List of the Top AI Coding Models in China in 2026 - Page 3

DeepSeek-V4-Pro is a next-generation Mixture-of-Experts language model designed to deliver high performance across reasoning, coding, and long-context AI tasks. It features a massive architecture with 1.6 trillion total parameters and 49 billion activated parameters, enabling efficient computation while maintaining strong capabilities. The model supports an industry-leading context window of up to one million tokens, allowing it to process extremely large datasets, documents, and workflows. Its hybrid attention mechanism combines advanced techniques to optimize long-context efficiency and reduce computational requirements. DeepSeek-V4-Pro is trained on over 32 trillion tokens, enhancing its knowledge base and reasoning abilities. It incorporates advanced optimization methods to improve training stability and convergence. The model supports multiple reasoning modes, including fast responses and deep analytical thinking for complex problem solving. It performs strongly across benchmarks in coding, mathematics, and knowledge-based tasks. The architecture is designed for agentic workflows, enabling it to handle multi-step tasks and tool-based interactions. As an open-source model, it offers flexibility for customization and deployment across various environments. It also supports efficient memory usage and reduced inference costs compared to previous versions. The model’s capabilities make it suitable for both research and enterprise applications. Overall, DeepSeek-V4-Pro represents a significant advancement in scalable, high-performance AI with long-context intelligence.

DeepSeek-V4-Flash is a next-generation Mixture-of-Experts language model engineered for high efficiency, scalability, and long-context intelligence. It consists of 284 billion total parameters with 13 billion activated parameters, enabling optimized performance with reduced computational overhead. The model supports an industry-leading context window of up to one million tokens, allowing it to process extensive datasets and complex workflows seamlessly. Its hybrid attention architecture combines advanced techniques to improve long-context efficiency and reduce memory usage. DeepSeek-V4-Flash is trained on over 32 trillion tokens, enhancing its capabilities in reasoning, coding, and knowledge-based tasks. It incorporates advanced optimization methods for stable training and faster convergence. The model supports multiple reasoning modes, including fast responses and deeper analytical processing for complex problems. While slightly less powerful than its Pro counterpart, it achieves comparable reasoning performance when given more computation budget. It is designed for agentic workflows, enabling multi-step reasoning and tool-based interactions. The model is well-suited for scalable deployments where performance and cost efficiency are both important. As an open-source solution, it offers flexibility for customization across various environments. It also reduces inference cost and resource usage compared to larger models. Overall, DeepSeek-V4-Flash delivers a strong balance of speed, efficiency, and capability for real-world AI use cases.

StarCoder and StarCoderBase are sophisticated Large Language Models crafted for coding tasks, built from freely available data sourced from GitHub, which includes an extensive array of over 80 programming languages, along with Git commits, GitHub issues, and Jupyter notebooks. Similarly to LLaMA, these models were developed with around 15 billion parameters trained on an astonishing 1 trillion tokens. Additionally, StarCoderBase was specifically optimized with 35 billion Python tokens, culminating in the evolution of what we now recognize as StarCoder. Our assessments revealed that StarCoderBase outperforms other open-source Code LLMs when evaluated against well-known programming benchmarks, matching or even exceeding the performance of proprietary models like OpenAI's code-cushman-001 and the original Codex, which was instrumental in the early development of GitHub Copilot. With a remarkable context length surpassing 8,000 tokens, the StarCoder models can manage more data than any other open LLM available, thus unlocking a plethora of possibilities for innovative applications. This adaptability is further showcased by our ability to engage with the StarCoder models through a series of interactive dialogues, effectively transforming them into versatile technical aides capable of assisting with a wide range of programming challenges. Furthermore, this interactive capability enhances user experience, making it easier for developers to obtain immediate support and insights on complex coding issues.

