Math LLMs are large language models focused on mathematical problems, capable of solving equations, proving theorems, generating mathematical reasoning steps, or handling symbolic computation. Trained on math-specific datasets, they support mathematics from basic arithmetic to advanced mathematics, covering algebra, geometry, number theory, and combinatorics.

What's the difference between math LLMs and general-purpose LLMs?

Math LLMs are optimized for mathematical problems, excelling in equation solving, theorem proving, and mathematical reasoning. General-purpose LLMs suit diverse scenarios, while math LLMs perform better in math benchmarks like GSM8K, MATH, and AIME 2025.

What's the difference between math LLMs and AI reasoning LLMs?

Math LLMs focus on mathematical problem solving and theorem proving, emphasizing symbolic computation and mathematical reasoning. AI reasoning LLMs focus on logical reasoning and problem solving, emphasizing multi-step reasoning. They differ in application scenarios and technical focus.

What are GSM8K, MATH, and AIME 2025?

GSM8K is a grade school math word problem benchmark for evaluating multi-step mathematical reasoning. MATH is a competition-level math problem benchmark for assessing advanced mathematical reasoning. AIME 2025 is a high school invitational math exam benchmark for evaluating competition-level mathematical problem-solving capabilities.

How to choose the right math LLM?

Consider task type (math education, research assistance, theorem proving, competition math), benchmark performance (GSM8K, MATH, AIME 2025), mathematical reasoning requirements, language needs, and cost budget. Try 2-3 models first, comparing actual performance before choosing.

How accurate are math LLMs in solving complex mathematical problems?

Professional math LLMs achieve high accuracy on standardized benchmarks, with top models scoring 90%+ on GSM8K and 80%+ on MATH. However, accuracy varies by problem complexity: basic arithmetic and algebra achieve near-perfect results, while advanced topics like topology and abstract algebra may require more verification. Real-world accuracy depends on problem clarity, model selection, and verification practices. Always verify critical mathematical results, especially for research or educational applications.

Can math LLMs handle mathematical notation and symbolic computation?

Yes, most math LLMs support mathematical notation including LaTeX, symbolic expressions, and formatted equations. Professional models can parse and generate complex mathematical notation, perform symbolic computation, and handle multi-step algebraic manipulations. However, notation support varies by model: some excel at specific notation types, while others provide broader coverage. For best results, use clear notation and verify symbolic computations, especially for complex expressions or specialized mathematical domains.

Best Math LLMs (2026): Equation Solving

Introduction

This comprehensive guide introduces the best math LLM tools in 2026, providing professional benchmark testing and user experience analysis to recommend the most suitable mathematical problem-solving and theorem proving solutions.

What Are Math LLMs

Math LLMs (LLM for Math) are large language models focused on mathematical problems, capable of solving equations, proving theorems, generating mathematical reasoning steps, or handling symbolic computation. These models are often trained on math-specific datasets (such as math competition problem sets), supporting mathematics from basic arithmetic to advanced mathematics, covering algebra, geometry, number theory, combinatorics, and other mathematical fields. Leading math LLMs include OpenAI's GPT-5.2, Google's Gemini 3 Pro, DeepSeek's DeepSeek R1, Anthropic's Claude Opus 4.5, and Moonshot AI's Kimi K2.

The core value of math LLMs lies in improving mathematical problem-solving capabilities, enhancing mathematical reasoning and proof abilities, enabling users to solve mathematical problems and understand mathematical concepts more accurately. Whether it's math education, research assistance, theorem proving, or mathematical modeling in data analysis, math LLMs play crucial roles.

The main difference between math LLMs and general-purpose LLMs or AI reasoning LLMs is that general-purpose LLMs like

GPT, Claude, Gemini

are suitable for diverse task scenarios, AI reasoning LLMs like

AI reasoning LLMs

focus on logical reasoning tasks, while math LLMs are specifically optimized for mathematical problems, excelling in equation solving, theorem proving, mathematical reasoning, and other math-related tasks.

How Math LLMs Work

Modern math LLM technology is based on deep learning and Transformer architecture, specifically trained on math-specific datasets, capable of understanding mathematical problem structure, performing multi-step mathematical reasoning and symbolic computation to generate high-quality, logically rigorous mathematical reasoning processes. The technology employs neural networks trained on vast mathematical datasets that learn problem-solving patterns, symbolic operations, and proof techniques. Compared to traditional mathematical tools requiring manual calculation and limited reasoning capabilities, math LLMs show significant improvements in mathematical reasoning depth, symbolic computation accuracy, and problem-solving capabilities, making advanced mathematical problem-solving accessible to more users.

Symbolic reasoning: Performing symbolic computation and algebraic operations, handling complex mathematical expressions and equations.
Multi-step reasoning: Performing complex mathematical reasoning through multiple steps, solving problems that require sequential logical thinking.
Theorem proving: Generating proof steps and verifying theorem correctness, providing rigorous mathematical proofs.
Thinking capabilities: Some models support thinking modes, enabling deep mathematical reasoning and exploration of solution approaches.

