatives

solution correctness evaluation via answer extraction and comparison

Medium confidence

Solves for

Best for

Researchers running large-scale model evaluations and needing automated scoring

Teams training models and requiring fast feedback loops on reasoning improvements

Benchmark maintainers comparing model performance across different architectures and training approaches

Requires

Generated solution text with #### delimiter marking the final answer

Ground truth answer values in numeric format

Python 3.6+ for answer extraction and comparison logic

Limitations

Requires exact numeric answer matching — does not credit solutions with correct reasoning but arithmetic errors

Does not evaluate solution quality or reasoning clarity — only final answer correctness

Assumes well-formatted solutions with clear #### delimiter — malformed outputs may fail to extract answers

What makes it unique

vs alternatives

socratic format problem generation with guided reasoning subquestions

Medium confidence

Solves for

Best for

Researchers studying chain-of-thought prompting and intermediate supervision techniques

Teams training models where explicit reasoning step supervision improves convergence

Educators or content creators building math tutoring systems that guide students through problems

Requires

Python 3.6+ for loading Socratic format data

Training framework that can handle variable-length sequences of subquestions

Model architecture supporting intermediate supervision or chain-of-thought training objectives

Limitations

Subquestions are human-written and may not reflect all valid problem decompositions

Requires models to learn to generate subquestions themselves at inference time — the dataset only provides examples

May introduce bias toward specific reasoning patterns if subquestions are not diverse

What makes it unique

vs alternatives

example model solutions dataset with multiple model sizes

Medium confidence

Solves for

Best for

Researchers analyzing scaling laws and how reasoning capability improves with model size

Teams benchmarking their models against known baselines from multiple architectures

Analysts studying failure modes and error patterns in model reasoning

Requires

Python 3.6+ for loading and analyzing solution data

Evaluation scripts to compare model solutions against ground truth

Limitations

Solutions are from specific model versions — may not reflect current state-of-the-art performance

Limited to models available at the time of dataset creation — does not include newer architectures

Does not include reasoning traces or intermediate steps for all models — only final solutions

What makes it unique

vs alternatives

json lines dataset loading and preprocessing pipeline

Medium confidence

Solves for

Best for

Researchers and engineers building models on GSM8K without wanting to implement data loading

Teams integrating GSM8K into existing training pipelines

Educators using GSM8K for teaching or research without deep data engineering expertise

Requires

Python 3.6+

JSON Lines (.jsonl) files in the standard GSM8K format

Read access to the dataset files

Limitations

Requires .jsonl files to be in the exact expected format — no validation or error recovery for malformed data

Limited to in-memory loading — does not support streaming for very large dataset variants

No built-in data augmentation or preprocessing beyond basic parsing

What makes it unique

vs alternatives

model training and sampling utilities for math reasoning

Medium confidence

Solves for

Best for

Researchers fine-tuning models specifically for mathematical reasoning

Teams implementing training pipelines that need to support multiple training objectives

Organizations evaluating pass@k metrics where multiple generations per problem are needed

Requires

Python 3.6+

Compatible training framework (PyTorch, TensorFlow, or equivalent)

Language model with fine-tuning support

Limitations

Requires models to be compatible with the training framework — may not work with all architectures

Training utilities are specific to GSM8K format — not easily generalizable to other datasets

Multiple sampling per problem increases computational cost significantly for pass@k evaluation

What makes it unique

vs alternatives

multi-step reasoning complexity stratification (2-8 steps)

Medium confidence

Solves for

Best for

Researchers studying scaling laws for reasoning complexity

Teams implementing curriculum learning strategies

Analysis of model failure modes as problem difficulty increases

Requires

Access to solution files with step-by-step reasoning

Ability to parse and count reasoning steps

Limitations

Step count is not formally defined — different annotators may count steps differently

No explicit complexity metrics beyond step count — does not account for arithmetic difficulty or problem structure

All problems use only basic arithmetic — does not test reasoning with more complex mathematical operations

What makes it unique

vs alternatives

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

Alternatives to GSM8K

promptfoo44Model

Compare →

mlflow43Prompt

Compare →

promptflow41Model

Build high-quality LLM apps - from prototyping, testing to production deployment and monitoring.

Compare →

amplication43Workflow

Compare →

GSM8K

Capabilities8 decomposed

multi-step mathematical reasoning benchmark evaluation

calculator-integrated solution generation with annotation parsing

solution correctness evaluation via answer extraction and comparison

socratic format problem generation with guided reasoning subquestions

example model solutions dataset with multiple model sizes

json lines dataset loading and preprocessing pipeline

model training and sampling utilities for math reasoning

multi-step reasoning complexity stratification (2-8 steps)

Related Artifactssharing capabilities

MATH

gsm8k

DeepSeek: R1

MATH Benchmark

DeepSeek V3

o3-mini

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to GSM8K

Are you the builder of GSM8K?

Get the weekly brief

Data Sources

GSM8K

Capabilities8 decomposed

multi-step mathematical reasoning benchmark evaluation

calculator-integrated solution generation with annotation parsing

solution correctness evaluation via answer extraction and comparison

socratic format problem generation with guided reasoning subquestions

example model solutions dataset with multiple model sizes

json lines dataset loading and preprocessing pipeline

model training and sampling utilities for math reasoning

multi-step reasoning complexity stratification (2-8 steps)

Related Artifactssharing capabilities

MATH

gsm8k

DeepSeek: R1

MATH Benchmark

DeepSeek V3

o3-mini

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to GSM8K

Are you the builder of GSM8K?

Get the weekly brief

Data Sources