Best AI Coding Models for Claude Code

Claude is an advanced AI assistant created by Anthropic to help users think, create, and work more efficiently. It is built to handle tasks such as content creation, document editing, coding, data analysis, and research with a strong focus on safety and accuracy. Claude enables users to collaborate with AI in real time, making it easy to draft websites, generate code, and refine ideas through conversation. The platform supports uploads of text, images, and files, allowing users to analyze and visualize information directly within chat. Claude includes powerful tools like Artifacts, which help organize and iterate on creative and technical projects. Users can access Claude on the web as well as on mobile devices for seamless productivity. Built-in web search allows Claude to surface relevant information when needed. Different plans offer varying levels of usage, model access, and advanced research features. Claude is designed to support both individual users and teams at scale. Anthropic’s commitment to responsible AI ensures Claude is secure, reliable, and aligned with real-world needs.

Kimi K2.7 Code is a Moonshot AI coding model built to help developers handle software engineering, code generation, debugging, and agent-based development workflows. It focuses on long-horizon coding tasks, where an AI assistant needs to understand goals, work across many files, and complete multi-step development work. The model builds on the Kimi K2.6 architecture and is described as improving agentic capabilities while reducing thinking-token usage by about 30% compared with K2.6. Kimi K2.7 Code offers a 256K context window, which helps developers work with larger repositories, longer prompts, and more detailed project instructions. It can be accessed through Kimi Code, Moonshot’s API platform, and third-party model providers such as Together AI. The model also supports OpenAI- and Anthropic-compatible APIs, making it easier for teams to test it as a replacement or addition to existing coding assistant workflows. Developers who want to self-host or experiment with the model can access it through Hugging Face, where deployment guidance references vLLM, SGLang, and KTransformers. Kimi K2.7 Code is especially relevant for teams interested in open-source coding agents, long-context software tasks, and tool-integrated development. While some third-party commentary notes that benchmark claims should be reviewed carefully, the model is positioned as a strong option for developers seeking flexible, agentic coding support.

Claude Sonnet 3.7, a state-of-the-art AI model by Anthropic, is designed for versatility, offering users the option to switch between quick, efficient responses and deeper, more reflective answers. This dynamic model shines in complex problem-solving scenarios, where high-level reasoning and nuanced understanding are crucial. By allowing Claude to pause for self-reflection before answering, Sonnet 3.7 excels in tasks that demand deep analysis, such as coding, natural language processing, and critical thinking applications. Its flexibility makes it an invaluable tool for professionals and organizations looking for an adaptable AI that delivers both speed and thoughtful insights.

Claude Opus 4 is the pinnacle of AI coding models, leading the way in software engineering tasks with an impressive SWE-bench score of 72.5% and Terminal-bench score of 43.2%. Its ability to handle complex challenges, large codebases, and multiple files simultaneously sets it apart from all other models. Opus 4 excels at coding tasks that require extended focus and problem-solving, automating tasks for software developers, engineers, and data scientists. This AI model doesn’t just perform—it continuously improves its capabilities over time, handling real-world challenges and optimizing workflows with confidence. Available through multiple platforms like Anthropic API, Amazon Bedrock, and Gemini Enterprise Agent Platform, Opus 4 is a must-have for cutting-edge developers and businesses looking to stay ahead.

Claude Opus 4.7 is an advanced AI model built to push the boundaries of software engineering, automation, and complex reasoning tasks. Compared to Opus 4.6, it delivers notable improvements in handling challenging coding workflows and executing long-duration tasks with consistency. The model excels at strictly following user instructions, reducing ambiguity and improving output accuracy. It also introduces stronger self-verification capabilities, allowing it to check and refine its own results before presenting them. One of its key upgrades is enhanced multimodal functionality, particularly its ability to process higher-resolution images with greater clarity. This enables more precise analysis of visuals such as technical diagrams, dense screenshots, and structured data layouts. Opus 4.7 is also more refined in generating professional content, including polished documents, presentations, and interface designs. In real-world applications, it performs effectively across domains like finance, legal analysis, and business workflows. The model incorporates improved memory features, allowing it to retain context across extended sessions and reduce repetitive input requirements. It also introduces built-in safeguards to detect and prevent misuse, especially in sensitive cybersecurity scenarios. With broad availability across APIs and cloud platforms, Opus 4.7 offers developers and enterprises a powerful, scalable AI solution.

