List of the Top Open-Weight AI Models for Ollama in 2026 - Page 2

GLM-5-Turbo is a swift advancement of Z.ai’s GLM-5 model, designed to provide both efficient and stable performance for scenarios driven by agents, while also maintaining strong reasoning and programming capabilities. It is specifically optimized for high-throughput requirements, particularly in intricate long-chain agent tasks that involve a sequence of steps, tools, and decisions executed with precision and minimal delay. By supporting advanced agent-driven workflows, GLM-5-Turbo significantly improves multi-step planning, tool application, and task execution, yielding a higher level of responsiveness than larger flagship models in the collection. Retaining the foundational advantages of the GLM-5 series, this model excels in reasoning, coding, and managing extensive contexts, while emphasizing the optimization of crucial factors such as speed, efficiency, and stability for production environments. Additionally, it is designed to integrate seamlessly with agent frameworks like OpenClaw, enabling it to effectively coordinate actions, oversee inputs, and execute tasks proficiently. This adaptability ensures that users experience a dependable and responsive tool capable of meeting diverse operational challenges and requirements, ultimately enhancing productivity and effectiveness in various applications.

MiniMax M2.7 is a cutting-edge AI model engineered to deliver high-performance productivity across coding, search, and professional office workflows. It is trained using reinforcement learning across extensive real-world environments, allowing it to handle complex, multi-step tasks with accuracy and adaptability. The model excels at structured problem-solving, breaking down challenges into logical steps before generating solutions across a wide range of programming languages. It offers high-speed processing with rapid token generation, enabling faster execution of tasks and improved workflow efficiency. Its optimized reasoning reduces unnecessary token usage, improving both performance and cost efficiency compared to earlier models. M2.7 achieves state-of-the-art results in software engineering benchmarks, demonstrating strong capabilities in debugging, development, and incident resolution. It also significantly reduces intervention time during system issues, improving operational reliability. The model is equipped with advanced agentic capabilities, enabling it to collaborate with tools and execute complex workflows with high precision. It supports multi-agent environments and maintains strong adherence to complex task requirements. Additionally, it excels in professional knowledge tasks, including high-quality office document editing and multi-turn interactions. Its ability to handle structured business workflows makes it suitable for enterprise use cases. With its balance of speed, intelligence, and affordability, it stands out among frontier AI models. Overall, MiniMax M2.7 provides a scalable and efficient solution for modern AI-driven productivity and automation.

Qwen3.5-35B-A3B is part of the Qwen3.5 "Medium" model lineup, designed as an efficient multimodal foundation model that effectively balances strong reasoning skills with real-world application demands. It features a Mixture-of-Experts (MoE) architecture, comprising 35 billion parameters but activating approximately 3 billion for each token, which allows it to deliver performance comparable to much larger models while significantly reducing computational costs. The model incorporates a hybrid attention mechanism that fuses linear attention with conventional attention layers, enhancing its capability to manage extensive context and improving scalability for complex tasks. As a vision-language model, it adeptly processes both text and visual inputs, catering to a wide range of applications such as multimodal reasoning, programming, and automated workflows. Additionally, it is designed to function as a flexible "AI agent," skilled in planning, tool utilization, and systematic problem-solving, thereby expanding its utility beyond simple conversational exchanges. This versatility not only enhances its performance in various tasks but also makes it an invaluable resource in fields that increasingly rely on sophisticated AI-driven solutions. Its adaptability and efficiency position it as a key player in the evolving landscape of artificial intelligence applications.

