Alternatives to Laguna XS.2

Devstral Small 2 serves as the streamlined, 24 billion-parameter version of Mistral AI's innovative coding-centric model lineup, released under the flexible Apache 2.0 license to facilitate both local implementations and API interactions. In conjunction with its larger counterpart, Devstral 2, this model introduces "agentic coding" features suitable for environments with limited computational power, boasting a generous 256K-token context window that allows it to comprehend and modify entire codebases effectively. Achieving a score of approximately 68.0% on the standard code-generation evaluation known as SWE-Bench Verified, Devstral Small 2 stands out among open-weight models that are significantly larger. Its compact size and efficient architecture enable it to operate on a single GPU or even in CPU-only configurations, making it an ideal choice for developers, small teams, or enthusiasts lacking access to expansive data-center resources. Furthermore, despite its smaller size, Devstral Small 2 successfully maintains essential functionalities of its larger variants, such as the ability to reason through multiple files and manage dependencies effectively, ensuring that users can still benefit from robust coding assistance. This blend of efficiency and performance makes it a valuable tool in the coding community.

Anthropic has introduced Claude Haiku 4.5, its newest small language model aimed at achieving near-frontier capabilities at a significantly reduced cost. This model mirrors the coding and reasoning abilities of the company's mid-tier Sonnet 4, yet operates at approximately one-third of the expense while delivering over double the processing speed. According to benchmarks highlighted by Anthropic, Haiku 4.5 either matches or surpasses the performance of Sonnet 4 in critical areas such as code generation and intricate "computer use" workflows. The model is specifically optimized for scenarios requiring real-time, low-latency performance, making it ideal for applications like chat assistants, customer support, and pair-programming. Available through the Claude API under the designation “claude-haiku-4-5,” Haiku 4.5 is designed for large-scale implementations where cost-effectiveness, responsiveness, and advanced intelligence are essential. Now accessible on Claude Code and various applications, this model's efficiency allows users to achieve greater productivity within their usage confines while still enjoying top-tier performance. Moreover, its launch marks a significant step forward in providing businesses with affordable yet high-quality AI solutions.

Kimi K2.6 is an advanced agentic AI model created by Moonshot AI, aiming to enhance practical implementation, programming, and complex reasoning compared to its predecessors, K2 and K2.5. This model is based on a Mixture-of-Experts framework and the multimodal, agent-centric principles of the Kimi series, merging language comprehension, coding capabilities, and tool utilization into one cohesive system that can plan and execute intricate workflows. It features enhanced reasoning skills and significantly better agent planning, enabling it to deconstruct tasks, synchronize various tools, and tackle multi-file or multi-step challenges with increased precision and effectiveness. Additionally, it provides robust tool-calling capabilities with a high degree of reliability, facilitating seamless integration with external platforms like web searches or APIs, and incorporates built-in validation systems to guarantee the accuracy of execution formats. Notably, Kimi K2.6 represents a significant leap forward in the realm of AI, setting new standards for the complexity and reliability of automated tasks.

Cursor has introduced Composer 2.5, a next-generation AI coding assistant built to deliver stronger reasoning, better collaboration, and improved reliability during software development tasks. The upgraded model performs better on long-running coding workflows and can manage complicated instructions with greater consistency than earlier Composer versions. Cursor expanded the training process by scaling compute resources, generating more advanced reinforcement learning environments, and refining behavioral traits that improve the developer experience. One of the key innovations in Composer 2.5 is its targeted textual feedback system, which helps the model learn from localized mistakes inside long coding trajectories instead of relying only on broad reward signals. This training method allows the AI to improve coding style, communication quality, and tool usage accuracy in a more focused way. The company also increased the amount of synthetic coding data by 25 times compared to Composer 2, giving the model exposure to more difficult and realistic programming tasks. During development, the system demonstrated sophisticated reasoning abilities by uncovering hidden implementation details and reverse-engineering deleted functionality inside synthetic environments. Composer 2.5 additionally uses advanced distributed training methods such as Sharded Muon and dual mesh HSDP to optimize large-scale model training performance. Available directly inside Cursor, the model comes in both standard and fast variants with different pricing tiers designed for developers, teams, and enterprise-scale engineering workflows.

Muse Spark is Meta’s first model in the Muse family, designed as a natively multimodal AI system focused on advanced reasoning and real-world applications. It combines text, visual understanding, and tool usage to provide more interactive and context-aware responses. The model introduces capabilities like visual chain-of-thought reasoning and multi-agent orchestration for complex problem-solving. Its Contemplating mode allows multiple AI agents to work in parallel, improving accuracy on challenging tasks. Muse Spark performs strongly across domains such as STEM reasoning, health insights, and multimodal perception. It can analyze images, generate interactive outputs, and assist with tasks like troubleshooting or educational content. The model is trained using improved pretraining, reinforcement learning, and efficient test-time reasoning techniques. It is designed to scale efficiently while delivering high performance with optimized compute usage. Safety measures include strong refusal behavior and alignment safeguards across high-risk domains. Overall, Muse Spark is a foundational step toward building personalized, highly capable AI systems.