We are excited to unveil the latest version of our open-source large language model, which includes model weights and initial code for the pretrained and fine-tuned Llama language models, ranging from 7 billion to 70 billion parameters. The Llama 2 pretrained models have been crafted using a remarkable 2 trillion tokens and boast double the context length compared to the first iteration, Llama 1. Additionally, the fine-tuned models have been refined through the insights gained from over 1 million human annotations. Llama 2 showcases outstanding performance compared to various other open-source language models across a wide array of external benchmarks, particularly excelling in reasoning, coding abilities, proficiency, and knowledge assessments. For its training, Llama 2 leveraged publicly available online data sources, while the fine-tuned variant, Llama-2-chat, integrates publicly accessible instruction datasets alongside the extensive human annotations mentioned earlier. Our project is backed by a robust coalition of global stakeholders who are passionate about our open approach to AI, including companies that have offered valuable early feedback and are eager to collaborate with us on Llama 2. The enthusiasm surrounding Llama 2 not only highlights its advancements but also marks a significant transformation in the collaborative development and application of AI technologies. This collective effort underscores the potential for innovation that can emerge when the community comes together to share resources and insights.

Code Llama is a sophisticated language model engineered to produce code from text prompts, setting itself apart as a premier choice among publicly available models for coding applications. This groundbreaking model not only enhances productivity for seasoned developers but also supports newcomers in tackling the complexities of learning programming. Its adaptability allows Code Llama to serve as both an effective productivity tool and a pedagogical resource, enabling programmers to develop more efficient and well-documented software. Furthermore, users can generate code alongside natural language explanations by inputting either format, which contributes to its flexibility for various programming tasks. Offered for free for both research and commercial use, Code Llama is based on the Llama 2 architecture and is available in three specific versions: the core Code Llama model, Code Llama - Python designed exclusively for Python development, and Code Llama - Instruct, which is fine-tuned to understand and execute natural language commands accurately. As a result, Code Llama stands out not just for its technical capabilities but also for its accessibility and relevance to diverse coding scenarios.

Yi-Large is a cutting-edge proprietary large language model developed by 01.AI, boasting an impressive context length of 32,000 tokens and a pricing model set at $2 per million tokens for both input and output. Celebrated for its exceptional capabilities in natural language processing, common-sense reasoning, and multilingual support, it stands out in competition with leading models like GPT-4 and Claude3 in diverse assessments. The model excels in complex tasks that demand deep inference, precise prediction, and thorough language understanding, making it particularly suitable for applications such as knowledge retrieval, data classification, and the creation of conversational chatbots that closely resemble human communication. Utilizing a decoder-only transformer architecture, Yi-Large integrates advanced features such as pre-normalization and Group Query Attention, having been trained on a vast, high-quality multilingual dataset to optimize its effectiveness. Its versatility and cost-effective pricing make it a powerful contender in the realm of artificial intelligence, particularly for organizations aiming to adopt AI technologies on a worldwide scale. Furthermore, its adaptability across various applications highlights its potential to transform how businesses utilize language models for an array of requirements, paving the way for innovative solutions in the industry. Thus, Yi-Large not only meets but also exceeds expectations, solidifying its role as a pivotal tool in the advancements of AI-driven communication.

Composer 1.5 stands as the latest coding model from Cursor, designed to significantly boost both speed and analytical capabilities for routine programming tasks, boasting an impressive 20-fold enhancement in reinforcement learning compared to its predecessor, which results in superior performance when addressing real-world coding challenges. This innovative model operates as a "thinking model," producing internal reasoning tokens that aid in evaluating a user's codebase and planning future actions, which allows it to respond quickly to simple problems while engaging in deeper reasoning for more complex issues. Furthermore, it ensures interactivity and efficiency, making it perfectly suited for everyday development workflows. To manage lengthy tasks, Composer 1.5 incorporates a self-summarization feature that enables the model to distill information and maintain context when it reaches certain limits, thereby ensuring accuracy across various input lengths. Internal assessments reveal that Composer 1.5 surpasses its earlier version in coding tasks, particularly shining in its ability to handle intricate challenges, which enhances its applicability for interactive solutions within Cursor's platform. Not only does this advancement represent a leap forward in coding assistance technology, but it also promises to significantly enhance the overall development experience for users, making it a vital tool for modern programmers.