Laguna XS.2 stands out as Poolside's groundbreaking open-weight coding model, noted for being the lightest and fastest in the Laguna lineup. Equipped with a staggering 33 billion parameters organized in a Mixture of Experts structure, of which 3 billion are active, this model has undergone extensive training in-house utilizing 30 trillion tokens. As the most recent generation model available to the public, it features a second-generation architecture and represents Poolside's first open-weight release, benefiting from lessons learned during the Laguna M.1 training process, which utilized synthetic data and reinforcement learning. Tailored specifically to optimize agentic coding workflows, Laguna XS.2 is exceptional in coding, acting, and rapid iteration, particularly within Poolside's coding agent ecosystem. This model is especially beneficial for developers and teams in need of a lightweight and efficient coding solution, as opposed to more complex frontier systems. Released under the flexible Apache 2.0 license, it enables the community to evaluate, refine, quantize, and build upon its weights, fostering an environment of collaborative development. Ultimately, Laguna XS.2 not only serves as a powerful tool for agentic coding but also promotes creativity and experimentation among its users, allowing for a diverse range of applications and enhancements.

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.

The GLM-5V-Turbo stands as a cutting-edge multimodal coding foundation model, expertly designed for scenarios necessitating visual inputs, proficient in interpreting various formats including images, videos, texts, and files to produce text-based results. This model is particularly optimized for agent workflows, enabling it to grasp environments effectively, devise suitable actions, and execute tasks, while also maintaining compatibility with agent frameworks such as Claude Code and OpenClaw. Notably, it excels in managing long-context interactions, offering an impressive context capacity of 200K tokens alongside an output limit of up to 128K tokens, making it exceptionally suited for complex, long-duration projects. Moreover, it presents an array of thinking modes tailored for different situations, demonstrates strong visual understanding of both images and videos, and streams outputs in real-time to improve user interaction. It also incorporates advanced function-calling capabilities that allow seamless integration of external tools, with its context caching feature significantly enhancing performance during extended dialogues. In real-world applications, the model is capable of skillfully converting design mockups into operational frontend projects, highlighting its adaptability and depth in practical coding environments. Furthermore, this adaptability empowers users to approach a diverse array of intricate tasks with assurance and effectiveness, greatly enhancing their productivity.

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.

Qwen3.8-Flash-Next is a pioneering open-weight multimodal Mixture-of-Experts architecture that offers an initial look at the design meant for its successor, Qwen4. This model has been expertly crafted to enhance various aspects such as attention mechanisms, residual pathways, embeddings, and optimization strategies, thereby increasing its overall functionality, enhancing computational efficiency, expanding its model capacity, and ensuring stability during training. Its unique hybrid structure combines Gated DeltaNet, which effectively condenses historical information, with Qwen Sparse Attention, facilitating the selection of meaningful context on a micro-block scale to reduce both attention and indexing expenses for lengthy sequences. The Gated Residual feature enhances the residual pathway by incorporating four streams, which helps in dynamically regulating the information flow across different layers. Moreover, the N-gram Embedding cleverly merges large-scale local-pattern memory with minimal computational overhead for each token, with the capability to transfer to host memory for added efficiency. The entire model is built around a main network comprising 125 billion parameters, supplemented by an additional 51 billion parameters specifically for N-gram embeddings, activating only 6 billion parameters for each token processed. This advanced framework underscores the continuous evolution in machine learning architectures, laying the groundwork for exciting future innovations, and it exemplifies the increasing sophistication and potential of multimodal models in various applications.

Gemma encompasses a series of innovative, lightweight open models inspired by the foundational research and technology that drive the Gemini models. Developed by Google DeepMind in collaboration with various teams at Google, the term "gemma" derives from Latin, meaning "precious stone." Alongside the release of our model weights, we are also providing resources designed to foster developer creativity, promote collaboration, and uphold ethical standards in the use of Gemma models. Sharing essential technical and infrastructural components with Gemini, our leading AI model available today, the 2B and 7B versions of Gemma demonstrate exceptional performance in their weight classes relative to other open models. Notably, these models are capable of running seamlessly on a developer's laptop or desktop, showcasing their adaptability. Moreover, Gemma has proven to not only surpass much larger models on key performance benchmarks but also adhere to our rigorous standards for producing safe and responsible outputs, thereby serving as an invaluable tool for developers seeking to leverage advanced AI capabilities. As such, Gemma represents a significant advancement in accessible AI technology.