Kimi K2.7 Code is a Moonshot AI coding model built to help developers handle software engineering, code generation, debugging, and agent-based development workflows. It focuses on long-horizon coding tasks, where an AI assistant needs to understand goals, work across many files, and complete multi-step development work. The model builds on the Kimi K2.6 architecture and is described as improving agentic capabilities while reducing thinking-token usage by about 30% compared with K2.6. Kimi K2.7 Code offers a 256K context window, which helps developers work with larger repositories, longer prompts, and more detailed project instructions. It can be accessed through Kimi Code, Moonshot’s API platform, and third-party model providers such as Together AI. The model also supports OpenAI- and Anthropic-compatible APIs, making it easier for teams to test it as a replacement or addition to existing coding assistant workflows. Developers who want to self-host or experiment with the model can access it through Hugging Face, where deployment guidance references vLLM, SGLang, and KTransformers. Kimi K2.7 Code is especially relevant for teams interested in open-source coding agents, long-context software tasks, and tool-integrated development. While some third-party commentary notes that benchmark claims should be reviewed carefully, the model is positioned as a strong option for developers seeking flexible, agentic coding support.

GLM-5.2 is a next-generation large language model built for users who need strong reasoning, coding support, and agentic AI capabilities. It can assist with complex software development tasks, technical problem-solving, automation workflows, and advanced research projects. The model is designed to process long-context information, which makes it helpful for analyzing large documents, reviewing codebases, and maintaining continuity across multi-step tasks. GLM-5.2 supports developers and organizations that want to create AI-powered tools capable of planning, reasoning, and executing more sophisticated workflows. Its architecture is structured to deliver high performance while improving efficiency for demanding AI use cases. Businesses can use GLM-5.2 to enhance productivity, streamline engineering processes, and build more capable intelligent applications. It is also useful for teams that need AI assistance across documentation, data interpretation, coding, testing, and workflow automation. The model’s emphasis on agentic engineering makes it well-suited for applications that require more than simple text generation. GLM-5.2 provides a flexible AI foundation for companies looking to bring advanced reasoning and automation into their products or internal operations.

GLM-5.1 represents the latest advancement in Z.ai’s GLM series, crafted as a cutting-edge, agent-focused AI model tailored for coding, reasoning, and managing long-term workflows. This iteration builds upon the framework of GLM-5, which employs a Mixture-of-Experts (MoE) architecture to achieve high performance without incurring excessive inference expenses, aligning with a larger initiative towards open-weight models that are accessible to developers. A significant emphasis of GLM-5.1 is on fostering agentic behavior, allowing it to plan, execute, and refine multi-step tasks instead of merely reacting to isolated prompts. Its capabilities are specifically engineered to manage intricate workflows, such as debugging code, exploring repositories, and performing sequential operations while maintaining context over time. In comparison to its predecessors, GLM-5.1 enhances reliability during lengthy interactions, ensuring coherence throughout extended sessions and minimizing failures in multi-step reasoning processes. Overall, this model signifies a leap forward in AI development, particularly in its ability to support complex task management seamlessly.

Gemma 4 is an advanced AI model developed by Google as part of its Gemini architecture, designed to deliver strong performance while remaining accessible to developers. The model is optimized to run on a single GPU or TPU, allowing more organizations and researchers to experiment with powerful AI technology. Gemma 4 improves natural language understanding and generation, making it suitable for applications such as chatbots, text analysis, and automated content creation. Its architecture enables the model to process complex language patterns while maintaining efficient computational performance. Developers can integrate Gemma 4 into various AI projects that require intelligent text processing or conversational capabilities. The model is designed with scalability in mind, allowing it to support both research experiments and production systems. By offering high-performance AI in a more accessible format, Gemma 4 lowers the barrier for developing sophisticated AI solutions. Its flexibility makes it useful for industries ranging from technology and education to business automation. Researchers can also use the model to explore new AI techniques and improve language processing systems. Overall, Gemma 4 represents a step forward in making powerful AI models easier to deploy and use.

Grok Build 0.1 is xAI’s purpose-built coding model created to support advanced software engineering and AI-driven development workflows. Unlike general-purpose language models, it focuses on agentic coding tasks where AI systems must plan, execute, and refine multiple steps to complete a project. The model can analyze both text and visual inputs, allowing it to work with source code, screenshots, technical diagrams, and project documentation. Developers can use it for activities such as debugging, code generation, refactoring, testing, and workflow automation. Grok Build 0.1 offers native support for tool calling and structured outputs, making it easier to integrate into development environments and automated systems. Its large 256K-token context window enables the model to understand extensive repositories and long development sessions without losing context. The platform is designed to work efficiently with coding agents that need to reason through problems rather than simply respond to prompts. xAI positions the model as a successor to earlier coding-focused Grok variants, with stronger support for agent-driven development processes. Grok Build 0.1 helps engineering teams accelerate software delivery while maintaining context across large and complex projects.

