What are Open Source LLMs for Math?
Open source LLMs for math are specialized Large Language Models designed to excel at mathematical reasoning, problem-solving, and computational tasks. Using advanced deep learning architectures and reinforcement learning techniques, they can understand complex mathematical concepts, solve equations, prove theorems, and explain step-by-step solutions. These models leverage reasoning capabilities through techniques like Chain-of-Thought (CoT) prompting and are trained on extensive mathematical datasets. They foster collaboration, accelerate innovation in mathematical AI, and democratize access to powerful computational tools, enabling a wide range of applications from educational platforms to advanced scientific research and engineering solutions.
DeepSeek-R1
DeepSeek-R1-0528 is a reasoning model powered by reinforcement learning (RL) that addresses the issues of repetition and readability. It achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks. With 671B total parameters in its MoE architecture and 164K context length, it delivers state-of-the-art mathematical reasoning capabilities through carefully designed training methods.
DeepSeek-R1: Elite Mathematical Reasoning Power
DeepSeek-R1-0528 is a reasoning model powered by reinforcement learning (RL) that addresses the issues of repetition and readability. Prior to RL, DeepSeek-R1 incorporated cold-start data to further optimize its reasoning performance. It achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks, and through carefully designed training methods, it has enhanced overall effectiveness. With a massive 671B total parameters using Mixture-of-Experts architecture and 164K context length, this model represents the pinnacle of open-source mathematical reasoning, making it ideal for complex mathematical proofs, multi-step problem solving, and advanced computational tasks.
Pros
- Performance comparable to OpenAI-o1 in mathematical reasoning.
- Massive 671B MoE architecture with 164K context length.
- Enhanced through reinforcement learning for optimal reasoning.
Cons
- Requires significant computational resources.
- Higher pricing at $2.18/M output tokens on SiliconFlow.
Why We Love It
- It delivers OpenAI-o1 level mathematical reasoning performance as an open-source model, making elite-level mathematical AI accessible to researchers and developers worldwide.
Qwen/QwQ-32B
QwQ-32B is the medium-sized reasoning model from the Qwen series, specifically designed for thinking and reasoning tasks. It achieves competitive performance against state-of-the-art reasoning models like DeepSeek-R1 and o1-mini, with 32B parameters and 33K context length. The model demonstrates significantly enhanced performance in mathematical problems and hard reasoning tasks.
Qwen/QwQ-32B: Balanced Mathematical Excellence
QwQ is the reasoning model of the Qwen series. Compared with conventional instruction-tuned models, QwQ, which is capable of thinking and reasoning, can achieve significantly enhanced performance in downstream tasks, especially hard problems. QwQ-32B is the medium-sized reasoning model, which is capable of achieving competitive performance against state-of-the-art reasoning models, e.g., DeepSeek-R1, o1-mini. The model incorporates technologies like RoPE, SwiGLU, RMSNorm, and Attention QKV bias, with 64 layers and 40 Q attention heads (8 for KV in GQA architecture). At 32B parameters, it offers an excellent balance between mathematical reasoning power and computational efficiency, making it ideal for complex mathematical tasks without requiring massive infrastructure.
Pros
- Competitive with state-of-the-art reasoning models.
- Excellent balance of performance and efficiency at 32B.
- Advanced architecture with RoPE, SwiGLU, and RMSNorm.
Cons
- Smaller context window (33K) compared to larger models.
- May not match the absolute peak performance of 671B models.
Why We Love It
- It delivers near-flagship mathematical reasoning performance at a fraction of the computational cost, making advanced mathematical AI accessible for mid-scale deployments.
THUDM/GLM-Z1-9B-0414
GLM-Z1-9B-0414 is a compact 9B parameter model that excels in mathematical reasoning despite its smaller scale. It exhibits excellent performance in mathematical reasoning and general tasks, achieving leading results among open-source models of the same size. The model features deep thinking capabilities and supports long contexts through YaRN technology, making it ideal for mathematical applications with limited computational resources.
THUDM/GLM-Z1-9B-0414: Lightweight Mathematical Champion
GLM-Z1-9B-0414 is a small-sized model in the GLM series with only 9 billion parameters that maintains the open-source tradition while showcasing surprising capabilities. Despite its smaller scale, GLM-Z1-9B-0414 still exhibits excellent performance in mathematical reasoning and general tasks. Its overall performance is already at a leading level among open-source models of the same size. The research team employed the same series of techniques used for larger models to train this 9B model. Especially in resource-constrained scenarios, this model achieves an excellent balance between efficiency and effectiveness, providing a powerful option for users seeking lightweight deployment. The model features deep thinking capabilities and can handle long contexts through YaRN technology, making it particularly suitable for applications requiring mathematical reasoning abilities with limited computational resources.
Pros
- Outstanding mathematical reasoning for only 9B parameters.
- Deep thinking capabilities with YaRN technology.
- Leading performance among models of similar size.
Cons
- Limited to 33K context length.
- May struggle with extremely complex multi-step proofs.
Why We Love It
- It proves that exceptional mathematical reasoning doesn't require massive models, delivering impressive performance in a lightweight package perfect for edge deployment and resource-constrained environments.
Mathematical LLM Comparison
In this table, we compare 2025's leading open-source LLMs for mathematical reasoning, each with unique strengths. DeepSeek-R1 offers elite-level performance comparable to OpenAI-o1, QwQ-32B provides the best balance of capability and efficiency, while GLM-Z1-9B-0414 delivers surprising mathematical prowess in a lightweight package. This side-by-side comparison helps you choose the right mathematical AI tool for your specific computational requirements and resource constraints, with pricing from SiliconFlow.
| Number | Model | Developer | Subtype | Pricing (SiliconFlow) | Core Strength |
|---|---|---|---|---|---|
| 1 | DeepSeek-R1 | deepseek-ai | Reasoning Model | $2.18/M output tokens | Elite o1-level math reasoning |
| 2 | Qwen/QwQ-32B | Qwen | Reasoning Model | $0.58/M output tokens | Optimal performance-efficiency balance |
| 3 | THUDM/GLM-Z1-9B-0414 | THUDM | Reasoning Model | $0.086/M tokens | Lightweight mathematical excellence |
Frequently Asked Questions
Our top three picks for best open source LLM for math in 2025 are DeepSeek-R1, Qwen/QwQ-32B, and THUDM/GLM-Z1-9B-0414. Each of these models stood out for their exceptional mathematical reasoning capabilities, innovation in training techniques, and unique approach to solving complex mathematical problems. DeepSeek-R1 delivers OpenAI-o1 comparable performance, QwQ-32B offers the best balance, and GLM-Z1-9B-0414 proves that lightweight models can excel at mathematical reasoning.
Our in-depth analysis reveals specific leaders for different mathematical needs. For absolute peak performance on the most complex mathematical proofs and research-level problems, DeepSeek-R1 with its 671B MoE architecture is the top choice. For production deployments requiring excellent mathematical reasoning with balanced resource requirements, QwQ-32B is ideal. For educational applications, mobile deployment, or resource-constrained environments where mathematical reasoning is still critical, GLM-Z1-9B-0414 delivers impressive capabilities at minimal computational cost, priced at just $0.086/M tokens on SiliconFlow.