Page 21 | Top Artificial Intelligence Software for Linux in 2026

Find and compare the best Artificial Intelligence software for Linux in 2026

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Use the comparison tool below to compare the top Artificial Intelligence software for Linux on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.

1

Mistral Vibe CLI

Mistral AI
Free

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The Mistral Vibe CLI is an innovative command-line tool designed for "vibe-coding," allowing developers to engage with their projects using natural language commands instead of relying solely on tedious manual edits or traditional IDE functionalities. This interface integrates with version control systems like Git, examining project files, the structure of directories, and the status of Git to establish context. It leverages this context alongside advanced AI coding models, such as Devstral 2 and Devstral Small, to perform a variety of tasks including multi-file edits, code refactoring, code generation, searching, and manipulating files— all initiated through simple English instructions. By keeping track of project-specific details such as dependencies, file organization, and history, it is capable of executing coordinated updates across multiple files at once, such as renaming a function and ensuring all references throughout the repository are adjusted accordingly. Additionally, it can create boilerplate code across different modules and even help outline new features starting from an overarching prompt, significantly streamlining the development process. This approach not only enhances productivity but also fosters a more intuitive coding environment for developers.
2

DeepCoder

Agentica Project
Free

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DeepCoder, an entirely open-source model for code reasoning and generation, has been developed through a partnership between Agentica Project and Together AI. Leveraging the foundation of DeepSeek-R1-Distilled-Qwen-14B, it has undergone fine-tuning via distributed reinforcement learning, achieving a notable accuracy of 60.6% on LiveCodeBench, which marks an 8% enhancement over its predecessor. This level of performance rivals that of proprietary models like o3-mini (2025-01-031 Low) and o1, all while operating with only 14 billion parameters. The training process spanned 2.5 weeks on 32 H100 GPUs, utilizing a carefully curated dataset of approximately 24,000 coding challenges sourced from validated platforms, including TACO-Verified, PrimeIntellect SYNTHETIC-1, and submissions to LiveCodeBench. Each problem mandated a legitimate solution along with a minimum of five unit tests to guarantee reliability during reinforcement learning training. Furthermore, to effectively manage long-range context, DeepCoder incorporates strategies such as iterative context lengthening and overlong filtering, ensuring it remains adept at handling complex coding tasks. This innovative approach allows DeepCoder to maintain high standards of accuracy and reliability in its code generation capabilities.
3

DeepSWE

Agentica Project
Free

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DeepSWE is an innovative and fully open-source coding agent that utilizes the Qwen3-32B foundation model, trained solely through reinforcement learning (RL) without any supervised fine-tuning or reliance on proprietary model distillation. Created with rLLM, which is Agentica’s open-source RL framework for language-based agents, DeepSWE operates as a functional agent within a simulated development environment facilitated by the R2E-Gym framework. This allows it to leverage a variety of tools, including a file editor, search capabilities, shell execution, and submission features, enabling the agent to efficiently navigate codebases, modify multiple files, compile code, run tests, and iteratively create patches or complete complex engineering tasks. Beyond simple code generation, DeepSWE showcases advanced emergent behaviors; when faced with bugs or new feature requests, it thoughtfully reasons through edge cases, searches for existing tests within the codebase, suggests patches, develops additional tests to prevent regressions, and adapts its cognitive approach based on the task at hand. This flexibility and capability make DeepSWE a powerful tool in the realm of software development.
4

DeepScaleR

Agentica Project
Free

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DeepScaleR is a sophisticated language model comprising 1.5 billion parameters, refined from DeepSeek-R1-Distilled-Qwen-1.5B through the use of distributed reinforcement learning combined with an innovative strategy that incrementally expands its context window from 8,000 to 24,000 tokens during the training process. This model was developed using approximately 40,000 meticulously selected mathematical problems sourced from high-level competition datasets, including AIME (1984–2023), AMC (pre-2023), Omni-MATH, and STILL. Achieving an impressive 43.1% accuracy on the AIME 2024 exam, DeepScaleR demonstrates a significant enhancement of around 14.3 percentage points compared to its base model, and it even outperforms the proprietary O1-Preview model, which is considerably larger. Additionally, it excels on a variety of mathematical benchmarks such as MATH-500, AMC 2023, Minerva Math, and OlympiadBench, indicating that smaller, optimized models fine-tuned with reinforcement learning can rival or surpass the capabilities of larger models in complex reasoning tasks. This advancement underscores the potential of efficient modeling approaches in the realm of mathematical problem-solving.
5

GLM-4.6V

Zhipu AI
Free

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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.
6

GLM-4.1V

Zhipu AI
Free

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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.
7

GLM-4.5V-Flash

Zhipu AI
Free

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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.
8

