What are LLMs for Reasoning Tasks?
LLMs for reasoning tasks are specialized large language models designed to excel in logical thinking, mathematical problem-solving, and complex multi-step reasoning. These models use advanced training techniques like reinforcement learning and chain-of-thought processing to break down complex problems into manageable steps. They can handle mathematical proofs, coding challenges, scientific reasoning, and abstract problem-solving with unprecedented accuracy. This technology enables developers and researchers to build applications that require deep analytical thinking, from automated theorem proving to complex data analysis and scientific discovery.
DeepSeek-R1
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.
DeepSeek-R1: Premier Reasoning Performance
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 671B parameters using MoE architecture and 164K context length, it represents the pinnacle of reasoning model development.
Pros
- Performance comparable to OpenAI-o1 in reasoning tasks.
- Advanced reinforcement learning optimization.
- Massive 671B parameter MoE architecture.
Cons
- Higher computational requirements due to large size.
- Premium pricing at $2.18/M output tokens on SiliconFlow.
Why We Love It
- It delivers state-of-the-art reasoning performance with carefully designed RL training that rivals the best closed-source models.
Qwen/QwQ-32B
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.
Qwen/QwQ-32B: Efficient Reasoning 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).
Pros
- Competitive performance against larger reasoning models.
- Efficient 32B parameter size for faster deployment.
- Advanced attention architecture with GQA.
Cons
- Smaller context length (33K) compared to larger models.
- May not match the absolute peak performance of 671B models.
Why We Love It
- It offers the perfect balance of reasoning capability and efficiency, delivering competitive performance in a more accessible package.
DeepSeek-V3
The new version of DeepSeek-V3 (DeepSeek-V3-0324) utilizes the same base model as the previous DeepSeek-V3-1226, with improvements made only to the post-training methods. The new V3 model incorporates reinforcement learning techniques from the training process of the DeepSeek-R1 model, significantly enhancing its performance on reasoning tasks.
DeepSeek-V3: Enhanced Reasoning Powerhouse
The new version of DeepSeek-V3 (DeepSeek-V3-0324) utilizes the same base model as the previous DeepSeek-V3-1226, with improvements made only to the post-training methods. The new V3 model incorporates reinforcement learning techniques from the training process of the DeepSeek-R1 model, significantly enhancing its performance on reasoning tasks. It has achieved scores surpassing GPT-4.5 on evaluation sets related to mathematics and coding. Additionally, the model has seen notable improvements in tool invocation, role-playing, and casual conversation capabilities.
Pros
- Incorporates R1 reinforcement learning techniques.
- Scores surpassing GPT-4.5 in math and coding.
- Massive 671B MoE architecture with 131K context.
Cons
- High computational requirements for deployment.
- Premium pricing structure for enterprise use.
Why We Love It
- It combines the best of both worlds: exceptional reasoning capabilities inherited from R1 with strong general-purpose performance.
Reasoning AI Model Comparison
In this table, we compare 2025's leading reasoning AI models, each with unique strengths. For cutting-edge reasoning performance, DeepSeek-R1 leads the way. For efficient reasoning without compromise, QwQ-32B offers the best balance. For versatile reasoning combined with general capabilities, DeepSeek-V3 excels. This side-by-side view helps you choose the right reasoning model for your specific analytical and problem-solving needs.
| Number | Model | Developer | Subtype | Pricing (SiliconFlow) | Core Strength |
|---|---|---|---|---|---|
| 1 | DeepSeek-R1 | deepseek-ai | Reasoning | $2.18/M out, $0.5/M in | Premier reasoning performance |
| 2 | Qwen/QwQ-32B | QwQ | Reasoning | $0.58/M out, $0.15/M in | Efficient reasoning excellence |
| 3 | DeepSeek-V3 | deepseek-ai | General + Reasoning | $1.13/M out, $0.27/M in | Versatile reasoning + general tasks |
Frequently Asked Questions
Our top three picks for 2025 reasoning tasks are DeepSeek-R1, Qwen/QwQ-32B, and DeepSeek-V3. Each of these models stood out for their exceptional performance in logical reasoning, mathematical problem-solving, and complex multi-step thinking capabilities.
Our analysis shows DeepSeek-R1 leads in pure reasoning performance with capabilities comparable to OpenAI-o1. For cost-effective reasoning without sacrificing quality, QwQ-32B offers competitive performance in a more efficient package. For users needing both reasoning and general capabilities, DeepSeek-V3 provides the best combination of analytical thinking and versatile AI assistance.