The Gemma family is composed of advanced and lightweight models that are built upon the same groundbreaking research and technology as the Gemini line. These state-of-the-art models come with powerful security features that foster responsible and trustworthy AI usage, a result of meticulously selected data sets and comprehensive refinements. Remarkably, the Gemma models perform exceptionally well in their varied sizes—2B, 7B, 9B, and 27B—frequently surpassing the capabilities of some larger open models. With the launch of Keras 3.0, users benefit from seamless integration with JAX, TensorFlow, and PyTorch, allowing for adaptable framework choices tailored to specific tasks. Optimized for peak performance and exceptional efficiency, Gemma 2 in particular is designed for swift inference on a wide range of hardware platforms. Moreover, the Gemma family encompasses a variety of models tailored to meet different use cases, ensuring effective adaptation to user needs. These lightweight language models are equipped with a decoder and have undergone training on a broad spectrum of textual data, programming code, and mathematical concepts, which significantly boosts their versatility and utility across numerous applications. This diverse approach not only enhances their performance but also positions them as a valuable resource for developers and researchers alike.

gpt-oss-20b is a robust text-only reasoning model featuring 20 billion parameters, released under the Apache 2.0 license and shaped by OpenAI’s gpt-oss usage guidelines, aimed at simplifying the integration into customized AI workflows via the Responses API without reliance on proprietary systems. It has been meticulously designed to perform exceptionally in following instructions, offering capabilities like adjustable reasoning effort, detailed chain-of-thought outputs, and the option to leverage native tools such as web search and Python execution, which leads to well-structured and coherent responses. Developers must take responsibility for implementing their own deployment safeguards, including input filtering, output monitoring, and compliance with usage policies, to ensure alignment with protective measures typically associated with hosted solutions and to minimize the risk of malicious or unintended actions. Furthermore, its open-weight architecture is particularly advantageous for on-premises or edge deployments, highlighting the significance of control, customization, and transparency to cater to specific user requirements. This flexibility empowers organizations to adapt the model to their distinct needs while upholding a high standard of operational integrity and performance. As a result, gpt-oss-20b not only enhances user experience but also promotes responsible AI usage across various applications.

gpt-oss-120b is a reasoning model focused solely on text, boasting 120 billion parameters, and is released under the Apache 2.0 license while adhering to OpenAI’s usage policies; it has been developed with contributions from the open-source community and is compatible with the Responses API. This model excels at executing instructions and utilizes various tools, including web searches and Python code execution, which allows for a customizable level of reasoning effort and results in detailed chain-of-thought outputs that can seamlessly fit into different workflows. Although it is constructed to comply with OpenAI's safety policies, its open-weight nature poses a risk, as adept users might modify it to bypass these protections, thereby prompting developers and organizations to implement additional safety measures akin to those of managed models. Assessments reveal that gpt-oss-120b falls short of high performance in specialized fields such as biology, chemistry, or cybersecurity, even after attempts at adversarial fine-tuning. Moreover, its introduction does not represent a substantial advancement in biological capabilities, indicating a cautious stance regarding its use. Consequently, it is advisable for users to stay alert to the potential risks associated with its open-weight attributes, and to consider the implications of its deployment in sensitive environments. As awareness of these factors grows, the community's approach to managing such technologies will evolve and adapt.

The Laguna XS 2.1 represents a sophisticated advancement in coding models, functioning as an open weight agentic system that excels in executing long-duration tasks on local machines. It boasts a robust 33-billion-parameter Mixture-of-Experts architecture, activating 3 billion parameters per token, while preserving the efficient design of its predecessor, Laguna XS.2, and significantly enhancing its capabilities in multilingual software engineering and terminal-related tasks. This model is meticulously crafted to support coding agents in reviewing code repositories, navigating complex changes, leveraging diverse tools, executing commands, and ensuring seamless progress throughout extensive projects. With an impressive context window of 256K, it empowers agents to adeptly handle large codebases, maintain extensive histories, and navigate intricate multi-step workflows. The Laguna XS 2.1 also enjoys compatibility with various platforms like vLLM, SGLang, NVIDIA TensorRT-LLM, Hugging Face Transformers, and Ollama, with aspirations for future native support from llama.cpp. Offered in multiple checkpoint formats such as BF16, FP8, INT4, and NVFP4, it allows developers to choose between high fidelity and configurations designed for environments with restricted VRAM or processing capacity. This versatility not only enhances its usability across different development frameworks but also positions it as a prime choice for diverse programming needs and settings. Furthermore, its ability to adapt to varying project demands makes it a valuable asset for developers seeking efficiency and performance in their workflows.