Qwen3.6 is a next-generation large language model developed by Alibaba, designed to deliver advanced reasoning, coding, and multimodal capabilities. It builds on the Qwen3.5 series with a strong emphasis on stability, efficiency, and real-world usability. The model supports multimodal inputs, enabling it to process text, images, and video for more complex analysis and decision-making. One of its key strengths is agentic AI, allowing it to perform multi-step tasks and operate more autonomously in workflows. Qwen3.6 is particularly optimized for coding, capable of handling complex engineering tasks at a repository level rather than just individual functions. It uses a mixture-of-experts architecture, with billions of parameters but only a subset activated during each inference, improving efficiency. The model is available in both open-weight and proprietary versions, giving developers flexibility in deployment and customization. It can be integrated into enterprise systems, APIs, and cloud environments for production use. Qwen3.6 also offers strong multimodal reasoning, enabling it to analyze documents, visuals, and structured data together. It is designed to support a wide range of applications, from software development to data analysis and automation. The model includes enhancements in performance, scalability, and usability compared to earlier versions. It reflects a broader shift toward agent-based AI systems that can execute tasks rather than just provide responses. Overall, Qwen3.6 represents a powerful and versatile AI model for modern enterprise and developer use cases.

PaLM 2 marks a significant advancement in the realm of large language models, furthering Google's legacy of leading innovations in machine learning and ethical AI initiatives. This model showcases remarkable skills in intricate reasoning tasks, including coding, mathematics, classification, question answering, multilingual translation, and natural language generation, outperforming earlier models, including its predecessor, PaLM. Its superior performance stems from a groundbreaking design that optimizes computational scalability, incorporates a carefully curated mixture of datasets, and implements advancements in the model's architecture. Moreover, PaLM 2 embodies Google’s dedication to responsible AI practices, as it has undergone thorough evaluations to uncover any potential risks, biases, and its usability in both research and commercial contexts. As a cornerstone for other innovative applications like Med-PaLM 2 and Sec-PaLM, it also drives sophisticated AI functionalities and tools within Google, such as Bard and the PaLM API. Its adaptability positions it as a crucial resource across numerous domains, demonstrating AI's capacity to boost both productivity and creative solutions, ultimately paving the way for future advancements in the field.

Xiaomi MiMo-V2.5-Pro is a cutting-edge open-source AI model built to handle complex reasoning, coding, and long-horizon tasks with high efficiency. It features a Mixture-of-Experts architecture with over one trillion total parameters and a large active parameter set for optimized performance. The model supports an extended context window of up to one million tokens, enabling it to process large amounts of information in a single workflow. It is designed for advanced agentic capabilities, allowing it to autonomously complete multi-step tasks over extended periods. MiMo-V2.5-Pro has demonstrated strong results in benchmarks related to software engineering, reasoning, and general AI performance. It is capable of building complete applications, optimizing engineering systems, and solving complex technical challenges. The model uses hybrid attention mechanisms to balance performance and efficiency across long contexts. It is also optimized for token efficiency, reducing resource usage while maintaining high-quality outputs. The model can integrate with development tools and frameworks to support real-world use cases. Xiaomi has open-sourced MiMo-V2.5-Pro, providing developers with access to its architecture, weights, and deployment tools. This allows organizations to customize and scale the model for their specific needs. Its ability to handle long workflows makes it suitable for tasks that require sustained reasoning and coordination. By combining scalability, efficiency, and advanced intelligence, MiMo-V2.5-Pro represents a significant advancement in open-source AI technology.

Xiaomi MiMo-V2.5 is a powerful open-source AI model designed to deliver advanced agentic capabilities alongside native multimodal understanding. It can process and reason across text, images, and audio within a unified system, enabling more complex and realistic interactions. The model is built using a sparse Mixture-of-Experts architecture with hundreds of billions of parameters, allowing it to scale efficiently while maintaining strong performance. It supports an extended context window of up to one million tokens, making it suitable for long-horizon tasks and detailed workflows. MiMo-V2.5 incorporates dedicated visual and audio encoders that enhance its ability to interpret and analyze multimodal inputs. It is capable of performing a wide range of tasks, including coding, reasoning, document analysis, and multimedia understanding. The model demonstrates strong benchmark performance across coding, reasoning, and multimodal evaluation tests. It is optimized for token efficiency, reducing computational cost while maintaining high-quality outputs. MiMo-V2.5 is designed to integrate with development tools and frameworks for real-world use cases. Xiaomi has released the model as open source, providing access to its weights, tokenizer, and architecture. This allows developers to customize and deploy the model for specific applications. Its ability to combine perception and reasoning makes it suitable for advanced AI workflows. By unifying multimodality and agentic intelligence, MiMo-V2.5 represents a significant advancement in open-source AI technology.