Math LLMs typically use Chain-of-Thought (CoT) technology to solve mathematical problems through step-by-step reasoning. Different models use different architectures: some focus on symbolic computation, others specialize in numerical reasoning. Main benchmarks include GSM8K (grade school math), MATH (competition-level math), and AIME 2025 (high school invitational math exam), helping users understand different models' actual performance on mathematical tasks. These developments not only improve mathematical problem-solving capabilities and accuracy but also provide more possibilities for students and researchers, making math LLMs more widespread.

Best Math LLMs 2026

Math LLMs are large language models focused on mathematical problems, accessible via API. Many mathematical applications are built on these models. These models excel in equation solving, theorem proving, mathematical reasoning, and other math-related tasks, demonstrating outstanding performance in benchmarks such as GSM8K, MATH, and AIME 2025.

1. GPT-5.2 (xhigh): Math Leader

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GPT-5.2 (xhigh) is OpenAI's top-tier math LLM, demonstrating exceptional performance in math benchmarks. The model achieves approximately 96% on GSM8K, 97.9% on MATH, and 100% on AIME 2025, with an average score of 95.2%, ranking first. Core features include advanced mathematical reasoning, symbolic computation, multi-step reasoning, and theorem proving capabilities. Ideal for complex mathematical reasoning, competition math, and advanced mathematical problem-solving scenarios.

2. Gemini 3 Pro Preview: Competition Math Expert

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Gemini 3 Pro Preview is Google DeepMind's math LLM, excelling in competition math. The model achieves approximately 95% on GSM8K, 91.8% on MATH, and 95% on AIME 2025, with an average score of 93.1%, ranking second. Core features include competition math optimization, advanced mathematical reasoning, symbolic computation, and multi-step reasoning capabilities. Ideal for competition math, advanced mathematical problem solving, and mathematical research scenarios.

3. DeepSeek R1: Reasoning Math Optimization

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DeepSeek R1 is DeepSeek's math LLM, excelling in reasoning math. The model achieves approximately 93% on GSM8K, 95% on MATH, and approximately 92% on AIME 2025, with an average score of 91.4%, ranking third. Core features include reasoning capabilities, mathematical reasoning optimization, symbolic computation, and high cost-effectiveness. Ideal for mathematical reasoning, Chinese math problems, and local deployment. Its open-source version makes it ideal for customized development.

4. Claude Opus 4.5 Thinking: Thinking Math

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Claude Opus 4.5 Thinking is Anthropic's math LLM, excelling in thinking capabilities. The model achieves approximately 93% on GSM8K, approximately 90% on MATH, and 93% on AIME 2025, with an average score of 90.7%, ranking fourth. Core features include thinking capabilities, advanced mathematical reasoning, symbolic computation, and logical analysis. Ideal for deep mathematical reasoning, theorem proving, and complex mathematical problem-solving scenarios.

5. Kimi K2 (0905): Chinese Math Optimization

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Kimi K2 (0905) is Moonshot AI's math LLM, excelling in Chinese math scenarios. The model achieves 92.1% on GSM8K, approximately 85% on MATH, and approximately 92% on AIME 2025, with an average score of 88.5%, ranking fifth. Core features include Chinese math optimization, mathematical reasoning capabilities, symbolic computation, and fast response. Ideal for Chinese math understanding, Chinese math problem solving, and real-time math assistance scenarios.

Other Math LLMs

Beyond the main math LLMs above, many other excellent math LLMs excel in specific mathematical scenarios:

o3 (High) (OpenAI): OpenAI's reasoning math model, achieving 95.8% on GSM8K, 96.4% on MATH, and approximately 98% on AIME 2025, excelling in mathematical reasoning tasks.
GPT-5.1 (OpenAI): OpenAI's math model, achieving 94.8% on GSM8K, approximately 92.5% on MATH, and 87.3% on AIME 2025.
Gemini 3 Pro (Google): Google's math model, achieving 93.4% on GSM8K, approximately 90% on MATH, and 91.9% on AIME 2025.
Gemini 2.5 Pro (Google): Google's math model, achieving 89.7% on GSM8K, approximately 85% on MATH, and approximately 80% on AIME 2025.
DeepSeek-V3.2 (Thinking) (DeepSeek): DeepSeek's thinking math model, achieving 92.1% on GSM8K, 85% on MATH, and approximately 85% on AIME 2025.
Claude Opus 4.5 (Anthropic): Anthropic's math model, achieving 92.3% on GSM8K, approximately 85% on MATH, and 90.8% on AIME 2025.
Claude 4.5 Sonnet (Anthropic): Anthropic's math model, achieving approximately 90% on GSM8K, 80.4% on MATH, and approximately 85% on AIME 2025.
Kimi K2 Thinking (Moonshot AI): Moonshot AI's thinking math model, achieving approximately 90% on GSM8K, 83% on MATH, and approximately 85% on AIME 2025.

Math LLM Comparison: Choose the Best for You

Below is a detailed comparison of leading math LLMs to help you quickly understand each model's benchmark performance, core features, and applicable scenarios:

Use Cases: Mathematical Problem Solving and Research

Math LLMs have very wide application scenarios, covering multiple mathematical fields from math education to research assistance.