Qwen3.6 is an advanced AI model from Alibaba that builds on previous Qwen releases with a focus on real-world utility and performance. It is designed as a multimodal large language model capable of understanding and generating text while also processing visual and structured data. The model is optimized for coding tasks, enabling developers to handle complex, repository-level programming workflows. Qwen3.6 uses a mixture-of-experts (MoE) architecture, which activates only a portion of its parameters during inference to improve efficiency. This design allows it to deliver strong performance while reducing computational costs. It is available in both proprietary and open-weight versions, giving developers flexibility in deployment. The model supports integration into enterprise systems and cloud platforms, particularly within Alibaba’s ecosystem. Qwen3.6 also introduces stronger agentic capabilities, allowing it to perform multi-step reasoning and more autonomous task execution. It is designed to handle complex workflows, including engineering, analysis, and decision-making tasks. The model emphasizes stability and responsiveness based on developer feedback. Overall, Qwen3.6 provides a scalable and efficient AI solution for coding, automation, and multimodal applications.

North Mini Code marks the debut of Cohere’s agentic coding model tailored for developers and serves as the first entry in its next generation of robust models. This compact and efficient open-source solution is specifically crafted for the independent developer community, ensuring remarkable software development capabilities without the need for high-end hardware. Featuring a mixture-of-experts architecture, it comprises a total of 30 billion parameters, with 3 billion of those being active, thereby providing developers with powerful agentic coding functionalities in a streamlined package. The model is finely tuned for various tasks, including code generation, agentic software engineering, and terminal operations, boasting an impressive 256K context length and a maximum generation capacity of 64K. It is designed with real-world developer practices in mind, enabling tasks such as understanding and managing sub-agents, mapping out system architectures, conducting code reviews, and assisting coding agents in navigating intricate software challenges. The integration of these capabilities empowers developers to enhance their productivity and efficiency significantly in software development projects.

Poolside is an AI developer agent platform designed to bring intelligent coding assistance into the environments where software teams already build. Developers can use Poolside through the command line, popular IDEs, web access, and automated pipeline workflows. Its agents can assist with code generation, review, testing, automation, and repository-level tasks without forcing users to switch tools. The platform includes sandboxed execution so agent-generated code can be run and checked in a contained environment before it reaches production work. Teams can apply explicit permissions that define whether agents may read files, edit directories, execute commands, or access APIs. Poolside also adds checkpoints for AI-assisted work, making each agent action inspectable, reversible, and separate from standard Git history. The Poolside Console gives engineering leaders visibility into agent activity, policies, usage, costs, models, and compliance-related traceability. It supports Poolside’s own foundation models as well as third-party and self-hosted models through a consistent agent experience. With its developer-focused integrations and administrative oversight, Poolside helps teams adopt AI agents while maintaining control, security, and accountability.

Laguna M.1 stands out as Poolside's most proficient model for agentic coding, meticulously developed in-house specifically for enhancing software development workflows. This model features a total of 225 billion parameters, utilizing a Mixture of Experts architecture with 23 billion activated parameters, and has been trained entirely within the organization on a dataset consisting of 30 trillion tokens, leveraging the power of 6,144 interconnected NVIDIA H200 GPUs. Poolside undertook the task of training Laguna M.1 from the ground up, employing its proprietary data, dedicated training codebase, and an asynchronous on-policy reinforcement learning approach within its agent framework, all tailored for agentic coding applications. The design of the model ensures optimal performance within Poolside's coding agent, enabling it to effectively reason through software tasks, interact with various tools, edit code, execute tests, and facilitate extended autonomous development sessions. Specifically crafted for developers and teams tackling intricate coding challenges, Laguna M.1 offers enhanced capabilities in reasoning, architectural comprehension, terminal operations, and multi-step execution, surpassing what lighter models can achieve. Ultimately, its robust feature set positions it as an essential asset for those engaged in demanding software projects.

Qwen3-Coder-Next is a language model with open weights, crafted for coding agents and local development, which excels in advanced coding reasoning, adept tool usage, and effective handling of long-term programming challenges with remarkable efficiency, utilizing a mixture-of-experts framework that harmonizes robust capabilities with a resource-efficient approach. This model enhances the coding prowess of software developers, AI system architects, and automated coding processes, allowing them to generate, debug, and comprehend code with a profound contextual grasp while adeptly recovering from execution errors, rendering it ideal for autonomous coding agents and applications focused on development. Furthermore, Qwen3-Coder-Next achieves impressive performance on par with larger parameter models, but does so while consuming fewer active parameters, thus facilitating economical deployment for intricate and evolving programming tasks in both research and production settings, ultimately contributing to a more streamlined development process.