Claude Opus 4.8 is Anthropic’s newest flagship AI model built to improve coding performance, reasoning accuracy, agentic task execution, and collaborative AI workflows for developers, enterprises, and advanced productivity use cases. The model serves as an upgrade to Claude Opus 4.7, delivering measurable improvements across benchmarks related to coding, practical reasoning, software engineering, and autonomous task management while maintaining the same pricing structure for standard usage. One of the most significant improvements in Claude Opus 4.8 is its enhanced honesty and judgment during complex tasks, reducing the likelihood of unsupported claims, hidden errors, or overlooked flaws in generated code and analytical outputs. Anthropic’s evaluations show that Opus 4.8 is substantially less likely than previous versions to allow software defects or reasoning mistakes to pass without flagging uncertainty or requesting clarification. The platform introduces new effort control settings that allow users to adjust how deeply the model reasons through tasks, balancing response quality, processing depth, speed, and token usage depending on workflow requirements. Claude Opus 4.8 also powers new dynamic workflow functionality in Claude Code, enabling the model to coordinate hundreds of parallel subagents within a single session to handle large-scale software engineering tasks such as codebase migrations and extensive automation projects. The model supports high-speed fast mode processing, now significantly more affordable than previous versions, while also offering higher-effort reasoning modes optimized for difficult coding and operational workflows.

Claude Fable 5 is Anthropic’s most capable generally available AI model, built to tackle demanding tasks across software development, research, business analysis, scientific exploration, and enterprise productivity. The model demonstrates state-of-the-art performance in coding, reasoning, visual understanding, long-context processing, and autonomous task execution. Claude Fable 5 can analyze large codebases, interpret complex documents and datasets, generate detailed reports, and assist with advanced decision-making processes. Its enhanced memory capabilities allow it to remain effective during long-running workflows and multi-step projects. The model also delivers strong performance in image analysis, chart interpretation, scientific reasoning, and technical problem-solving. Anthropic has incorporated advanced safety classifiers that detect certain high-risk topics and automatically redirect those interactions to a more restricted model experience. These safeguards are designed to reduce misuse while still providing productive assistance for legitimate users. Claude Fable 5 is available through the Claude platform and API, enabling developers and organizations to integrate advanced AI capabilities into their applications and workflows. The platform is designed to help businesses improve productivity, accelerate innovation, and streamline complex knowledge work.

Claude Mythos 5 is a frontier AI model from Anthropic created for highly trusted users working on advanced cybersecurity, infrastructure protection, and scientific research. It is based on the same core model as Claude Fable 5, but certain safeguards are lifted for approved partners operating under restricted access programs. The model offers exceptional performance across software engineering, cybersecurity analysis, autonomous development workflows, scientific reasoning, visual understanding, and long-context tasks. In cybersecurity, Claude Mythos 5 is positioned for cyberdefenders and critical infrastructure providers who need advanced AI support for securing complex systems. In life sciences, the model has demonstrated strong capabilities in drug design, protein research, molecular biology, and genomics. Claude Mythos 5 can perform long-running research and technical workflows with minimal high-level human input. Anthropic designed the model for controlled deployment because its advanced capabilities could create misuse risks if broadly available without safeguards. Access is initially limited to Project Glasswing partners, with broader trusted access programs planned for cybersecurity and select biology researchers. Claude Mythos 5 helps approved organizations apply powerful AI to high-impact technical and scientific challenges while operating within a stricter governance model.

GLM-5.2 is a next-generation large language model built for users who need strong reasoning, coding support, and agentic AI capabilities. It can assist with complex software development tasks, technical problem-solving, automation workflows, and advanced research projects. The model is designed to process long-context information, which makes it helpful for analyzing large documents, reviewing codebases, and maintaining continuity across multi-step tasks. GLM-5.2 supports developers and organizations that want to create AI-powered tools capable of planning, reasoning, and executing more sophisticated workflows. Its architecture is structured to deliver high performance while improving efficiency for demanding AI use cases. Businesses can use GLM-5.2 to enhance productivity, streamline engineering processes, and build more capable intelligent applications. It is also useful for teams that need AI assistance across documentation, data interpretation, coding, testing, and workflow automation. The model’s emphasis on agentic engineering makes it well-suited for applications that require more than simple text generation. GLM-5.2 provides a flexible AI foundation for companies looking to bring advanced reasoning and automation into their products or internal operations.

