Kilo Code Integrations

Ring represents an advanced trillion-parameter model developed by Ant Group, designed to optimize real-world Agent workflows. Utilizing a Mixture of Experts architecture akin to that of Ling, it activates around 63 billion parameters for each inference and is adept at performing tasks such as coding agents, using tools, collaborating with diverse instruments, software engineering, conducting research, and managing long-term projects. Rather than simply aiming for more intelligent outcomes, Ring focuses on ensuring the dependable execution of complex tasks while keeping costs manageable, thereby achieving a harmonious balance of quality, speed, and efficiency in production environments. The most recent version, Ring-2.6-1T, features a customizable Reasoning Effort mechanism with high and xhigh reasoning intensity levels that adjust the reasoning budget based on task complexity. The high mode is specifically designed for frequent Agent workflows, leading to reduced token costs and expedited multi-step processes, while also promoting multi-turn conversations, tool collaboration, and task breakdown. This evolution significantly boosts the operational capabilities of agents, making them more effective across various domains and enhancing their overall performance in dynamic environments. Consequently, Ring stands as a pivotal advancement in the realm of intelligent agents, showcasing its versatility and reliability.

Tencent HY is a multifaceted family of extensive models crafted internally by Tencent to provide AI solutions specifically tailored for various enterprise requirements, spanning areas like content generation, business automation, and real-world agent services. The model integrates several modalities, including language processing, visual content, 3D modeling, and translation, merging Tencent’s proprietary algorithms with cutting-edge natural language processing and computer vision technologies to facilitate exceptional image generation, 3D content development, and intelligent applications. Through the Tencent Hunyuan AI Studio, users can interact with the model via an intuitive human-computer dialogue interface, enabling the system to interpret commands, execute tasks, assist in information retrieval, generate diverse content, and explore the model's vast capabilities within an easily navigable environment. Moreover, Tencent HY supports API integration and customizable parameter settings, improving accessibility and functionality for developers, product teams, and applications aimed at enterprises. This flexibility guarantees that a broad spectrum of users can harness the capabilities of Tencent HY in their initiatives, thereby fostering innovation and enhancing efficiency across various sectors. As a result, Tencent HY not only addresses current needs but also paves the way for future advancements in AI technology.

Ling 3.0 Flash is an evolved language model specifically designed for long-term agent tasks, featuring rapid response capabilities, low activation levels, and reliable tool utilization. With a Mixture-of-Experts architecture, it encompasses an impressive total of 124 billion parameters, activating 5.1 billion parameters for each token, which optimizes its performance while ensuring efficient inference. The model showcases a remarkable native context window of 256K tokens, expandable to a maximum of 1 million tokens, facilitating effective information retrieval from extensive contexts. In comparison to its earlier version, the original Flash model, Ling 3.0 Flash offers superior stability for extended operations, enhances tool-calling accuracy, adheres more closely to instructions, and shows improved compatibility with agent harnesses and coding tasks. Furthermore, its advanced spatial awareness capabilities allow it to construct grids of physical scenes and assess relative positions with precision, while its hybrid reasoning abilities increase success rates across various task complexities. This model not only represents a substantial advancement in language modeling technology but also ensures users can attain exceptional performance across a wide array of applications, thus broadening its potential use cases. Overall, Ling 3.0 Flash stands out as a groundbreaking development in the field, likely to influence future applications significantly.

Seed2.0 Pro is a production-grade, general-purpose AI agent built to tackle sophisticated real-world challenges at scale. It is specifically optimized for long-chain reasoning, enabling it to manage complex, multi-stage instructions without sacrificing accuracy or stability. As the most advanced model in the Seed 2.0 lineup, it delivers comprehensive improvements in multimodal understanding, spanning text, images, motion, and structured data. The model consistently achieves leading results across benchmarks in mathematics, coding competitions, scientific reasoning, visual puzzles, and document comprehension. Its visual intelligence allows it to analyze intricate charts, interpret spatial relationships, and recreate complete web interfaces from a single image while generating executable front-end code. Seed2.0 Pro also supports interactive and dynamic applications, including AI-driven coaching systems and advanced real-time visual analysis. In professional settings, it can automate CAD modeling workflows, extract geometric properties, and assist with scientific algorithm refinement. The system demonstrates strong performance in research-level tasks, extending beyond competition-style evaluations into high-economic-value applications. With enhanced instruction-following accuracy, it reliably executes detailed commands across technical, business, and analytical domains. Its long-context capabilities ensure coherence and reasoning stability across extended documents and multi-step processes. Designed for enterprise deployment, it balances depth of reasoning with operational efficiency and consistency. Altogether, Seed2.0 Pro represents a convergence of multimodal intelligence, agent autonomy, and production-ready robustness for advanced AI-driven workflows.