EXAONE Deep represents a collection of advanced language models that are enhanced for reasoning, created by LG AI Research, and come in sizes of 2.4 billion, 7.8 billion, and 32 billion parameters. These models excel in a variety of reasoning challenges, particularly in areas such as mathematics and coding assessments. Significantly, the EXAONE Deep 2.4B model outshines other models of its size, while the 7.8B variant outperforms both open-weight models of similar dimensions and the proprietary reasoning model known as OpenAI o1-mini. Furthermore, the EXAONE Deep 32B model competes effectively with top-tier open-weight models in the field. The accompanying repository offers extensive documentation that includes performance assessments, quick-start guides for leveraging EXAONE Deep models with the Transformers library, detailed explanations of quantized EXAONE Deep weights formatted in AWQ and GGUF, as well as guidance on how to run these models locally through platforms like llama.cpp and Ollama. Additionally, this resource serves to enhance user understanding and accessibility to the capabilities of EXAONE Deep models.

Qwen2 represents a collection of extensive language models crafted by the Qwen team at Alibaba Cloud. This series encompasses a variety of models, including base and instruction-tuned versions, with parameters varying from 0.5 billion to an impressive 72 billion, showcasing both dense configurations and a Mixture-of-Experts approach. The Qwen2 series aims to outperform many earlier open-weight models, including its predecessor Qwen1.5, while also striving to hold its own against proprietary models across numerous benchmarks in areas such as language comprehension, generation, multilingual functionality, programming, mathematics, and logical reasoning. Furthermore, this innovative series is poised to make a significant impact in the field of artificial intelligence, offering enhanced capabilities for a diverse range of applications.

Qwen3.5 represents a major advancement in open-weight multimodal AI models, engineered to function as a native vision-language agent system. Its flagship model, Qwen3.5-397B-A17B, leverages a hybrid architecture that fuses Gated DeltaNet linear attention with a high-sparsity mixture-of-experts framework, allowing only 17 billion parameters to activate during inference for improved speed and cost efficiency. Despite its sparse activation, the full 397-billion-parameter model achieves competitive performance across reasoning, coding, multilingual benchmarks, and complex agent evaluations. The hosted Qwen3.5-Plus version supports a one-million-token context window and includes built-in tool use for search, code interpretation, and adaptive reasoning. The model significantly expands multilingual coverage to 201 languages and dialects while improving encoding efficiency with a larger vocabulary. Native multimodal training enables strong performance in image understanding, video processing, document analysis, and spatial reasoning tasks. Its infrastructure includes FP8 precision pipelines and heterogeneous parallelism to boost throughput and reduce memory consumption. Reinforcement learning at scale enhances multi-step planning and general agent behavior across text and multimodal environments. Overall, Qwen3.5 positions itself as a high-efficiency foundation for autonomous digital agents capable of reasoning, searching, coding, and interacting with complex environments.

Infor® Point of Sale (POS) caters to both full-service and quick-service dining establishments, as well as managed food services in sectors like higher education, healthcare, and corporate environments. This innovative solution empowers these businesses to enhance their operational efficiency, surpass customer expectations, and gain valuable insights into their performance. Hospitality providers have the flexibility to deploy various service options, including terminals, kiosks, tableside, and poolside setups, allowing them to accommodate guests in virtually any location. The system is designed to help service staff become proficient quickly through a customizable and user-friendly interface. Additionally, it enables seamless global menu updates with a robust enterprise management platform, ensuring consistency across all service points. By offering adaptable services through fixed terminals, kiosks, tablets, and dual-mode terminals, Infor® POS ensures that businesses can meet the diverse needs of their clientele effectively. This comprehensive approach helps in fostering a better dining experience while also driving business growth.

Mistral Large 3 pushes open-source AI into frontier territory with a massive sparse MoE architecture that activates 41B parameters per token while maintaining a highly efficient 675B total parameter design. It sets a new performance standard by combining long-context reasoning, multilingual fluency across 40+ languages, and robust multimodal comprehension within a single unified model. Trained end-to-end on thousands of NVIDIA H200 GPUs, it reaches parity with top closed-source instruction models while remaining fully accessible under the Apache 2.0 license. Developers benefit from optimized deployments through partnerships with NVIDIA, Red Hat, and vLLM, enabling smooth inference on A100, H100, and Blackwell-class systems. The model ships in both base and instruct variants, with a reasoning-enhanced version on the way for even deeper analytical capabilities. Beyond general intelligence, Mistral Large 3 is engineered for enterprise customization, allowing organizations to refine the model on internal datasets or domain-specific tasks. Its efficient token generation and powerful multimodal stack make it ideal for coding, document analysis, knowledge workflows, agentic systems, and multilingual communications. With Mistral Large 3, organizations can finally deploy frontier-class intelligence with full transparency, flexibility, and control.

