Mathematical Problem Solving Benchmark

via “mathematical problem-solving benchmark”

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

Unique: This benchmark uniquely combines a large dataset of challenging competition problems with a robust evaluation framework for language models.

vs others: Unlike other benchmarks, MATH offers a comprehensive set of competition-level problems specifically designed for rigorous evaluation of mathematical reasoning in AI models.

via “visual mathematical reasoning benchmark”

Visual mathematical reasoning benchmark.

Unique: MathVista uniquely combines visual understanding with mathematical problem-solving, focusing on how well models interpret visual representations of math.

vs others: Unlike traditional benchmarks, MathVista specifically targets the intersection of visual and mathematical reasoning, providing a unique evaluation framework.

via “expert-authored frontier mathematics problem curation”

Expert-level math problems created by mathematicians.

Unique: Uses unpublished, expert-authored problems across four mathematical subdisciplines with explicit tiering from undergraduate to research level, plus a separate collection of genuinely unsolved problems — avoiding contamination from public datasets and testing on problems that have resisted professional mathematician attempts

vs others: Differs from MATH and other public benchmarks by using original, unpublished problems authored by expert mathematicians with peer review, providing frontier-level difficulty calibration that public datasets cannot offer

via “arithmetic and mathematical reasoning evaluation”

23 hardest BIG-Bench tasks where models initially failed.

Unique: Focuses specifically on multi-step arithmetic and mathematical reasoning through few-shot examples, isolating numerical reasoning capability from general language understanding. Tasks test both calculation accuracy and mathematical inference patterns.

vs others: More focused on mathematical reasoning than general reasoning benchmarks; more accessible than formal mathematics verification because it uses natural language problem statements rather than symbolic notation.

via “mathematical reasoning over visual data”

Mistral's 124B multimodal model with vision capabilities.

Unique: Achieves 69.4% on MathVista benchmark (outperforming all tested models) through integrated visual parsing and mathematical reasoning in a single 124B model, without requiring separate symbolic math engines or specialized mathematical libraries

vs others: Outperforms GPT-4o, Gemini-1.5 Pro, and Claude-3.5 Sonnet on MathVista while being available for self-hosted deployment, eliminating API dependency for educational or research mathematical analysis

via “benchmark dataset for mathematical reasoning”

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

Unique: This dataset includes detailed step-by-step solutions for each problem, making it unique for training AI in mathematical reasoning.

vs others: Unlike other datasets, MATH provides a structured approach to evaluating mathematical reasoning with competition-level problems and solutions.

via “multi-step mathematical reasoning benchmark evaluation”

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

Unique: Uses linguistically diverse, human-authored grade school problems (not synthetic) that require genuine multi-step reasoning with basic arithmetic, combined with a standardized answer extraction format (#### delimiter) that enables reproducible evaluation across heterogeneous model outputs

vs others: More challenging than simple arithmetic benchmarks (requires 2-8 reasoning steps) yet more accessible than advanced math benchmarks, making it ideal for measuring practical reasoning improvements in production models

via “mathematical reasoning with math benchmark performance”

Meta's 70B open model matching 405B-class performance.

Unique: Achieves strong mathematical reasoning performance at 70B parameters through instruction-tuning on mathematical problem-solving datasets, enabling competitive MATH benchmark performance without specialized symbolic reasoning modules

vs others: Provides mathematical reasoning capability comparable to larger closed-source models while remaining open-weight and self-hostable, though without formal verification guarantees of symbolic math systems

via “mathematical reasoning and problem-solving”

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

Unique: Achieves 90.2% on MATH benchmark through MoE architecture that routes mathematical reasoning tokens through specialized expert parameters, enabling efficient scaling of reasoning capability without proportional increase in active parameters per token

vs others: Matches GPT-4o mathematical reasoning performance (90.2% MATH) while using 37B active parameters vs GPT-4o's undisclosed parameter count, reducing inference latency and cost for math-heavy workloads

via “mathematical reasoning with math benchmark 80+ and structured problem-solving”

Alibaba's 72B open model trained on 18T tokens.

Unique: Integrates three distinct reasoning paradigms (CoT for symbolic reasoning, PoT for code-based computation, TIR for external tool orchestration) within single 72B dense model, enabling flexible problem-solving strategies without model switching. 128K context window allows full problem histories and solution verification within single inference call.

vs others: Outperforms Llama 2 70B (significantly lower math performance) and matches Llama 3 70B on general benchmarks while offering specialized math reasoning patterns; Qwen2.5-Math 72B variant provides deeper specialization but general-purpose 72B enables seamless math-to-code-to-text transitions without model switching.

via “mathematical problem-solving with outcome-based verification”

Alibaba's 32B reasoning model with chain-of-thought.

Unique: Trained with outcome-based rewards using accuracy verifiers that check final answer correctness, enabling the model to learn which reasoning paths lead to correct solutions rather than relying on human-annotated reasoning traces — this verification-driven approach achieves 79.5% on AIME 2024 with only 32B parameters

vs others: Achieves AIME performance comparable to much larger reasoning models (DeepSeek-R1 at 671B) through efficient RL training with outcome verification, making it deployable on single-GPU hardware while maintaining competitive mathematical reasoning capability

via “mathematics problem solving with aime-level performance”

Open-source reasoning model matching OpenAI o1.