Claude Opus 4.6 is a state-of-the-art AI model from Anthropic, designed to deliver advanced reasoning, coding, and enterprise-level performance. It improves significantly on previous versions with better planning, debugging, and code review capabilities. The model can sustain long-running, agentic workflows and operate effectively across large codebases. One of its key features is a 1 million token context window in beta, allowing it to handle extensive documents and complex tasks. Claude Opus 4.6 excels in knowledge work, including financial analysis, research, and document creation. It also performs strongly on industry benchmarks, leading in areas like agentic coding and multidisciplinary reasoning. The model includes adaptive thinking, enabling it to adjust its reasoning depth based on task complexity. Developers can control performance using adjustable effort levels for speed, cost, and accuracy. It integrates with productivity tools such as Excel and PowerPoint for enhanced workflow automation. Overall, Claude Opus 4.6 provides a powerful and reliable AI solution for professional and enterprise use cases.

Claude Sonnet 4.6 represents a comprehensive upgrade to Anthropic’s Sonnet model line, delivering expanded capabilities across coding, reasoning, computer interaction, and professional knowledge tasks. With a beta 1M token context window, the model can process massive datasets such as full repositories, extended legal agreements, or multi-document research projects in a single request. Developers report improved reliability, better instruction adherence, and fewer hallucinations, making long working sessions smoother and more predictable. Early users preferred Sonnet 4.6 over its predecessor in the majority of tests and often selected it over Opus 4.5 for practical coding work. The model’s computer-use skills have advanced significantly, enabling it to navigate spreadsheets, complete web forms, and manage multi-tab workflows with near human-level competence in many cases. Benchmark evaluations show consistent performance gains across reasoning, coding, and long-horizon planning tasks. In competitive simulations like Vending-Bench Arena, Sonnet 4.6 demonstrated strategic capacity-building and profit optimization over time. On the developer platform, it supports adaptive and extended thinking modes, context compaction, and improved tool integration for greater efficiency. Claude’s API tools now automatically execute filtering and code-processing steps to enhance search and token optimization. Sonnet 4.6 is available across Claude.ai, Cowork, Claude Code, the API, and major cloud providers at the same starting price as Sonnet 4.5.

GLM-5 is a next-generation open-source foundation model from Z.ai designed to push the boundaries of agentic engineering and complex task execution. Compared to earlier versions, it significantly expands parameter count and training data, while introducing DeepSeek Sparse Attention to optimize inference efficiency. The model leverages a novel asynchronous reinforcement learning framework called slime, which enhances training throughput and enables more effective post-training alignment. GLM-5 delivers leading performance among open-source models in reasoning, coding, and general agent benchmarks, with strong results on SWE-bench, BrowseComp, and Vending Bench 2. Its ability to manage long-horizon simulations highlights advanced planning, resource allocation, and operational decision-making skills. Beyond benchmark performance, GLM-5 supports real-world productivity by generating fully formatted documents such as .docx, .pdf, and .xlsx files. It integrates with coding agents like Claude Code and OpenClaw, enabling cross-application automation and collaborative agent workflows. Developers can access GLM-5 via Z.ai’s API, deploy it locally with frameworks like vLLM or SGLang, or use it through an interactive GUI environment. The model is released under the MIT License, encouraging broad experimentation and adoption. Overall, GLM-5 represents a major step toward practical, work-oriented AI systems that move beyond chat into full task execution.