Customers can scan a QR code on a tabletop or tap an NFC tag using their smart phones to access a digital menu that is vividly illustrated. There is no need to download any apps. The order is sent directly to the vendor (a restaurant, fresh food producer, or other supplier) where it will be processed as any other delivery service. Customers are notified about the status of their orders and receive feedback surveys to ensure customer satisfaction. RoomOrders is a global leader in digital resort and hotel ecosystems. It connects hotels with their facilities, such as restaurants, gift shops and golf courses.

MiniMax M3 is a frontier open-weight AI model built for coding, agentic work, multimodal understanding, and ultra-long-context tasks. The model supports up to a 1 million token context window, allowing it to work across large codebases, long documents, logs, project histories, and complex task environments. MiniMax M3 introduces MiniMax Sparse Attention, a sparse attention architecture designed to make long-context processing more efficient. The model is natively multimodal, with training that supports deeper semantic fusion across text, image, and video inputs. It is designed to support software engineering tasks, repository analysis, terminal-style work, browser-style retrieval, tool use, and autonomous workflows. MiniMax M3 has a mixture-of-experts architecture with hundreds of billions of total parameters and a smaller activated parameter count for more efficient inference. Developers can use it for AI coding assistants, workflow automation, research agents, document analysis, visual reasoning, and enterprise AI systems. Its long-context capability makes it especially useful when tasks require many files, references, instructions, or interaction histories to stay available at once. MiniMax M3 helps teams build more capable AI agents that can understand larger problems, work across multiple modalities, and execute complex tasks with stronger context awareness.

Tülu 3 is a cutting-edge language model created by the Allen Institute for AI (Ai2) that aims to improve proficiency in fields like knowledge, reasoning, mathematics, coding, and safety. It is based on the Llama 3 Base and undergoes a detailed four-stage post-training regimen: careful prompt curation and synthesis, supervised fine-tuning on a wide array of prompts and completions, preference tuning utilizing both off- and on-policy data, and a unique reinforcement learning strategy that enhances targeted skills through measurable rewards. Notably, this open-source model sets itself apart by ensuring complete transparency, offering access to its training data, code, and evaluation tools, thus bridging the performance divide between open and proprietary fine-tuning techniques. Performance assessments reveal that Tülu 3 surpasses other models with comparable sizes, like Llama 3.1-Instruct and Qwen2.5-Instruct, across an array of benchmarks, highlighting its effectiveness. The continuous development of Tülu 3 signifies the commitment to advancing AI capabilities while promoting an open and accessible approach to technology.

While lounging by the pool at their neighborhood leisure facility, they recognized the demand for a streamlined software solution that could assist sports instructors, front desk staff, parents, and students alike in simplifying the management of sports programs. Since that pivotal moment, CoursePro has expanded to serve over 3 million users and is implemented in numerous locations globally. If you seek exceptional software, perhaps it’s time for you to become part of the growing community. The platform includes a Teacher Portal app that enables educators to efficiently manage attendance, evaluate students, adhere to teaching schedules, and much more. The HomePortal feature monitors student progress, provides feedback, and organizes lessons, ensuring a seamless connection with your members while alleviating the workload for teachers. The inception of CoursePro stems from a couple of individuals who, witnessing the chaos of scattered paperwork, envisioned a more efficient alternative for managing swimming classes. This innovative approach not only revolutionized their local center but also set a benchmark for sporting institutions everywhere.

The Mixtral 8x7B model is an advanced sparse mixture of experts (SMoE) system that boasts open weights and is released under the Apache 2.0 license. This model demonstrates superior performance compared to Llama 2 70B across various benchmarks while achieving inference speeds that are six times faster. Recognized as the leading open-weight model with a flexible licensing framework, Mixtral also excels in terms of cost-efficiency and performance. Notably, it competes with and often surpasses GPT-3.5 in numerous established benchmarks, highlighting its significance in the field. Its combination of accessibility, speed, and effectiveness makes it a compelling choice for developers seeking high-performing AI solutions.

We are excited to unveil Codestral, our inaugural code generation model. This open-weight generative AI system is specifically crafted for tasks related to code generation, enabling developers to seamlessly write and engage with code via a unified instruction and completion API endpoint. As it becomes proficient in both programming languages and English, Codestral is poised to facilitate the creation of sophisticated AI applications tailored for software developers. With a training foundation that encompasses a wide array of over 80 programming languages—ranging from widely-used options like Python, Java, C, C++, JavaScript, and Bash to more niche languages such as Swift and Fortran—Codestral ensures a versatile support system for developers tackling various coding challenges and projects. Its extensive language capabilities empower developers to confidently navigate different coding environments, making Codestral an invaluable asset in the programming landscape.

