Best Open-Weight AI Models for OpenCode
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Compare the Top Open-Weight AI Models that integrate with OpenCode as of October 2026
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This a list of Open-Weight AI Models that integrate with OpenCode. Use the filters on the left to add additional filters for products that have integrations with OpenCode. View the products that work with OpenCode in the table below.
What are Open-Weight AI Models for OpenCode?
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 OpenCode currently available using the table below. This list is updated regularly.
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1
MiMo-V2.6-Pro
Xiaomi Technology
MiMo-V2.6-Pro is Xiaomi MiMo’s most capable open-source omnimodal AI model, built for coding, general agent workflows, visual tasks, research, and multimodal creation. The model combines strong software engineering capabilities with computer use, 3D spatial reasoning, visual perception, and tool use for complex multi-step work. MiMo-V2.6-Pro can build interactive 3D environments, generate Blender models, create frontend interfaces and presentations, and coordinate agents to refine outputs through visual feedback. It also supports research workflows such as literature review, computational experimentation, materials discovery, and formal mathematical proof development. Xiaomi trained the model with large-scale reinforcement learning across coding, general agents, visual tasks, and cybersecurity environments and has open-sourced the technical report, training environments, and RL code.Starting Price: Free -
2
MiMo-V2.6-Flash
Xiaomi Technology
MiMo-V2.6-Flash is an open-source, natively omnimodal AI model from Xiaomi MiMo designed to balance intelligence, efficiency, and cost. The model supports coding, general agent workflows, visual reasoning, computer use, automation, and multimodal creative tasks. Its capabilities extend beyond software engineering into frontend design, presentation creation, 3D modeling, interactive world generation, video production, and embodied simulation. Xiaomi trained MiMo-V2.6-Flash with large-scale reinforcement learning across coding, general agent, visual, and cybersecurity tasks, completing roughly 750,000 training trajectories. The model is positioned as the more cost-efficient member of the MiMo-V2.6 family while retaining strong performance across software engineering, tool use, automation, and visual coding benchmarks. MiMo-V2.6-Flash is available through MiMo Desktop, AI Studio, MiMo Code, the Xiaomi MiMo API Platform, OpenRouter, and the open-source MiMo-V2.6 release.Starting Price: Free -
3
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) -
4
Laguna S 2.1
Poolside
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 K2.7 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 -
6
Muse Glimmer
Meta
Muse Glimmer is a 30-billion-parameter open-weights model from Meta Superintelligence Labs, optimized for always-on local agent workflows. Small enough to run on a Mac or PC with a single consumer GPU, it is designed for local agents, function calling, coding, and LLM-as-a-judge evaluation without depending on cloud infrastructure or network access. The model combines long-horizon execution, precise tool calling, multimodal understanding, long-context memory, and instruction following. It can complete end-to-end agentic tasks, sustain multi-step reasoning across extended workflows, recover from failed or unexpected tool calls, and accept interleaved text and images through a dedicated perception encoder for interpreting screenshots, charts, and documents. Muse Glimmer works with OpenClaw and other agentic orchestration patterns, supports controllable reasoning effort, and is trained on data from more than 100 languages.Starting Price: Free -
7
Gemma 4
Google
Gemma 4 is an AI model introduced by Google and built on the Gemini architecture to deliver improved performance and flexibility. The model is designed to run efficiently on a single GPU or TPU, making it more accessible to developers and researchers. Gemma 4 enhances capabilities in natural language understanding and text generation, supporting a wide range of AI-driven applications. Its architecture allows it to handle complex tasks while maintaining efficient resource usage. Developers can use the model to build applications that rely on advanced language processing and automation. The design emphasizes scalability so that it can support both smaller projects and larger AI systems. By combining efficiency with powerful language capabilities, Gemma 4 helps advance the development of modern AI solutions.Starting Price: Free -
8
Kimi K2
Moonshot AI
Kimi K2 is a state-of-the-art open source large language model series built on a mixture-of-experts (MoE) architecture, featuring 1 trillion total parameters and 32 billion activated parameters for task-specific efficiency. Trained with the Muon optimizer on over 15.5 trillion tokens and stabilized by MuonClip’s attention-logit clamping, it delivers exceptional performance in frontier knowledge, reasoning, mathematics, coding, and general agentic workflows. Moonshot AI provides two variants, Kimi-K2-Base for research-level fine-tuning and Kimi-K2-Instruct pre-trained for immediate chat and tool-driven interactions, enabling both custom development and drop-in agentic capabilities. Benchmarks show it outperforms leading open source peers and rivals top proprietary models in coding tasks and complex task breakdowns, while its 128 K-token context length, tool-calling API compatibility, and support for industry-standard inference engines.Starting Price: Free -
9
Kimi K2.5
Moonshot AI
Kimi K2.5 is a next-generation multimodal AI model designed for advanced reasoning, coding, and visual understanding tasks. It features a native multimodal architecture that supports both text and visual inputs, enabling image and video comprehension alongside natural language processing. Kimi K2.5 delivers open-source state-of-the-art performance in agent workflows, software development, and general intelligence tasks. The model offers ultra-long context support with a 256K token window, making it suitable for large documents and complex conversations. It includes long-thinking capabilities that allow multi-step reasoning and tool invocation for solving challenging problems. Kimi K2.5 is fully compatible with the OpenAI API format, allowing developers to switch seamlessly with minimal changes. With strong performance, flexibility, and developer-focused tooling, Kimi K2.5 is built for production-grade AI applications.Starting Price: Free -
10
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 -
11
Gemma 3
Google
Gemma 3, introduced by Google, is a new AI model built on the Gemini 2.0 architecture, designed to offer enhanced performance and versatility. This model is capable of running efficiently on a single GPU or TPU, making it accessible for a wide range of developers and researchers. Gemma 3 focuses on improving natural language understanding, generation, and other AI-driven tasks. By offering scalable, powerful AI capabilities, Gemma 3 aims to advance the development of AI systems across various industries and use cases.Starting Price: Free -
12
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 -
13
North Mini Code
Cohere
North Mini Code is Cohere’s first agentic coding model for developers and the inaugural member of its next generation of powerful models. Small, efficient, and open-source, it is built for the sovereign developer ecosystem and designed to deliver strong software development performance without requiring extensive hardware. North Mini Code is a mixture-of-experts model with 30B total parameters and 3B active parameters, giving developers access to agentic coding capabilities in a compact and efficient form. The model is optimized for code generation, agentic software engineering, and terminal tasks, with a 256K total context length and up to 64K maximum generation. It is built for real-world developer workflows, including understanding and orchestrating sub-agents, mapping system architecture, running code reviews, and supporting coding agents that need to reason through complex software tasks.
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