GLM-5.1 represents the latest advancement in Z.ai’s GLM series, crafted as a cutting-edge, agent-focused AI model tailored for coding, reasoning, and managing long-term workflows. This iteration builds upon the framework of GLM-5, which employs a Mixture-of-Experts (MoE) architecture to achieve high performance without incurring excessive inference expenses, aligning with a larger initiative towards open-weight models that are accessible to developers. A significant emphasis of GLM-5.1 is on fostering agentic behavior, allowing it to plan, execute, and refine multi-step tasks instead of merely reacting to isolated prompts. Its capabilities are specifically engineered to manage intricate workflows, such as debugging code, exploring repositories, and performing sequential operations while maintaining context over time. In comparison to its predecessors, GLM-5.1 enhances reliability during lengthy interactions, ensuring coherence throughout extended sessions and minimizing failures in multi-step reasoning processes. Overall, this model signifies a leap forward in AI development, particularly in its ability to support complex task management seamlessly.

Kimi K2.6 is an advanced agentic AI model created by Moonshot AI, aiming to enhance practical implementation, programming, and complex reasoning compared to its predecessors, K2 and K2.5. This model is based on a Mixture-of-Experts framework and the multimodal, agent-centric principles of the Kimi series, merging language comprehension, coding capabilities, and tool utilization into one cohesive system that can plan and execute intricate workflows. It features enhanced reasoning skills and significantly better agent planning, enabling it to deconstruct tasks, synchronize various tools, and tackle multi-file or multi-step challenges with increased precision and effectiveness. Additionally, it provides robust tool-calling capabilities with a high degree of reliability, facilitating seamless integration with external platforms like web searches or APIs, and incorporates built-in validation systems to guarantee the accuracy of execution formats. Notably, Kimi K2.6 represents a significant leap forward in the realm of AI, setting new standards for the complexity and reliability of automated tasks.

MiniMax M3 is a frontier open-weight AI model built for coding, agentic work, multimodal understanding, and ultra-long-context tasks. The model supports up to a 1 million token context window, allowing it to work across large codebases, long documents, logs, project histories, and complex task environments. MiniMax M3 introduces MiniMax Sparse Attention, a sparse attention architecture designed to make long-context processing more efficient. The model is natively multimodal, with training that supports deeper semantic fusion across text, image, and video inputs. It is designed to support software engineering tasks, repository analysis, terminal-style work, browser-style retrieval, tool use, and autonomous workflows. MiniMax M3 has a mixture-of-experts architecture with hundreds of billions of total parameters and a smaller activated parameter count for more efficient inference. Developers can use it for AI coding assistants, workflow automation, research agents, document analysis, visual reasoning, and enterprise AI systems. Its long-context capability makes it especially useful when tasks require many files, references, instructions, or interaction histories to stay available at once. MiniMax M3 helps teams build more capable AI agents that can understand larger problems, work across multiple modalities, and execute complex tasks with stronger context awareness.

GLM-4.6 builds upon the foundations laid by its predecessor, showcasing enhanced reasoning, coding, and agent capabilities, resulting in notable advancements in inferential accuracy, improved tool usage during reasoning tasks, and a more seamless integration within agent frameworks. In comprehensive benchmark evaluations that assess reasoning, coding, and agent performance, GLM-4.6 surpasses GLM-4.5 and competes robustly against other models like DeepSeek-V3.2-Exp and Claude Sonnet 4, although it still lags behind Claude Sonnet 4.5 in terms of coding capabilities. Furthermore, when subjected to practical tests utilizing an extensive “CC-Bench” suite that includes tasks in front-end development, tool creation, data analysis, and algorithmic challenges, GLM-4.6 outperforms GLM-4.5 while nearing parity with Claude Sonnet 4, achieving victory in approximately 48.6% of direct comparisons and demonstrating around 15% improved token efficiency. This latest model is accessible through the Z.ai API, providing developers the flexibility to implement it as either an LLM backend or as the core of an agent within the platform's API ecosystem. In addition, its advancements could significantly enhance productivity in various application domains, making it an attractive option for developers looking to leverage cutting-edge AI technology.