OLMo 2 represents a collection of completely open language models created by the Allen Institute for AI (AI2), aimed at giving researchers and developers clear access to training datasets, open-source code, reproducible training methodologies, and thorough assessments. These models are trained on an impressive volume of up to 5 trillion tokens and compete effectively with top open-weight models like Llama 3.1, particularly in English academic evaluations. A key focus of OLMo 2 is on ensuring training stability, employing strategies to mitigate loss spikes during extended training periods, and applying staged training interventions in the later stages of pretraining to mitigate weaknesses in capabilities. Additionally, the models leverage cutting-edge post-training techniques derived from AI2's Tülu 3, leading to the development of OLMo 2-Instruct models. To facilitate ongoing enhancements throughout the development process, an actionable evaluation framework known as the Open Language Modeling Evaluation System (OLMES) was created, which includes 20 benchmarks that evaluate essential capabilities. This comprehensive approach not only fosters transparency but also encourages continuous improvement in language model performance.

ClinePass is a subscription service that provides access to open weight models within Cline, aimed at offering developers ample quotas and dependable access to powerful coding models without the hassle of managing different provider setups or API keys. Tailored for use with Cline IDE and CLI, this service allows developers to transition from registration to coding in just a few minutes; simply create an account, install Cline, choose the ClinePass provider, and begin coding. The platform features an agent harness optimized for open-weight model workflows, streamlining the development process. ClinePass encompasses a variety of open weight models from notable sources such as Z.ai, Moonshot AI, DeepSeek, MiniMax, MiMo, and Qwen. Among these models are GLM 5.2 for advanced reasoning, Kimi K2.7 Code specifically for coding tasks, and Kimi K2.6 designed for agentic workflows. Additionally, the service includes DeepSeek V4 Pro for handling extensive changes, DeepSeek V4 Flash for rapid iteration, MiniMax M3 catering to general coding needs, MiMo V2.5 Pro for professional workloads, MiMo V2.5 for efficient editing, Qwen3.7-Max suited for demanding tasks, and Qwen3.7-Plus offering a balanced approach to coding. This diverse array of models ensures that developers have the tools they need for a wide range of programming challenges.

Tinker is an innovative training API tailored for researchers and developers, providing comprehensive control over model fine-tuning while simplifying the complexities of infrastructure management. It offers essential primitives that empower users to create bespoke training loops, supervision techniques, and reinforcement learning workflows. Currently, it facilitates LoRA fine-tuning on open-weight models from both the LLama and Qwen families, accommodating a range of model sizes from smaller variants to extensive mixture-of-experts configurations. Users can write Python scripts to manage data, loss functions, and algorithmic processes, while Tinker autonomously takes care of scheduling, resource distribution, distributed training, and recovery from failures. The platform allows users to download model weights at various checkpoints without the burden of managing the computational environment. Delivered as a managed service, Tinker executes training jobs on Thinking Machines’ proprietary GPU infrastructure, alleviating users from the challenges of cluster orchestration and enabling them to focus on building and optimizing their models. This seamless integration of capabilities makes Tinker a vital tool for advancing machine learning research and development.

Mistral 3 represents the newest iteration of open-weight AI models developed by Mistral AI, encompassing a diverse range of models that span from compact, edge-optimized versions to a leading large-scale multimodal model. This lineup features three efficient “Ministral 3” models with 3 billion, 8 billion, and 14 billion parameters, tailored for deployment on devices with limited resources, such as laptops, drones, or other edge devices. Additionally, there is the robust “Mistral Large 3,” which is a sparse mixture-of-experts model boasting a staggering 675 billion total parameters, with 41 billion of them being active. These models are designed to handle multimodal and multilingual tasks, excelling not only in text processing but also in image comprehension, and they have showcased exceptional performance on general queries, multilingual dialogues, and multimodal inputs. Furthermore, both the base and instruction-fine-tuned versions are made available under the Apache 2.0 license, allowing for extensive customization and integration into various enterprise and open-source initiatives. This flexibility in licensing encourages innovation and collaboration among developers and organizations alike.

Reka Flash 3 is a cutting-edge multimodal AI model with 21 billion parameters, crafted by Reka AI to perform exceptionally well in tasks such as general conversation, coding, following instructions, and executing functions. This model adeptly handles and analyzes a myriad of inputs, including text, images, video, and audio, providing a versatile and compact solution for a wide range of applications. Built from the ground up, Reka Flash 3 was trained on a rich array of datasets, encompassing both publicly available and synthetic information, and it underwent a meticulous instruction tuning process with high-quality selected data to fine-tune its capabilities. The final phase of its training involved employing reinforcement learning techniques, specifically using the REINFORCE Leave One-Out (RLOO) method, which combined both model-based and rule-based rewards to significantly improve its reasoning skills. With an impressive context length of 32,000 tokens, Reka Flash 3 competes effectively with proprietary models like OpenAI's o1-mini, making it an excellent choice for applications requiring low latency or on-device processing. The model operates at full precision with a memory requirement of 39GB (fp16), although it can be efficiently reduced to just 11GB through the use of 4-bit quantization, demonstrating its adaptability for various deployment scenarios. Overall, Reka Flash 3 represents a significant advancement in multimodal AI technology, capable of meeting diverse user needs across multiple platforms.