Math Education

Math LLMs excel in math education, answering mathematical questions, generating solution steps, and explaining mathematical concepts. Students can describe mathematical problems in natural language, and models automatically generate detailed solution steps and explanations. This significantly lowers math learning barriers, allowing students to better understand mathematical concepts and solution methods.

Research Assistance

Math LLMs have unique advantages in research assistance, performing mathematical calculations, verifying mathematical formulas, and generating mathematical proofs. Models with thinking capabilities enable complex mathematical reasoning and analysis, providing more accurate mathematical research support. This is significant for improving research efficiency and mathematical research quality.

Theorem Proving

Math LLMs demonstrate powerful capabilities in theorem proving, generating mathematical proof steps, verifying theorem correctness, and analyzing proof logic. Researchers can receive powerful theorem proving support, accelerating mathematical research progress. This is significant for improving proof efficiency and mathematical research quality.

Mathematical Modeling in Data Analysis

Math LLMs excel in mathematical modeling in data analysis, performing statistical analysis, building mathematical models, and solving optimization problems. Data analysts can quickly build mathematical models, obtaining detailed analysis results. This is significant for improving data analysis efficiency and mathematical modeling accuracy.

Competition Math

Math LLMs have unique advantages in competition math, solving competition-level mathematical problems, generating solution ideas, and analyzing problem structure. Competition participants can receive powerful mathematical reasoning support, improving competition math capabilities. This is significant for improving competition performance and competition math level.

How to Choose a Math LLM

Select the right math LLM based on your mathematical task type, benchmark performance, mathematical reasoning requirements, symbolic computation capabilities, and cost budget to maximize your mathematical problem-solving capabilities and learning efficiency.

1. Evaluate Mathematical Task Type

Choose models based on task type: math education requires clear explanations and step-by-step solutions; research assistance needs advanced mathematical reasoning; theorem proving requires rigorous logical reasoning; competition math benefits from high accuracy and speed; Chinese math needs language-specific optimization. Select models providing corresponding mathematical capabilities based on task type.

2. Consider Benchmark Performance

Reference benchmark results: GSM8K tests grade school math problem-solving; MATH tests competition-level mathematical reasoning; AIME 2025 tests advanced competition math. Consider performance across benchmarks based on project needs: high scores indicate strong capabilities in specific mathematical domains.

3. Evaluate Mathematical Reasoning Requirements

If deep mathematical reasoning is needed, prioritize models with thinking capabilities that enable multi-step mathematical reasoning and deep analysis, excelling in complex mathematical tasks. If symbolic computation is needed, prioritize models with strong symbolic computation capabilities for algebraic manipulation. For fast math assistance scenarios, choose models optimized for specific languages or use cases.

4. Consider Language and Cost

If Chinese math understanding is needed, prioritize models optimized for Chinese with better performance for Chinese mathematical content. For English or other languages, choose models with strong multilingual capabilities. Choose plans based on usage frequency and budget: free versions suit small-scale use; paid versions suit large-scale use with higher limits and advanced features.

5. Test and Compare

Try 2-3 models first, testing performance in actual mathematical scenarios, comparing mathematical reasoning quality, response speed, and accuracy. Compare different models' performance in math education, research assistance, theorem proving, and other tasks. Continuously assess and optimize model selection based on project needs. Math LLMs should serve as collaborative partners, handling complex mathematical work, enabling users to focus on creativity and decision-making.

Conclusion

Math LLMs are advancing mathematical problem-solving capabilities, transforming how users approach math education and research. Tools like GPT-5.2, Gemini 3 Pro, and DeepSeek R1 provide exceptional mathematical assistance and efficiency improvements, enabling students, researchers, and professionals to solve complex mathematical problems more effectively.

Choose the right model based on your math needs: GPT-5.2 and Gemini 3 Pro Preview for math education, DeepSeek R1 and Claude Opus 4.5 Thinking for research assistance, DeepSeek R1 and Kimi K2 for Chinese math understanding. Evaluate problem complexity, language requirements, accuracy needs, and budget constraints to select the most suitable math LLM solution.

Math LLMs serve as collaborative partners, handling complex mathematical work, enabling users to focus on creativity and decision-making. The best approach is human-AI collaboration: AI manages mathematical computation and problem-solving, while users provide strategic thinking, verification, and application, maximizing both problem-solving efficiency and mathematical understanding.

Frequently Asked Questions

References

MATH Dataset. (2026). MATH Dataset: Measuring Mathematical Problem Solving. Retrieved from https://github.com/hendrycks/math - Competition-level math problem benchmark for assessing advanced mathematical reasoning capabilities.
WhatLLM. (2026). Best Math LLMs January 2026: Top AI Models for Mathematical Reasoning. Retrieved from https://whatllm.org/blog/best-math-models-january-2026 - January 2026 rankings and analysis of best math LLMs based on AIME 2025, GPQA Diamond, and other benchmarks.
LLMDB. (2026). GSM8K Benchmark. Retrieved from https://llmdb.com/benchmarks/gsm8k - Grade school math word problem benchmark for evaluating multi-step mathematical reasoning capabilities.

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