# Reasoning

Claude 4 is the latest AI model series launched by Anthropic, featuring powerful programming and reasoning capabilities, capable of efficiently handling complex tasks. Its outstanding performance ranks it at the top in programming benchmark tests, becoming an essential tool for developers. Claude 4 has introduced several new functions, improving the efficiency and accuracy of information processing, making it ideal for users who need efficient coding and logical reasoning.

DeepSeek-Prover-V2-671B is an advanced artificial intelligence model designed to provide strong reasoning capabilities. It is based on the latest technology and applicable to various scenarios. The model is open source, aiming to promote the democratization and popularization of AI technology, reduce technical barriers, and enable more developers and researchers to use AI technology for innovation. By using this model, users can enhance their work efficiency and advance the progress of various projects.

This model improves the reasoning capabilities of diffusion large language models through reinforcement learning and masked self-supervised fine-tuning with high-quality reasoning trajectories. The importance of this technology lies in its ability to optimize the model's reasoning process, reduce computational costs, while ensuring the stability of learning dynamics. Suitable for users who want to improve efficiency in writing and reasoning tasks.

Kimi-VL is an advanced expert-mixed visual language model designed for multi-modal reasoning, long-context understanding, and powerful agent capabilities. This model excels in several complex domains, boasting efficient 2.8B parameters while exhibiting outstanding mathematical reasoning and image understanding capabilities. Kimi-VL sets a new standard for multi-modal models with its optimized computational performance and ability to handle long inputs.

The o1-pro model is an advanced AI language model designed for high-quality text generation and complex reasoning. It excels in reasoning and response accuracy, making it suitable for applications requiring high-precision text processing. The model's pricing is based on tokens used, with a price of $150 per million input tokens and $600 per million output tokens. It's ideal for enterprises and developers to integrate efficient text generation capabilities into their applications.

QwQ-32B is a reasoning model from the Qwen series, focusing on the ability to think and reason through complex problems. It excels in downstream tasks, especially in solving difficult problems. Based on the Qwen2.5 architecture, it has been optimized through pre-training and reinforcement learning, boasting 32.5 billion parameters and supporting a context length of up to 131,072 tokens. Its main advantages include powerful reasoning capabilities, efficient long-text processing capabilities, and flexible deployment options. This model is suitable for scenarios requiring deep thinking and complex reasoning, such as academic research, programming assistance, and creative writing.

QwQ-Max-Preview is the latest in the Qwen series, built on Qwen2.5-Max. It demonstrates enhanced capabilities in mathematics, programming, and general tasks, while also showing strong performance in workflows involving Agents. As a preview release for the upcoming QwQ-Max, this version is undergoing continuous optimization. Key advantages include its robust deep reasoning, mathematical capabilities, programming assistance, and Agent task handling. Future plans include open-sourcing QwQ-Max and Qwen2.5-Max under the Apache 2.0 license to foster innovation across diverse applications.

Claude 3.7 Sonnet is Anthropic's latest hybrid reasoning model, seamlessly switching between rapid response and deep reasoning. It excels in programming and front-end development, providing fine-grained control over reasoning depth via API. This model not only enhances code generation and debugging capabilities but also optimizes complex task handling, making it suitable for enterprise-level applications. Pricing is consistent with previous generations: $3 per million input tokens and $15 per million output tokens.

DeepHermes 3 is an advanced language model developed by NousResearch, designed to enhance answer accuracy through systematic reasoning. It supports both a reasoning mode and a regular response mode, which users can switch between using system prompts. This model excels in multi-turn conversations, role-playing, and reasoning tasks, aiming to provide users with more powerful and flexible language generation capabilities. The model is fine-tuned based on Llama-3.1-8B, has 8.03 billion parameters, and supports a variety of application scenarios, such as reasoning, dialogue, and function calling.

DeepSeek R1 and V3 APIs are powerful AI model interfaces provided by Kie.ai. DeepSeek R1 is the latest reasoning model designed specifically for advanced reasoning tasks such as mathematics, programming, and logical reasoning. Trained through large-scale reinforcement learning, it delivers precise results. DeepSeek V3 is suitable for handling routine AI tasks. These APIs are deployed on secure U.S.-based servers, ensuring data security and privacy. Kie.ai also offers detailed API documentation and a variety of pricing plans to meet diverse needs and help developers quickly integrate AI capabilities and improve project performance.

Grok 3 is the newest flagship AI model developed by Elon Musk's AI company, xAI. It represents a significant upgrade in computational power and dataset size, enabling it to handle complex mathematical and scientific problems and support multimodal input. Its primary advantage lies in its robust reasoning capabilities, providing more accurate answers and surpassing existing top-tier models in certain benchmarks. The launch of Grok 3 marks a further step in xAI's development in the AI field, aiming to provide users with more intelligent and efficient AI services. The model is currently primarily available through the Grok APP and X platform, with voice mode and enterprise API interfaces planned for the future. It is positioned as a high-end AI solution mainly targeted at users who require deep reasoning and multimodal interaction.