GLM-4.5V

Zhipu AI
Free

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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.
9

Foxglove

Foxglove
$18 per month

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Foxglove is a sophisticated platform designed specifically for the visualization, observability, and management of data in the robotics and embodied AI sectors, effectively centralizing various large and complex multimodal temporal datasets such as time series, sensor logs, imagery, lidar/point clouds, and geospatial maps within a unified workspace. It empowers engineers to efficiently record, import, organize, stream, and visualize both live and archived data from robotic systems through user-friendly, customizable dashboards that feature interactive panels for 3D scenes, plots, images, and maps, thereby enhancing the understanding of robotic perception, cognition, and actions. Furthermore, Foxglove facilitates real-time integration with systems like ROS and ROS 2 through bridges and web sockets, supports cross-platform operations (available as a desktop application for Linux, Windows, and macOS), and accelerates the processes of analysis, debugging, and performance enhancement by synchronizing disparate data sources in both time and spatial contexts. Additionally, its intuitive design and comprehensive functionalities make it an invaluable tool for researchers and developers alike, ensuring a streamlined workflow in the dynamic field of robotics.
10

NWarch AI

Daten And Wissen
500 per use case per month

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Daten & Wissen, recognized by DPIIT and a partner of NVIDIA Inception, has developed NWarch AI, an innovative platform focused on edge-first video analytics and automation that transforms current CCTV and sensor feeds into immediate insights related to safety, crowd management, and operational effectiveness. Our solution addresses the challenges of disjointed video data, the inefficiencies of slow manual oversight, and the expenses tied to replacing existing systems by offering easy-to-integrate edge inference, AI-driven natural language agents for instant inquiries, and automation workflows that require no coding. NWarch AI caters to various sectors including construction, manufacturing, logistics, retail, and security, facilitating quicker incident responses, streamlining compliance reporting, and achieving significant efficiency improvements. By leveraging our technology, businesses can enhance their operational capabilities and make data-driven decisions more effectively.
11

GLM-4.7

Zhipu AI
Free

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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.
12

MiniMax-M2.1

MiniMax
Free

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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.
13

Dafthunk

Dafthunk
Free

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Dafthunk is an innovative platform designed for visual workflow automation, allowing users to create, manage, and implement serverless automation workflows effortlessly with a user-friendly drag-and-drop interface, eliminating the need for any infrastructure setup or container usage. The platform enables users to build workflows by visually linking nodes that execute various tasks involving AI, browser automation, data manipulation, media creation, integrations, and development tools, which are then processed on Cloudflare’s extensive global edge network, ensuring seamless scaling and reliable execution. It features a variety of workflow triggers, such as HTTP webhooks, queues, schedules based on cron, and options for manual initiation, facilitating automation that is responsive to events, time-sensitive, or initiated by users. The platform also offers persistent storage for workflow states and execution logs through Cloudflare's D1 and R2 storage services, ensuring data integrity and accessibility. Users can enhance their workflows by integrating AI models from well-known providers like OpenAI, Anthropic, Google, and Cloudflare AI, enabling capabilities in text generation, summarization, vision processing, natural language processing, transcription, image generation, and more. This comprehensive approach empowers users to streamline their processes and harness the full potential of automation technology.
14

Happy Coder

Happy Coder
Free

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Happy, often referred to as Happy Coder, is a free and open-source client available for both mobile and web platforms, allowing users to create, observe, and manage multiple sessions of Claude Code AI coding agents across a variety of devices including phones, tablets, laptops, and desktops, all while ensuring real-time synchronization through an encrypted relay system that enables users to continue their work on different devices without any loss of context. This system features three interconnected components: a locally running CLI program that launches and oversees the Claude Code sessions, a mobile or web application that securely connects to the CLI via end-to-end encryption to protect user data from being accessed by anyone, even the relay server, and a relay server that merely transmits encrypted data between devices without accessing the information itself; this architecture allows developers to use their preferred tools, editors, and workflows while integrating remote control functionalities effortlessly. Additionally, the seamless transition between devices promotes enhanced productivity and flexibility for users engaged in coding tasks.
15

Pencil

Pencil
Free

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Pencil.dev is an innovative design-in-code platform that utilizes AI to seamlessly integrate visual interface design within development environments such as Cursor, VS Code, and various other IDEs, allowing designers and developers to collaborate without the need for tool handoffs. Centered around an agent-driven Model Context Protocol (MCP) canvas and an accessible design format embedded in your codebase, Pencil enables users to create, refine, and produce pixel-perfect UI screens with the aid of AI, all while maintaining version control in Git alongside their source code, which facilitates branches, merges, and rollbacks akin to traditional coding practices. By incorporating a Figma-like canvas directly into the IDE, it significantly reduces the hassle of switching between different tools, supports the import of frames and assets from Figma while preserving vectors and styles, and allows for manipulation of design elements through intuitive editing panels, layers, and CSS-like properties. Furthermore, AI models assist in the simultaneous generation of screens, flows, and components, enhancing productivity and creativity in the design process. This integration fosters a more cohesive workflow, making it easier for teams to innovate and iterate on their projects efficiently.
16