Unique: Achieves frontier-level mathematics performance (79.8% AIME 2024) through RL-trained reasoning rather than specialized symbolic solvers, making it a general-purpose reasoning model rather than a domain-specific tool.

vs others: Outperforms most open-source models on mathematics and matches proprietary o1 on AIME, while being fully open-source under MIT license, enabling local deployment and fine-tuning.

via “mathematical problem solving with symbolic reasoning”

Cost-efficient reasoning model with configurable effort levels.

Unique: Implements specialized mathematical reasoning patterns with step-by-step derivation generation, achieving competition-level math performance through domain-specific training rather than general reasoning

vs others: Matches o3 on mathematical benchmarks at lower cost; outperforms standard LLMs (GPT-4, Claude) on competition-level problems due to reasoning-grade capabilities

via “mathematical problem solving with step-by-step verification”

text-generation model by undefined. 38,71,385 downloads.

Unique: Trained via RL to optimize for mathematical correctness with explicit intermediate step generation; learns to recognize and correct errors during reasoning rather than committing to incorrect paths

vs others: Outperforms GPT-4 on MATH and AIME benchmarks (94.3% vs 80%+ on AIME) through learned reasoning allocation; provides more transparent reasoning than Gemini while maintaining higher accuracy

via “advanced mathematical problem evaluation”

Competition mathematics problems (harder than GSM8K)

Unique: MATH's dataset is specifically curated from high school math contests, providing a unique challenge that is more difficult than typical benchmarks, allowing for a clearer differentiation of model capabilities.

vs others: More challenging than GSM8K, making it a superior choice for evaluating advanced mathematical reasoning in AI models.

via “multi-step mathematical reasoning evaluation”

Grade school math problems requiring multi-step reasoning

Unique: GSM8K is specifically curated to include a diverse set of multi-step reasoning problems, making it more targeted than generic math datasets, allowing for precise evaluation of reasoning capabilities in LLMs.

vs others: More focused on multi-step reasoning than other benchmarks like MATH, which may include less structured problems.

via “mathematical reasoning and logic problem evaluation with specialized scoring”

ReLE评测：中文AI大模型能力评测（持续更新）：目前已囊括374个大模型，覆盖chatgpt、gpt-5.4、谷歌gemini-3.1-pro、Claude-4.6、文心ERNIE-X1.1、ERNIE-5.0、qwen3.6-max、qwen3.6-plus、百川、讯飞星火、商汤senseChat等商用模型， 以及step3.5-flash、kimi-k2.6、ernie4.5、MiniMax-M2.7、deepseek-v4、Qwen3.6、llama4、智谱GLM-5.1、MiMo-V2、LongCat、gemma4、mistral等开源大模型。不仅提供排行榜，也提供规模超200万的大

Unique: Evaluates mathematical reasoning with 1-5 quality scale for reasoning steps rather than binary correctness, enabling partial credit for correct methodology with computational errors. Combines final answer accuracy with reasoning quality assessment to capture mathematical thinking capability. Includes multi-step reasoning problems and logical inference tasks beyond simple arithmetic.

vs others: More nuanced mathematical assessment than MMLU (binary correctness) and captures reasoning quality vs answer-only evaluation

via “mathematical-problem-solving-with-symbolic-reasoning”

Gemini 2.5 Pro is Google’s state-of-the-art AI model designed for advanced reasoning, coding, mathematics, and scientific tasks. It employs “thinking” capabilities, enabling it to reason through responses with enhanced accuracy...

Unique: Leverages extended internal reasoning to explore multiple mathematical approaches and verify symbolic manipulations before responding, providing higher confidence in mathematical correctness than models without reasoning capabilities.

vs others: Exceeds GPT-4 and Claude on complex mathematics by using internal reasoning to validate symbolic steps, reducing hallucinated solutions and improving explanation quality for educational use cases.

via “mathematical reasoning evaluation”

UGI-Leaderboard — AI demo on HuggingFace

Unique: Isolates mathematical reasoning as a distinct evaluation dimension on the leaderboard, enabling models to be ranked separately on math vs general generation, revealing capability specialization.

vs others: Simpler than running MATH or GSM8K locally with custom evaluation scripts, but less transparent than open-source math benchmarks regarding problem selection and difficulty.

via “mathematical-reasoning-and-problem-solving”

Hermes 4 70B is a hybrid reasoning model from Nous Research, built on Meta-Llama-3.1-70B. It introduces the same hybrid mode as the larger 405B release, allowing the model to either...

Unique: Trained on mathematical problem datasets with explicit step-by-step annotations, enabling the model to generate intermediate steps that match human problem-solving patterns rather than jumping directly to answers

vs others: More transparent than Wolfram Alpha for showing reasoning steps, though less reliable for advanced mathematics; stronger than GPT-3.5 on symbolic manipulation due to larger parameter count