Anthropic has introduced Claude Haiku 4.5, its newest small language model aimed at achieving near-frontier capabilities at a significantly reduced cost. This model mirrors the coding and reasoning abilities of the company's mid-tier Sonnet 4, yet operates at approximately one-third of the expense while delivering over double the processing speed. According to benchmarks highlighted by Anthropic, Haiku 4.5 either matches or surpasses the performance of Sonnet 4 in critical areas such as code generation and intricate "computer use" workflows. The model is specifically optimized for scenarios requiring real-time, low-latency performance, making it ideal for applications like chat assistants, customer support, and pair-programming. Available through the Claude API under the designation “claude-haiku-4-5,” Haiku 4.5 is designed for large-scale implementations where cost-effectiveness, responsiveness, and advanced intelligence are essential. Now accessible on Claude Code and various applications, this model's efficiency allows users to achieve greater productivity within their usage confines while still enjoying top-tier performance. Moreover, its launch marks a significant step forward in providing businesses with affordable yet high-quality AI solutions.

MiniMax M2 is an open-source foundational model tailored for agent-driven applications and coding tasks, achieving an innovative equilibrium of efficiency, velocity, and affordability. It shines in comprehensive development environments, adeptly managing programming tasks, invoking tools, and executing intricate, multi-step processes, complete with features like Python integration, while offering impressive inference speeds of approximately 100 tokens per second and competitive API pricing at around 8% of similar proprietary models. The model includes a "Lightning Mode" designed for rapid, streamlined agent operations, alongside a "Pro Mode" aimed at thorough full-stack development, report creation, and the orchestration of web-based tools; its weights are entirely open source, allowing for local deployment via vLLM or SGLang. MiniMax M2 stands out as a model ready for production use, empowering agents to autonomously perform tasks such as data analysis, software development, tool orchestration, and implementing large-scale, multi-step logic across real organizational contexts. With its advanced capabilities, this model is poised to revolutionize the way developers approach complex programming challenges.

The GLM-4.6V is an advanced, open-source multimodal vision-language model that belongs to the Z.ai (GLM-V) family, specifically engineered for tasks involving reasoning, perception, and action. It is available in two configurations: a comprehensive version with 106 billion parameters suitable for cloud environments or high-performance computing clusters, and a streamlined “Flash” variant featuring 9 billion parameters, which is tailored for local implementation or scenarios requiring low latency. With a remarkable native context window that accommodates up to 128,000 tokens during its training phase, GLM-4.6V can effectively manage extensive documents or multimodal data inputs. One of its standout features is the built-in Function Calling capability, allowing the model to accept various forms of visual media — such as images, screenshots, and documents — as inputs directly, eliminating the need for manual text conversion. This functionality not only facilitates reasoning about the visual content but also enables the model to initiate tool calls, effectively merging visual perception with actionable results. The versatility of GLM-4.6V opens the door to a wide array of applications, including the generation of interleaved image-and-text content, which can seamlessly integrate document comprehension with text summarization or the creation of responses that include image annotations, thereby greatly enhancing user interaction and output quality.

GLM-4.1V is an advanced vision-language model that offers a robust and streamlined multimodal capability for reasoning and understanding across various forms of media, including images, text, and documents. The 9-billion-parameter version, known as GLM-4.1V-9B-Thinking, is developed on the foundation of GLM-4-9B and has been improved through a unique training approach that employs Reinforcement Learning with Curriculum Sampling (RLCS). This model accommodates a context window of 64k tokens and can process high-resolution inputs, supporting images up to 4K resolution with any aspect ratio, which allows it to tackle intricate tasks such as optical character recognition, image captioning, chart and document parsing, video analysis, scene comprehension, and GUI-agent workflows, including the interpretation of screenshots and recognition of UI elements. In benchmark tests conducted at the 10 B-parameter scale, GLM-4.1V-9B-Thinking demonstrated exceptional capabilities, achieving the highest performance on 23 out of 28 evaluated tasks. Its advancements signify a substantial leap forward in the integration of visual and textual data, setting a new standard for multimodal models in various applications.

GLM-4.5V-Flash is a vision-language model that is open source and specifically crafted to integrate robust multimodal functionalities into a compact and easily deployable framework. It accommodates various types of inputs including images, videos, documents, and graphical user interfaces, facilitating a range of tasks such as understanding scenes, parsing charts and documents, reading screens, and analyzing multiple images. In contrast to its larger counterparts, GLM-4.5V-Flash maintains a smaller footprint while still embodying essential visual language model features such as visual reasoning, video comprehension, handling GUI tasks, and parsing complex documents. This model can be utilized within “GUI agent” workflows, allowing it to interpret screenshots or desktop captures, identify icons or UI components, and assist with both automated desktop and web tasks. While it may not achieve the performance enhancements seen in the largest models, GLM-4.5V-Flash is highly adaptable for practical multimodal applications where efficiency, reduced resource requirements, and extensive modality support are key considerations. Its design ensures that users can harness powerful functionalities without sacrificing speed or accessibility.