Zo

Zo Computer
$18/month

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Zo is an AI-powered cloud computer that goes beyond a traditional assistant to actively execute tasks for you. It operates continuously, handling inbox management, meeting scheduling, research, and automation even while you sleep. Zo provides a fully integrated environment where your data, tools, and AI models work together seamlessly. Under the hood, it’s a customizable Linux server that can host websites, APIs, and self-hosted tools on demand. Users can choose from leading AI models or bring their own API keys for maximum flexibility. Zo is accessible via app or text, making powerful automation simple and intuitive. It transforms AI from a passive interface into an active, always-working system. The result is a personal operating system designed around your needs. Zo adapts, builds, and scales as you use it more.
17

Composer 1

Cursor
$20 per month

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Composer is an AI model crafted by Cursor, specifically tailored for software engineering functions, and it offers rapid, interactive coding support within the Cursor IDE, an enhanced version of a VS Code-based editor that incorporates smart automation features. This model employs a mixture-of-experts approach and utilizes reinforcement learning (RL) to tackle real-world coding challenges found in extensive codebases, enabling it to deliver swift, contextually aware responses ranging from code modifications and planning to insights that grasp project frameworks, tools, and conventions, achieving generation speeds approximately four times faster than its contemporaries in performance assessments. Designed with a focus on development processes, Composer utilizes long-context comprehension, semantic search capabilities, and restricted tool access (such as file editing and terminal interactions) to effectively address intricate engineering inquiries with practical and efficient solutions. Its unique architecture allows it to adapt to various programming environments, ensuring that users receive tailored assistance suited to their specific coding needs.
18

Kimi Code CLI

Moonshot AI
Free

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Kimi Code CLI serves as an AI-driven command-line tool designed to aid developers in software creation and terminal tasks by interpreting and altering code, executing shell commands, retrieving web content, autonomously strategizing and modifying actions during processes, and offering an interactive shell environment where users can articulate their requirements in everyday language or switch to command mode for direct input; it seamlessly integrates with IDEs and local agent clients through the Agent Client Protocol, enhancing workflows and streamlining activities like code writing, bug fixing, project exploration, addressing architectural queries, and automating batch processes or build and test scripts. The installation process involves running a script that sets up the essential tool manager before downloading the Kimi CLI package, after which users can confirm installation with a version check and proceed to configure an API source for optimal functionality. Additionally, the Kimi Code CLI not only enhances productivity but also fosters a more intuitive interaction between developers and their coding environment.
19

LobeHub

LobeHub
$9.90 per month

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LobeHub is a versatile open-source AI platform designed for users to develop, tailor, and oversee AI agents and assistant teams that evolve alongside their requirements, facilitating collaboration across various workflows and projects with a shared context and responsive behavior. The platform accommodates a range of AI models and providers through a user-friendly interface, which allows for effortless switching and interactions among different models while also integrating knowledge bases, plugins, and specialized skills that boost productivity. Users have the capability to launch private chat applications and assistants, link agents to real-world tools and data sources, and systematically arrange work into projects, schedules, and workspaces, with coordinated agents performing tasks simultaneously. Emphasizing a long-term partnership between humans and agents, LobeHub fosters personal memory and ongoing learning, presenting flexible frameworks for multimodal interaction and community engagement, including an agent marketplace and a plugin ecosystem. This innovative approach not only enhances user experience but also encourages continuous improvement of AI capabilities. Ultimately, LobeHub positions itself as a key player in the future of collaborative AI development.
20

Composer 1.5

Cursor
$20 per month

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Composer 1.5 is the newest agentic coding model from Cursor that enhances both speed and intelligence for routine coding tasks, achieving a remarkable 20-fold increase in reinforcement learning capabilities compared to its earlier version, which translates to improved performance on real-world programming problems. This model is crafted as a "thinking model," generating internal reasoning tokens that facilitate the analysis of a user's codebase and the planning of subsequent actions, enabling swift responses to straightforward issues while engaging in more profound reasoning for intricate challenges. Additionally, it maintains interactivity and efficiency, making it ideal for daily development processes. To address prolonged tasks, Composer 1.5 features self-summarization, which allows the model to condense information and retain context when it hits limits, thus preserving accuracy across a variety of input lengths. Internal evaluations indicate that Composer 1.5 outperforms its predecessor in coding tasks, particularly excelling in tackling more complex problems, further enhancing its utility for interactive applications within Cursor's ecosystem. Overall, this model represents a significant advancement in coding assistance technology, promising to streamline the development experience for users.
21