Qwen3-Coder is a versatile coding model that comes in various sizes, prominently featuring the 480B-parameter Mixture-of-Experts version with 35B active parameters, which naturally accommodates 256K-token contexts that can be extended to 1M tokens. This model achieves impressive performance that rivals Claude Sonnet 4, having undergone pre-training on 7.5 trillion tokens, with 70% of that being code, and utilizing synthetic data refined through Qwen2.5-Coder to enhance both coding skills and overall capabilities. Furthermore, the model benefits from post-training techniques that leverage extensive, execution-guided reinforcement learning, which facilitates the generation of diverse test cases across 20,000 parallel environments, thereby excelling in multi-turn software engineering tasks such as SWE-Bench Verified without needing test-time scaling. In addition to the model itself, the open-source Qwen Code CLI, derived from Gemini Code, empowers users to deploy Qwen3-Coder in dynamic workflows with tailored prompts and function calling protocols, while also offering smooth integration with Node.js, OpenAI SDKs, and environment variables. This comprehensive ecosystem supports developers in optimizing their coding projects effectively and efficiently.

MAI-Thinking-1 represents Microsoft AI's advanced reasoning model, specifically engineered to tackle intricate and significant challenges, exhibiting superior reasoning capabilities alongside robust software engineering performance within its category. This model features a configuration of 35 billion active parameters and roughly 1 trillion total parameters as a sparse Mixture of Experts, allowing it to maintain a more streamlined inference footprint compared to much larger alternatives while still achieving performance comparable to leading models on essential software engineering benchmarks. Microsoft developed MAI-Thinking-1 from the ground up, utilizing high-quality, enterprise-grade, commercially licensed data, ensuring that its abilities are acquired rather than derived from third-party models. Integral to Microsoft AI’s innovative Hill-Climbing Machine, this model benefits from a collaborative development process designed for ongoing and reliable enhancements throughout all stages of model creation. MAI-Thinking-1 is particularly suited for agentic coding environments, as it is capable of reading code, modifying files, executing tests, detecting errors, and recovering from mistakes made along the way. This ability to adapt and learn in real-time makes it a valuable asset for developers seeking efficiency and reliability in their projects.

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.

Qwen3.6-27B is an open-source, dense multimodal language model from the Qwen3.6 series, engineered to provide top-tier performance in areas such as coding, reasoning, and agent-driven workflows, all while maintaining an efficient parameter count of 27 billion. This model is recognized for its ability to outperform or compete closely with much larger counterparts on essential benchmarks, particularly excelling in agent-based coding tasks. It features dual operational modes—thinking and non-thinking—that enable it to effectively adapt its reasoning depth and response speed based on the specific requirements of each task. Additionally, it supports a variety of input types, including text, images, and video, showcasing its versatility. As part of the Qwen3.6 lineup, this model prioritizes practical usability, consistency, and the enhancement of developer productivity, reflecting advancements inspired by community insights and real-world application demands. Its innovative design not only responds to immediate user needs but also anticipates future trends in AI development.

Phi-4-reasoning is an advanced transformer model featuring 14 billion parameters, specifically tailored for tackling intricate reasoning challenges, including mathematics, programming, algorithm development, and strategic planning. Through a meticulous process of supervised fine-tuning on select "teachable" prompts and reasoning examples created using o3-mini, it excels at generating thorough reasoning sequences that optimize computational resources during inference. By integrating outcome-driven reinforcement learning, Phi-4-reasoning is capable of producing extended reasoning paths. Its performance notably surpasses that of significantly larger open-weight models like DeepSeek-R1-Distill-Llama-70B and nears the capabilities of the comprehensive DeepSeek-R1 model across various reasoning applications. Designed for use in settings with limited computing power or high latency, Phi-4-reasoning is fine-tuned with synthetic data provided by DeepSeek-R1, ensuring it delivers precise and methodical problem-solving. This model's ability to handle complex tasks with efficiency makes it a valuable tool in numerous computational contexts.

Cognition has unveiled SWE-1.5, the newest agent-model specifically designed for software engineering, featuring an expansive "frontier-size" architecture composed of hundreds of billions of parameters and an end-to-end optimization (encompassing the model, inference engine, and agent harness) that enhances both speed and intelligence. This model showcases nearly state-of-the-art coding capabilities and establishes a new standard for latency, achieving inference speeds of up to 950 tokens per second, which is approximately six times quicker than its predecessor, Haiku 4.5, and thirteen times faster than Sonnet 4.5. Trained through extensive reinforcement learning in realistic coding-agent environments that incorporate multi-turn workflows, unit tests, and quality assessments, SWE-1.5 also leverages integrated software tools and high-performance hardware, including thousands of GB200 NVL72 chips paired with a custom hypervisor infrastructure. Furthermore, its innovative architecture allows for more effective handling of complex coding tasks and improves overall productivity for software development teams. This combination of speed, efficiency, and intelligent design positions SWE-1.5 as a game changer in the realm of coding models.