Xiaomi MiMo-V2.5-Pro is a cutting-edge open-source AI model built to handle complex reasoning, coding, and long-horizon tasks with high efficiency. It features a Mixture-of-Experts architecture with over one trillion total parameters and a large active parameter set for optimized performance. The model supports an extended context window of up to one million tokens, enabling it to process large amounts of information in a single workflow. It is designed for advanced agentic capabilities, allowing it to autonomously complete multi-step tasks over extended periods. MiMo-V2.5-Pro has demonstrated strong results in benchmarks related to software engineering, reasoning, and general AI performance. It is capable of building complete applications, optimizing engineering systems, and solving complex technical challenges. The model uses hybrid attention mechanisms to balance performance and efficiency across long contexts. It is also optimized for token efficiency, reducing resource usage while maintaining high-quality outputs. The model can integrate with development tools and frameworks to support real-world use cases. Xiaomi has open-sourced MiMo-V2.5-Pro, providing developers with access to its architecture, weights, and deployment tools. This allows organizations to customize and scale the model for their specific needs. Its ability to handle long workflows makes it suitable for tasks that require sustained reasoning and coordination. By combining scalability, efficiency, and advanced intelligence, MiMo-V2.5-Pro represents a significant advancement in open-source AI technology.

Xiaomi MiMo-V2.5 is a powerful open-source AI model designed to deliver advanced agentic capabilities alongside native multimodal understanding. It can process and reason across text, images, and audio within a unified system, enabling more complex and realistic interactions. The model is built using a sparse Mixture-of-Experts architecture with hundreds of billions of parameters, allowing it to scale efficiently while maintaining strong performance. It supports an extended context window of up to one million tokens, making it suitable for long-horizon tasks and detailed workflows. MiMo-V2.5 incorporates dedicated visual and audio encoders that enhance its ability to interpret and analyze multimodal inputs. It is capable of performing a wide range of tasks, including coding, reasoning, document analysis, and multimedia understanding. The model demonstrates strong benchmark performance across coding, reasoning, and multimodal evaluation tests. It is optimized for token efficiency, reducing computational cost while maintaining high-quality outputs. MiMo-V2.5 is designed to integrate with development tools and frameworks for real-world use cases. Xiaomi has released the model as open source, providing access to its weights, tokenizer, and architecture. This allows developers to customize and deploy the model for specific applications. Its ability to combine perception and reasoning makes it suitable for advanced AI workflows. By unifying multimodality and agentic intelligence, MiMo-V2.5 represents a significant advancement in open-source AI technology.

UnoRouter acts as a flexible entry point for engaging with a wide array of language models that are compatible with OpenAI. Users can harness the capabilities of more than 200 models from various providers such as OpenAI, Anthropic, Google, and others, all through a single API key, which enhances the usability of coding agents like Claude Code, Cline, Codex, and Kilo Code. By routing any OpenAI SDK to a specified base URL, users can easily switch between different models without altering their current codebase. Furthermore, UnoRouter incorporates a built-in chat and character client that enables users to create personas, manage lorebooks, and import SillyTavern cards, all while utilizing the same API key. The platform employs a usage-based pricing structure, which includes a complimentary tier, making it accessible for users to receive real-time updates on model availability and associated costs. This groundbreaking system streamlines the experience of working with numerous AI models for diverse use cases, making it an invaluable tool for developers. Moreover, UnoRouter's user-friendly interface is designed to enhance productivity and facilitate seamless integration across various applications.

The Ming-Flash Omni 2.0, created by Ant Group, embodies a cutting-edge large language model that functions within a unified multimodal framework, prioritizing the concept of “modal unity + task unity.” As the latest addition to the Ming series, this model is designed to foster a seamless understanding and generation of content across diverse modalities, such as text, images, audio, and video, thereby removing the necessity for various specialized models to carry out specific tasks like visual recognition, audio processing, verbal communication, and artistic creation. Building on advancements made by its earlier versions, Ming-Light Omni and Ming-Flash Omni Preview, this release not only confirms the viability of a consolidated architecture but also scales up to hundreds of billions of parameters while employing a Data Scaling strategy that achieves top-tier performance in open-source settings across a wide array of benchmarks. Significantly, the model features four critical capability modules: image-text comprehension, video interpretation, speech generation, and image creation or manipulation. To further improve image-text understanding, Ming utilizes structured knowledge graphs that enhance its ability to perceive visuals with greater depth. This pioneering methodology not only expands the model's range of applications but also establishes a new benchmark in the realm of artificial intelligence, pushing the boundaries of what is possible in multimodal learning. In doing so, it also opens up new avenues for research and development within the field.