MedRAX is an innovative AI framework specifically developed for intelligent analysis of chest X-rays (CXR). By integrating cutting-edge CXR analysis tools with a multimodal large language model, it can dynamically handle complex medical queries. MedRAX operates without requiring additional training, supports real-time CXR interpretation, and is suitable for various clinical scenarios. Its primary advantages include high flexibility, strong reasoning capabilities, and a transparent workflow. This product targets healthcare professionals, aiming to improve diagnostic efficiency and accuracy while promoting the practical application of medical AI.

Confucius-o1-14B is an inference model developed by the NetEase Youdao team, optimized based on Qwen2.5-14B-Instruct. It employs a two-stage learning strategy that automatically generates reasoning chains and summarizes step-by-step problem-solving processes. This model is aimed at the education field, particularly suitable for K12 math problems, helping users quickly acquire correct problem-solving strategies and answers. Its lightweight nature allows it to be deployed on a single GPU without quantization, reducing the barrier to use. Its reasoning capabilities have demonstrated outstanding performance in internal evaluations, providing robust technical support for AI applications in education.

Developed by ByteDance, UI-TARS is a novel GUI agent model that focuses on seamless interactions with graphical user interfaces through human-like perception, reasoning, and action capabilities. This model integrates key components such as perception, reasoning, positioning, and memory into a single visual language model, enabling end-to-end task automation without predefined workflows or manual rules. Its primary advantages include robust cross-platform interaction capabilities, multi-step task execution, and the ability to learn from both synthetic and real data, making it suitable for a variety of automation scenarios like desktop, mobile, and web environments.

DeepSeek-R1-Distill-Llama-70B is a large language model developed by the DeepSeek team, based on the Llama-70B architecture and optimized through reinforcement learning. It excels in reasoning, dialogue, and multilingual tasks, supporting diverse applications such as code generation, mathematical reasoning, and natural language processing. Its primary advantages include efficient reasoning capabilities and problem-solving skills for complex tasks, while also supporting both open-source and commercial use. This model is suitable for enterprises and research institutions that require high-performance language generation and reasoning abilities.

Kimi k1.5, developed by MoonshotAI, is a multimodal language model that significantly enhances performance in complex reasoning tasks through reinforcement learning and long-context extension techniques. The model has achieved industry-leading results on several benchmark tests, surpassing GPT-4o and Claude Sonnet 3.5 in mathematical reasoning tasks such as AIME and MATH-500. Its primary advantages include an efficient training framework, strong multimodal reasoning capabilities, and support for long contexts. Kimi k1.5 is mainly aimed at application scenarios requiring complex reasoning and logical analysis, such as programming assistance, mathematical problem-solving, and code generation.

InternVL2.5-MPO is a series of multimodal large language models based on InternVL2.5 and Mixed Preference Optimization (MPO). It excels in multimodal tasks by integrating the recently incrementally pre-trained InternViT with various pre-trained large language models (LLMs) such as InternLM 2.5 and Qwen 2.5, utilizing a randomly initialized MLP projector. This model series has been trained on the multimodal reasoning preference dataset MMPR, which contains approximately 3 million samples, enhancing the model's reasoning capabilities and answer quality through an effective data construction process and mixed preference optimization techniques.

Developed by the InternLM team, the InternLM3-8B-Instruct is a large language model featuring exceptional reasoning capabilities and proficiency in knowledge-intensive tasks. Despite being trained with only 40 trillion high-quality tokens, it achieves over 75% lower training costs than similar models, while outperforming models such as Llama3.1-8B and Qwen2.5-7B on multiple benchmark tests. It supports deep reasoning modes that tackle complex inference tasks, while also offering smooth user interaction capabilities. The model is open-sourced under the Apache-2.0 license, making it suitable for various applications needing efficient reasoning and knowledge processing.

Eurus-2-7B-SFT is a large language model fine-tuned from the Qwen2.5-Math-7B model, aimed at enhancing mathematical reasoning and problem-solving abilities. The model learns reasoning patterns through imitation learning (supervised fine-tuning), effectively solving complex mathematical and programming tasks. Its main advantages lie in its powerful reasoning capabilities and accurate handling of mathematical problems, making it suitable for scenarios that require complex logical reasoning. Developed by the PRIME-RL team, the model aims to improve its reasoning capabilities through implicit rewards.

HuatuoGPT-o1-70B is a large language model (LLM) developed by FreedomIntelligence, specifically designed for complex medical reasoning. Before providing a final response, the model generates a detailed thought process that reflects and refines its reasoning. HuatuoGPT-o1-70B can handle intricate medical issues, providing thoughtful answers that are crucial for improving the quality and efficiency of medical decisions. The model is based on the LLaMA-3.1-70B architecture, supports English, and can be deployed on various tools like vllm or Sglang, or used for direct inference.