Oz

Warp
$18 per month

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Oz serves as a cloud-centered orchestration platform tailored for AI coding agents, empowering developers and teams to effortlessly execute, oversee, automate, and expand an unlimited number of parallel cloud coding agents without the need for custom infrastructure. This platform offers programmable, auditable workflows that streamline repetitive development tasks and intricate code modifications, ensuring full control over the process. Users can initiate agents through various interfaces including the CLI, web application, APIs, SDKs, Warp Terminal, or mobile devices. Additionally, Oz allows for the orchestration of numerous agents simultaneously, complete with integrated audit trails, session tracking, and comprehensive visibility, and provides the capability to monitor or engage with active agents within a shared control environment. The platform also accommodates flexible hosting options, whether on your own infrastructure or Warp's, while ensuring that each agent operates within secure, isolated environments. Oz produces tangible artifacts such as plans and pull requests, and is adept at managing multi-repo alterations, allowing agents to effectively synchronize extensive updates across vast codebases. With its robust features, Oz significantly enhances the efficiency of software development processes, making it an indispensable tool for modern development teams.
22

GLM-5

Zhipu AI
Free

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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.
23

Rowboat

Rowboat
Free

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RowBoat is a user-friendly, open source integrated development environment that leverages AI assistance to enable developers and teams to swiftly create, oversee, test, and launch multi-agent AI systems, also known as intelligent assistants, through a visual interface and natural language commands, all while streamlining the integration of various tools and workflows without requiring extensive engineering resources. Central to RowBoat is RowBoat Studio, where users can articulate their desired assistant in simple English, prompting an AI "Copilot" to generate the necessary agents, link them into cohesive workflows, and allow for real-time refinement and testing prior to deployment. An assistant consists of a network of agents, each equipped with access to various tools and data sources, collaborating to engage with users, execute background operations, or automate intricate workflows, and it offers robust support for API and Python SDK integration, enabling agents to facilitate conversations or perform actions seamlessly within applications and websites. This platform not only enhances productivity but also democratizes AI development, making it accessible for teams of all technical levels.
24

MiniMax M2.5

MiniMax
Free

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MiniMax M2.5 is a next-generation foundation model built to power complex, economically valuable tasks with speed and cost efficiency. Trained using large-scale reinforcement learning across hundreds of thousands of real-world task environments, it excels in coding, tool use, search, and professional office workflows. In programming benchmarks such as SWE-Bench Verified and Multi-SWE-Bench, M2.5 reaches state-of-the-art levels while demonstrating improved multilingual coding performance. The model exhibits architect-level reasoning, planning system structure and feature decomposition before writing code. With throughput speeds of up to 100 tokens per second, it completes complex evaluations significantly faster than earlier versions. Reinforcement learning optimizations enable more precise search rounds and fewer reasoning steps, improving overall efficiency. M2.5 is available in two variants—standard and Lightning—offering identical capabilities with different speed configurations. Pricing is designed to be dramatically lower than competing frontier models, reducing cost barriers for large-scale agent deployment. Integrated into MiniMax Agent, the model supports advanced office skills including Word formatting, Excel financial modeling, and PowerPoint editing. By combining high performance, efficiency, and affordability, MiniMax M2.5 aims to make agent-powered productivity accessible at scale.
25

PicoClaw

PicoClaw
Free

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PicoClaw is a compact and highly efficient AI assistant engineered in Go to deliver powerful agent capabilities on extremely modest hardware. Designed to function on devices costing as little as $10, it consumes under 10MB of memory and achieves startup times of less than one second. Unlike many resource-heavy AI systems, PicoClaw prioritizes performance optimization and portability, running smoothly across RISC-V, ARM, and x86 architectures using a single binary. The project showcases an AI-bootstrapped development approach, where much of the core system was generated and refined through agent-driven processes. Users can deploy it through direct binary installation, source compilation, or Docker Compose for containerized environments. It connects seamlessly to popular messaging platforms including Telegram, Discord, QQ, DingTalk, and LINE, allowing users to interact with their assistant anywhere. PicoClaw includes structured workspace management for sessions, memory, scheduled jobs, and customizable skills. Security is enforced through sandboxed execution and restrictions that prevent dangerous commands or system-level damage. The assistant also supports periodic heartbeat tasks, asynchronous subagents, and cron-based scheduling for automation. Overall, PicoClaw delivers a scalable, low-cost AI agent framework suitable for personal assistants, smart devices, and lightweight server environments.