Seed2.1 Turbo is a cutting-edge productivity AI designed to effectively address complex real-world issues through its powerful general-agent functionalities, programming skills, and multimodal capabilities. Unlike conventional models that typically focus on singular solutions, this advanced system is proficient in managing multi-step workflows to meet specific goals, thereby producing practical and actionable outcomes across diverse tools and environments. It proves to be beneficial in both professional and everyday scenarios, assisting with project management, document processing, data evaluation, solution creation, content structuring, tool application, and result synthesis. Furthermore, it thrives in educational, office, and research settings, enabling activities such as developing lesson-plan presentations, analyzing intricate spreadsheets, and producing thorough industry assessments. In the software engineering domain, Seed2.1 Turbo supports the entire project lifecycle, including requirements gathering, feature implementation, debugging, environment setup, terminal command execution, and result validation, while maintaining an in-depth comprehension of codebase structure, dependencies, and business logic for efficient modifications. This model's adaptability not only enhances productivity but also streamlines workflows, solidifying its position as an indispensable resource across a multitude of applications. Ultimately, its comprehensive capabilities empower users to fully harness AI technology in their daily tasks and long-term projects alike.

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.

Spawn is an advanced utility within OpenRouter that simplifies the deployment of AI coding agents on your infrastructure with just one command. Users can easily choose their preferred agent and select a cloud provider, after which Spawn manages the entire process by provisioning a virtual machine, installing the chosen agent along with its dependencies, and authenticating to both OpenRouter and the cloud through a CLI OAuth procedure. It also configures all necessary endpoints and model routing, and initiates an SSH session to allow immediate task execution. Each unique combination of agent and cloud is packaged in its own script, removing the need for Terraform or YAML files, which ensures that deployments are portable and straightforward. The range of supported agents includes Claude Code, OpenClaw, Codex CLI, OpenCode, Kilo Code, Hermes Agent, Junie, Pi, Cursor CLI, and T3 Code, enabling users to easily explore different coding agent workflows or switch between agents effortlessly. Furthermore, in addition to well-known cloud platforms such as DigitalOcean, Sprite, Hetzner Cloud, AWS Lightsail, GCP Compute Engine, and Daytona, Spawn also supports local installations and temporary local Docker environments. This wide-ranging flexibility guarantees that developers can select the most suitable environment for their specific requirements while optimizing their workflow efficiency. Consequently, Spawn emerges as a vital resource for developers looking to streamline their coding and deployment processes.

Ling 3.0 Tiny is an advanced reasoning model with open weights, consisting of 7.9 billion parameters in total and 1.3 billion that are active, while boasting a remarkable context window of 262,000 tokens. Utilizing a mixture-of-experts architecture, it expands the open-weights Pareto frontier in intelligence relative to its active parameters, all while maintaining a compact size suitable for deployment in various settings. With a score of 25 on the Artificial Analysis Intelligence Index, it rivals gpt-oss-120b, which has a score of 24, even though it uses 15 times fewer total parameters and 4 times fewer active parameters. This exceptional efficiency in parameters comes with a cost, as it demands a hefty 213 million output tokens to finalize the Intelligence Index evaluation. Moreover, Ling 3.0 Tiny shows significant progress in mitigating hallucination rates when compared to Ling-mini-2.0; it boosts its AA-Omniscience score by an impressive 59 points while maintaining consistent accuracy. Rather than resorting to random guesses in uncertain scenarios, the model opted to attempt only 37% of the posed questions during assessment, which resulted in a drastically lowered hallucination rate of 30%, a substantial improvement from the previous generation's staggering 96%. This strategic decision not only underscores the model's enhanced reasoning abilities but also emphasizes its potential for practical applications in the real world. Overall, Ling 3.0 Tiny exemplifies a significant step forward in the development of efficient and reliable AI models.

MiMo-V2-Omni is a next-generation multimodal AI model designed to handle complex, real-world tasks across multiple data types within a single unified framework. It supports inputs such as text, code, and structured data, enabling it to operate effectively across a wide range of applications, from development workflows to enterprise automation. The model is built with strong agentic capabilities, allowing it to orchestrate multi-step processes, interact with tools, and execute tasks autonomously. It combines advanced reasoning with contextual awareness, enabling it to break down complex problems and generate accurate, structured solutions. MiMo-V2-Omni is optimized for real-world performance, focusing on reliability, stability, and efficiency in practical scenarios. Its ability to maintain long-context understanding ensures consistency across extended interactions and workflows. The model also integrates seamlessly with external systems, enhancing its ability to automate tasks and streamline operations. With its multimodal capabilities, it can adapt to various industries and use cases, including coding, research, and business processes. It is designed to support scalable deployment, making it suitable for both individual users and enterprise environments. By combining intelligence, flexibility, and execution power, it enables more advanced AI-driven workflows. Its architecture emphasizes both performance and efficiency, ensuring fast and accurate results. Overall, MiMo-V2-Omni represents a significant step forward in building versatile, real-world AI systems.