# Reasoning Model

HunYuan T1 is a large-scale reasoning model launched by Tencent, based on reinforcement learning technology, significantly improving reasoning capabilities through extensive post-training. It excels in long text processing and context capture, while optimizing the consumption of computing resources, thus possessing efficient reasoning capabilities. It is suitable for various reasoning tasks, and particularly excels in mathematics and logical reasoning. This product is based on deep learning and continuously optimized with actual feedback, suitable for applications in various fields such as scientific research and education.

HunYuan T1 is a deep reasoning large model based on reinforcement learning, launched by Tencent. Through extensive post-training and alignment with human preferences, it significantly improves reasoning ability and efficiency. The product is based on a large-scale Hybrid-Transformer-Mamba MoE architecture, enabling the model to perform better when handling long texts. Suitable for various users who need complex reasoning and logical solutions, assisting scientific research and technological development.

HunYuan T1 is an ultra-large-scale reasoning model based on reinforcement learning. Post-training significantly improves reasoning ability and aligns with human preferences. This model focuses on long-text processing and complex reasoning tasks, exhibiting significant performance advantages.

Reka Flash 3 is a 2.1 billion parameter general-purpose reasoning model trained from scratch, using synthetic and public datasets for supervised fine-tuning, combined with model-based and rule-based rewards for reinforcement learning. This model excels in low-latency and on-device deployment applications and possesses strong research capabilities. It is currently the best choice among similar open-source models and is suitable for various natural language processing tasks and application scenarios.

EXAONE Deep is an advanced reasoning AI model launched by LG AI Research, signifying Korea's competitiveness in the global AI market. With 3.2 billion parameters, it demonstrates outstanding performance, particularly in solving mathematical and scientific problems. The release of this model marks LG's entry into the era of autonomous decision-making in the AI field, and its open-source nature allows more developers to utilize this technology for research and development. EXAONE Deep's lightweight design and on-device model make it suitable for multiple industries, including education, scientific research, and programming.

Steiner is a series of reasoning models developed by Yichao 'Peak' Ji, focusing on training on synthetic data through reinforcement learning, capable of exploring multiple paths and autonomously verifying or retracing during reasoning. The model aims to replicate the reasoning capabilities of OpenAI o1 and verify the scaling curve during reasoning. Steiner-preview is an ongoing project, and its open-source nature aims to share knowledge and obtain feedback from more real users. Although the model performs well in some benchmark tests, it has not yet fully achieved the reasoning scaling capabilities of OpenAI o1 and is therefore still under development.

OpenThinker-32B is an open-source reasoning model developed by the Open Thoughts team. It achieves robust reasoning capabilities by expanding data scale, validating reasoning paths, and scaling model size. The model excels in reasoning benchmarks for mathematics, code, and science, surpassing existing open data reasoning models. Its key advantages include open-source data, high performance, and scalability. The model is fine-tuned based on Qwen2.5-32B-Instruct and trained on a large-scale dataset, aiming to provide researchers and developers with a powerful reasoning tool.

DeepSeek-R1-Distill-Qwen-7B is a reinforcement learning-optimized reasoning model distilled from Qwen-7B. It excels in mathematical, coding, and reasoning tasks, generating high-quality reasoning chains and solutions. This model significantly enhances reasoning capabilities and efficiency through large-scale reinforcement learning and data distillation techniques, making it suitable for scenarios requiring complex reasoning and logical analysis.

InternThinker is a powerful reasoning AI model developed by the Shanghai Artificial Intelligence Laboratory (Shanghai AI Lab), aiming to explore open, controllable, and reliable General Artificial Intelligence (AGI) through a 'fusion of general and specialized knowledge' approach. The model exhibits long-chain thinking capabilities and can engage in self-reflection and correction during the reasoning process, achieving superior results in various complex reasoning tasks such as mathematics, coding, and reasoning puzzles. Its innovation lies in its metacognitive capabilities, allowing it to autonomously generate high-density intellectual data and gather feedback in large-scale sandbox environments, leading to the independent construction of high-quality thinking chains and significantly enhancing the model's performance in complex task handling.