HuatuoGPT-o1-7B is a large language model (LLM) developed by FreedomIntelligence for the medical domain, specifically designed for advanced medical reasoning. The model generates complex reasoning processes before providing final answers, reflecting and refining its inference. HuatuoGPT-o1-7B supports both Chinese and English, handles complex medical queries, and outputs results in a 'thought-answer' format, which is crucial for improving the transparency and reliability of medical decisions. Based on Qwen2.5-7B, it has been specifically trained to meet the needs of the medical field.

HuatuoGPT-o1-8B is a large language model (LLM) specifically designed for advanced medical reasoning. Before providing a final response, it generates a complex thinking process that reflects and refines its reasoning. Built on the LLaMA-3.1-8B framework, it supports English language input and employs a 'thinks-before-it-answers' approach, producing outputs that include both the reasoning process and final response. This model is significant in the medical field as it can address complex medical issues and provide well-considered answers, critical for enhancing the quality and efficiency of medical decision-making.

The Gemini 2.0 Flash Thinking Mode is an experimental AI model launched by Google, designed to generate the 'thought process' of the model during its response. Compared to the basic Gemini 2.0 Flash model, the Thinking Mode demonstrates stronger reasoning abilities in its responses. This model is available in Google AI Studio and the Gemini API and represents a significant technological advancement in the field of artificial intelligence. It provides developers and researchers with a powerful tool to explore and implement complex AI applications.

Gemini 2.0 is the latest AI model launched by Google DeepMind, designed to support the 'smart assistant era.' This model boasts upgraded multimodal capabilities, including native image and audio output as well as tool utility, bringing the vision of a universal intelligent assistant closer to reality. The release of Gemini 2.0 signifies Google's deep exploration and continuous innovation in the AI field, providing enhanced information processing and output capabilities that make information more useful for users, resulting in a more efficient and convenient experience.

MAmmoTH-VL is a large-scale multimodal reasoning platform that significantly enhances the performance of multimodal large language models (MLLMs) on various multimodal tasks through instruction tuning techniques. The platform has created a dataset consisting of 12 million instruction-response pairs using open models, covering a wide range of reasoning-intensive tasks and providing detailed and accurate reasoning steps. MAmmoTH-VL has achieved state-of-the-art performance on benchmarks such as MathVerse, MMMU-Pro, and MuirBench, showcasing its importance in education and research.

Deepthought-8B is a compact yet powerful inference model constructed on the LLaMA-3.1 8B framework, designed to make AI reasoning more transparent and controllable. Despite its relatively small size, it achieves complex reasoning capabilities comparable to larger models. The model features a unique problem-solving methodology that breaks down its thought process into clear, distinct, and documented steps, outputting the reasoning process in a structured JSON format to facilitate understanding and verification of its decision-making.

Skywork-o1-Open-Llama-3.1-8B is a series of models developed by the Kunlun Technology Skywork team, integrating the slow thinking and reasoning capabilities characteristic of o1 style. This series showcases inherent thinking, planning, and reflective abilities in its outputs, alongside a significant enhancement in reasoning skills as evidenced by standard benchmark tests. This series represents a strategic advancement in AI capabilities, elevating a traditionally weaker foundational model to state-of-the-art performance in reasoning tasks.

QwQ-32B-Preview is an experimental research model developed by the Qwen team, aimed at improving AI reasoning capabilities. This model demonstrates promising analytical abilities, but it also has significant limitations. It excels in mathematics and programming; however, it has room for improvement in common-sense reasoning and nuanced language understanding. The model employs a transformer architecture with 32.5 billion parameters, 64 layers, and 40 attention heads (GQA). Background information reveals that QwQ-32B-Preview is a further development of the Qwen2.5-32B model, featuring enhanced language understanding and generation abilities.

DeepSeek-R1-Lite-Preview is an AI model focused on enhancing reasoning abilities, demonstrating impressive performance in the AIME and MATH benchmark tests. The model features a real-time transparent thought process and plans to release an open-source version and API. The reasoning capabilities of DeepSeek-R1-Lite-Preview improve steadily with longer thought durations, showcasing better performance. According to product background information, this latest offering from DeepSeek aims to enhance user work efficiency and problem-solving skills through AI technology. Currently, the product is available for free trial, with specific pricing and positioning details yet to be announced.

Mistral-Large-Instruct-2411 is a large language model provided by Mistral AI, featuring 123 billion parameters with cutting-edge abilities in reasoning, knowledge, and coding. It supports multiple languages and has been trained in over 80 programming languages, including but not limited to Python, Java, C, C++, and more. With a focus on agent-based interactions, it offers native function calls and JSON output capabilities, making it an ideal choice for research and development.