Qwen3.8-2.4T-A95B emerges as the largest open model in the Qwen3.8 series, presenting advanced Qwen-Max-class capabilities in a format that is accessible to the public. Built on the robust foundation of Qwen3.5, this model offers marked improvements in performance across various domains, including coding, professional applications, research, and complex, extended agentic tasks, underscoring its ability to reliably execute intricate, multi-step workflows to completion. With its innovative mixture-of-experts architecture, it features a remarkable total of 2.4 trillion parameters, of which 95 billion are activated, utilizing 512 experts and allowing for simultaneous engagement of 10 routed experts alongside one shared expert. The model supports a native context length of 262,144 tokens, extendable to about 1.01 million tokens, thereby enabling considerable adaptability for diverse applications. Additionally, enhancements in agent execution, such as superior autonomous planning and improved responsiveness to environmental cues, enhance its overall efficiency. Its extensive compatibility with popular agent frameworks and development tools further aids in smooth integration into current systems, making it an appealing option for both developers and researchers. This versatility is particularly beneficial for those seeking to leverage advanced AI capabilities in their projects.

Qwen3.8-Omni-Flash is a groundbreaking omnimodal model designed to significantly boost the efficiency of agents operating in productivity-focused settings, transitioning from basic understanding of multimodal inputs to actively performing tasks, utilizing diverse tools, and engaging in creative projects. Built upon the sophisticated Qwen3.8-Flash-Next architecture, it adeptly handles text, images, audio, and video inputs with an extraordinary context window of up to 1 million tokens, while maintaining strong performance in text-centric applications. This model transcends traditional coding and knowledge-based tasks, enriching workflows related to audio and video through capabilities such as video editing, crafting music videos, providing film commentary, summarizing audiovisual content, and facilitating real-time discussions. It particularly excels at enhancing the interpretation of long-form audio and video through organized descriptions, enabling agents to gather compelling evidence, grasp meeting content, and conduct thorough research focused on video materials. Users are empowered to specify parameters including subject matter, time frame, level of detail, and output format for video assessments, allowing for comprehensive overviews and customized analyses. This adaptability positions it as an indispensable resource for both professionals and creatives eager to optimize their productivity across a variety of multimedia platforms, ensuring that every project reaches its full potential. Furthermore, the model's seamless integration into diverse workflows opens up new possibilities for collaboration and innovation in content creation.

Recursive Flint is a state-of-the-art language model boasting 40 billion parameters, crafted as a mixture-of-experts particularly for local coding tasks. It utilizes approximately 2.5 billion parameters for every token it processes, allowing for advanced reasoning, multi-turn tool usage, and support for context windows reaching up to 512,000 tokens. Developers have the opportunity to integrate Flint into coding agents, enabling them to inspect codebases, fix bugs, create new features, and formulate tests while running inference directly on their own systems. The model's weights are accessible under the Apache 2.0 license, fostering broader accessibility for users. Flint supports a range of platforms, including llama.cpp, Ollama, Unsloth, LM Studio, vLLM, and SGLang. In addition, Recursive provides rec, an inference engine finely tuned for optimal performance on Apple Silicon M4 Pro and M5 Pro/Max chips. To facilitate the implementation of the model on local machines, detailed setup documentation and practical examples are available for developers. This versatile tool not only aids developers and technical teams in running a code-focused model tailored to their individual system needs but also enhances their overall coding efficiency. As a result, users can significantly boost their development workflows and achieve their programming goals with greater ease and speed than ever before.