Best Open-Weight AI Models for Visual Studio Code

Compare the Top Open-Weight AI Models that integrate with Visual Studio Code as of October 2026

This a list of Open-Weight AI Models that integrate with Visual Studio Code. Use the filters on the left to add additional filters for products that have integrations with Visual Studio Code. View the products that work with Visual Studio Code in the table below.

What are Open-Weight AI Models for Visual Studio Code?

Open-weight AI models are artificial intelligence models whose trained parameters, or weights, are publicly available for developers and organizations to download, deploy, customize, and run on their own infrastructure. These models can support applications such as natural language processing, code generation, reasoning, image generation, speech recognition, multimodal understanding, and autonomous AI agents without requiring exclusive reliance on proprietary model APIs. Open-weight AI models often support fine-tuning, quantization, model distillation, local inference, and deployment across cloud servers, GPUs, edge devices, and on-premises environments. Many models integrate with AI development frameworks, inference engines, model serving platforms, and machine learning tools, allowing organizations to customize performance, control data handling, and optimize infrastructure costs. Unlike fully open-source AI models, open-weight models do not necessarily provide public access to training datasets or source code and may include licensing restrictions on commercial use, redistribution, or modification. Compare and read user reviews of the best Open-Weight AI Models for Visual Studio Code currently available using the table below. This list is updated regularly.

  • 1
    Kimi K3.1 Pro

    Kimi K3.1 Pro

    Moonshot AI

    Kimi K3.1 Pro is a multimodal large language model developed by Moonshot AI for advanced reasoning, software development, 3D modeling, financial analysis, and autonomous AI agent workflows. The model uses a mixture-of-experts architecture with approximately 2.8 trillion total parameters and 104 billion active parameters, supporting a context window of one million tokens. Its coding capabilities include debugging existing applications, generating complete software projects, developing interactive websites, and creating applications with game engines. Kimi K3.1 Pro can analyze images, interpret spatial relationships, generate CAD scripts, and create three-dimensional assets from text descriptions or visual references. Its reasoning and tool-use capabilities support financial modeling, investment research, document analysis, and multistep business workflows. The model is available through Kimi's web and mobile applications, Kimi Work, Kimi Code, and the Kimi API, with a Highspeed variant.
    Starting Price: $2 per 1M tokens
  • 2
    Kimi K3

    Kimi K3

    Moonshot AI

    Kimi K3 is Moonshot AI’s most capable model, built for frontier intelligence scenarios such as software engineering, knowledge work, deep reasoning, and multimodal understanding. The model has 2.8 trillion parameters and uses Kimi Delta Attention, a hybrid linear attention mechanism, along with Attention Residuals for long-context performance. Kimi K3 supports a 1 million token context window, making it useful for analyzing large codebases, long documents, complex knowledge bases, and multi-step workflows. It includes native visual understanding for images and videos, with support for structured message formats, base64 image input, uploaded video files, and multimodal reasoning. Developers can use Kimi K3 through an OpenAI-compatible API with support for streaming, structured JSON output, partial mode, custom tools, dynamic tool loading, and automatic context caching.
    Starting Price: $3 per 1M tokens (input)
  • 3
    Kimi K2.7 Code

    Kimi K2.7 Code

    Moonshot AI

    Kimi K2.7 Code is an open-source, coding-focused agentic AI model developed by Moonshot AI for long-horizon software engineering tasks. It is designed to improve coding performance, agent workflows, and real-world development assistance compared with earlier Kimi K2 versions. The model supports a 256K context window, making it useful for working with large codebases, long technical documents, and complex multi-step programming tasks. Kimi K2.7 Code is available through Kimi Code and API access, with OpenAI- and Anthropic-compatible options for easier integration into developer workflows. It is also listed on Hugging Face and supports deployment through inference engines such as vLLM, SGLang, and KTransformers. With improved agentic capabilities, long-context support, and reduced thinking-token usage compared with K2.6, Kimi K2.7 Code gives developers a flexible open-source option for AI-assisted coding.
    Starting Price: Free
  • 4
    Laguna S 2.1
    Laguna S 2.1 is an open weight agentic coding model designed to pursue longer-horizon work and make effective use of reasoning. It uses a 118-billion-parameter Mixture-of-Experts architecture with 8 billion active parameters per token and supports a context window of up to one million tokens in both thinking and no-thinking modes. Its compact active size makes it suitable for complex work on local machines while remaining competitive with models many times larger on terminal, software-engineering, codebase-question-answering, and tool-use benchmarks. Laguna S 2.1 is built to keep working through difficult tasks with greater persistence, verification, and willingness to backtrack instead of declaring success too early. In demonstrated runs, it built and validated a browser rendering engine from an empty folder, optimized an agent harness for faster execution and substantially lower memory allocation, and completed extended mathematical research using the tools in its environment.
  • 5
    Kimi K2.6

    Kimi K2.6

    Moonshot AI

    Kimi K2.6 is a next-generation agentic AI model developed by Moonshot AI, designed to push forward real-world execution, coding, and multi-step reasoning beyond earlier K2 and K2.5 versions. It builds on a Mixture-of-Experts architecture and the multimodal, agent-first foundation of the Kimi series, combining language understanding, coding, and tool use into a single system capable of planning and executing complex workflows. It introduces deeper reasoning capabilities and significantly improved agent planning, allowing it to break down tasks, coordinate tools, and handle multi-file or multi-step problems with greater accuracy and efficiency. It supports advanced tool calling with high reliability, enabling integration with external systems such as web search or APIs, and includes built-in validation mechanisms to ensure correct execution formats.
    Starting Price: Free
  • 6
    Laguna XS.2

    Laguna XS.2

    Poolside

    Laguna XS.2 is Poolside’s open-weight agentic coding model, built as the lightest and fastest model in the Laguna family. It is a 33B total-parameter Mixture of Experts model with 3B activated parameters, trained completely in-house on 30T tokens. As Poolside’s newest generation model open to the community, Laguna XS.2 is a second-generation architecture and the company’s first open-weight model, built on the lessons learned from training Laguna M.1 across synthetic data and reinforcement learning. The model is designed for agentic coding workflows, where it can code, act, iterate quickly, and perform best inside Poolside’s coding agent. Laguna XS.2 is positioned as a strong model for rapid agentic iteration, especially for developers and teams that need a compact, efficient coding model rather than a heavier frontier system. It is released under an Apache 2.0 license, allowing the community to evaluate, fine-tune, quantize, serve, and build on the weights.
    Starting Price: Free
  • 7
    Laguna XS 2.1
    Laguna XS 2.1 is an upgraded open weight agentic coding model designed for long-horizon work on a local machine. It uses a 33-billion-parameter Mixture-of-Experts architecture with 3 billion activated parameters per token, retaining the same efficient architecture as Laguna XS.2 while improving multilingual software engineering and terminal-style task performance. The model is built to support coding agents that inspect repositories, reason through complex changes, use tools, execute commands, and continue working across extended tasks. It is served with a 256K context window, giving agents room to work with large codebases, lengthy histories, and multi-step workflows. Laguna XS 2.1 is supported by vLLM, SGLang, NVIDIA TensorRT-LLM, Hugging Face Transformers, and Ollama, with native llama.cpp support planned. It is available in BF16, FP8, INT4, and NVFP4 checkpoints, allowing developers to choose between maximum fidelity and configurations suited to tighter VRAM or compute budgets.
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