GLM-4.5V is an evolution of the GLM-4.5-Air model, incorporating a Mixture-of-Experts (MoE) framework that boasts a remarkable total of 106 billion parameters, with 12 billion specifically dedicated to activation. This model stands out by delivering top-tier performance among open-source vision-language models (VLMs) of comparable scale, demonstrating exceptional capabilities across 42 public benchmarks in diverse contexts such as images, videos, documents, and GUI interactions. It offers an extensive array of multimodal functionalities, encompassing image reasoning tasks like scene understanding, spatial recognition, and multi-image analysis, alongside video comprehension tasks that include segmentation and event recognition. Furthermore, it excels in parsing complex charts and lengthy documents, facilitating GUI-agent workflows through tasks like screen reading and desktop automation, while also providing accurate visual grounding by locating objects and generating bounding boxes. Additionally, the introduction of a "Thinking Mode" switch enhances user experience by allowing the selection of either rapid responses or more thoughtful reasoning based on the situation at hand. This innovative feature makes GLM-4.5V not only versatile but also adaptable to various user needs.

GLM-4.7 is a next-generation AI model built to serve as a powerful coding and reasoning partner. It improves significantly on its predecessor across software engineering, multilingual coding, and terminal interaction benchmarks. GLM-4.7 introduces enhanced agentic behavior by thinking before tool use or execution, improving reliability in long and complex tasks. The model demonstrates strong performance in real-world coding environments and popular coding agents. GLM-4.7 also advances visual and frontend generation, producing modern UI designs and well-structured presentation slides. Its improved tool-use capabilities allow it to browse, analyze, and interact with external systems more effectively. Mathematical and logical reasoning have been strengthened through higher benchmark performance on challenging exams. The model supports flexible reasoning modes, allowing users to trade latency for accuracy. GLM-4.7 can be accessed via Z.ai, OpenRouter, and agent-based coding tools. It is designed for developers who need high performance without excessive cost.

MiniMax-M2.1 is a state-of-the-art open-source AI model built specifically for agent-based development and real-world automation. It focuses on delivering strong performance in coding, tool calling, and long-term task execution. Unlike closed models, MiniMax-M2.1 is fully transparent and can be deployed locally or integrated through APIs. The model excels in multilingual software engineering tasks and complex workflow automation. It demonstrates strong generalization across different agent frameworks and development environments. MiniMax-M2.1 supports advanced use cases such as autonomous coding, application building, and office task automation. Benchmarks show significant improvements over previous MiniMax versions. The model balances high reasoning ability with stability and control. Developers can fine-tune or extend it for specialized agent workflows. MiniMax-M2.1 empowers teams to build reliable AI agents without vendor lock-in.

The Xiaomi MiMo API open platform serves as a developer-centric interface that allows for the integration and access of Xiaomi’s MiMo AI model family, which includes various reasoning and language models like MiMo-V2-Flash, enabling the creation of applications and services via standardized APIs and cloud endpoints. This platform empowers developers to incorporate AI-driven functionalities such as conversational agents, reasoning processes, code assistance, and search-enhanced tasks without the need to handle the complexities of model infrastructure. It features RESTful API access complete with authentication, request signing, and well-structured responses, allowing software to send user queries and receive generated text or processed results in a programmatic manner. The platform also supports essential operations including text generation, prompt management, and model inference, facilitating seamless interactions with MiMo models. Furthermore, it provides comprehensive documentation and onboarding resources, enabling teams to effectively integrate the latest open-source large language models from Xiaomi, which utilize innovative Mixture-of-Experts (MoE) architectures to enhance performance and efficiency. Overall, this open platform significantly lowers the barriers for developers looking to harness advanced AI capabilities in their projects.