Qwen2.5-Math

Categories: Coding & Developer Tools, Research, Education | Pricing: Free | Official Website ↗

Qwen2.5-Math is an open-source series of large language models specialized in solving mathematical problems in English and Chinese.

Qwen2.5-Math is an upgraded series of open-source mathematical large language models (LLMs) from the Qwen family, building upon the previous Qwen2-Math. It includes base models (1.5B, 7B, 72B), instruction-tuned models (1.5B, 7B, 72B-Instruct), and a mathematical reward model (72B-RM). These models are designed to solve math problems using both Chain-of-Thought (CoT) and Tool-integrated Reasoning (TIR) in English and Chinese. The series demonstrates significant performance improvements on various mathematical benchmarks, including GSM8K, MATH, MMLU-STEM, CMATH, and GaoKao Math. The models leverage synthesized high-quality mathematical pre-training data, aggregated data from web sources, books, and code, and are initialized with the Qwen2.5 series base model. The instruction-tuned models are further refined using a math-specific reward model and reinforcement learning techniques. A demo is available that supports TIR mode via Qwen-Agent and a multi-modal demo on Huggingface and Modelscope for image, text, or sketch input.

Key Features

Mathematical problem-solving
Supports Chain-of-Thought (CoT)
Supports Tool-integrated Reasoning (TIR)
Bilingual support (English and Chinese)
Base models (1.5B, 7B, 72B)
Instruction-tuned models (1.5B, 7B, 72B-Instruct)
Mathematical reward model (72B-RM)
Multi-modal math demo (image, text, sketch input)

Pros

Achieves high scores on challenging mathematical benchmarks (e.g., MATH, AIME, AMC)
Outperforms many open-source and some leading closed-source models (e.g., GPT-4o, Gemini Math-Specialized 1.5 Pro)
Supports both Chain-of-Thought and Tool-integrated Reasoning for enhanced accuracy
Bilingual support for English and Chinese math problems
Open-source, allowing for broader access and development

Cons

Primarily designed for mathematical tasks; not recommended for other general LLM tasks
Performance can vary significantly across different model sizes (1.5B vs 72B)
Requires specific prompting techniques (CoT, TIR) for optimal performance
The blog post is a technical announcement, not a user-facing product page.

Use Cases

Solving complex mathematical problems
Developing AI agents with precise computational abilities
Benchmarking mathematical reasoning in LLMs
Educational tools for math problem-solving

Best For

AI researchers
Developers building math-focused AI applications
Educators and students in STEM fields
Anyone needing advanced mathematical reasoning capabilities in LLMs

Integrations: Hugging Face, ModelScope, Qwen-Agent

Platforms: Web

Watch demo on YouTube ↗

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