Kimi K2 represents a cutting-edge series of open-source large language models utilizing a mixture-of-experts (MoE) architecture, with a staggering 1 trillion parameters in total and 32 billion activated parameters tailored for optimized task execution. Utilizing the Muon optimizer, it has been trained on a substantial dataset of over 15.5 trillion tokens, with its performance enhanced by MuonClip’s attention-logit clamping mechanism, resulting in remarkable capabilities in areas such as advanced knowledge comprehension, logical reasoning, mathematics, programming, and various agentic operations. Moonshot AI offers two distinct versions: Kimi-K2-Base, designed for research-level fine-tuning, and Kimi-K2-Instruct, which is pre-trained for immediate applications in chat and tool interactions, facilitating both customized development and seamless integration of agentic features. Comparative benchmarks indicate that Kimi K2 surpasses other leading open-source models and competes effectively with top proprietary systems, particularly excelling in coding and intricate task analysis. Furthermore, it boasts a generous context length of 128 K tokens, compatibility with tool-calling APIs, and support for industry-standard inference engines, making it a versatile option for various applications. The innovative design and features of Kimi K2 position it as a significant advancement in the field of artificial intelligence language processing.

LongCat-2.0 represents a significant advancement in the realm of language models, featuring a staggering 1.6 trillion parameters through a Mixture-of-Experts architecture that leverages AI ASIC superpods, with approximately 48 billion parameters engaged per token, showcasing exceptional capabilities in coding and agentic tasks. This model marks a notable improvement over its predecessors by integrating a large-scale sparse architecture with specialized post-training methods tailored for tasks in real-world software development, tool utilization, long-context reasoning, and complex agent workflows. Entirely developed and executed on AI ASIC superpods, LongCat-2.0 underwent pretraining that encompassed over 35 trillion tokens and millions of accelerator hours, exemplifying cutting-edge training methodologies on innovative hardware solutions. To enhance its performance on tasks requiring long-term context, the model incorporates LongCat Sparse Attention and is trained using hundreds of billions of tokens from 1M-context datasets, enabling it to effectively manage ultra-long context tasks and ensure robust understanding of lengthy documents. This combination of features positions LongCat-2.0 as a pioneering force in the landscape of advanced language models.

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.

Phi-4-reasoning-plus is an advanced reasoning model with 14 billion parameters, enhancing the capabilities of the original Phi-4-reasoning. It employs reinforcement learning for better inference efficiency, processing 1.5 times the number of tokens compared to its predecessor, which results in improved accuracy. Remarkably, this model performs better than both OpenAI's o1-mini and DeepSeek-R1 across various benchmarks, including challenging tasks in mathematical reasoning and advanced scientific inquiries. Notably, it even outperforms the larger DeepSeek-R1, which boasts 671 billion parameters, on the prestigious AIME 2025 assessment, a qualifier for the USA Math Olympiad. Furthermore, Phi-4-reasoning-plus is accessible on platforms like Azure AI Foundry and HuggingFace, making it easier for developers and researchers to leverage its capabilities. Its innovative design positions it as a top contender in the realm of reasoning models.

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.

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.

The Hy3 preview represents Tencent Hy's most advanced model in the Hy series to date, featuring a substantial 295 billion parameters in a Mixture-of-Experts structure, with 21 billion parameters activated and an impressive 3.8 billion parameters dedicated to the MTP layer, all while accommodating a context window of up to 256,000 tokens. This groundbreaking model is the first to harness Tencent Hy's newly revamped infrastructure, aimed at enhancing practical applications in areas such as complex reasoning, following instructions, learning from context, coding tasks, and overall inference capabilities. By seamlessly integrating both rapid and thorough cognitive processing, it provides straightforward answers for simpler inquiries while facilitating in-depth analysis for intricate math, programming, and reasoning challenges. The model is crafted to exhibit comprehensive skills in understanding long contexts, adhering to instructions, employing tools, and executing agent workflows, with assessments conducted not only against conventional benchmarks but also within real-world business and development contexts. Furthermore, its design ensures adaptability to a wide range of scenarios, thereby broadening its usability in diverse applications.

Liquid AI's LFM2.5 represents an advanced iteration of on-device AI foundation models, engineered to provide high-efficiency and performance for AI inference on edge devices like smartphones, laptops, vehicles, IoT systems, and embedded hardware without the need for cloud computing resources. This new version builds upon the earlier LFM2 framework by greatly enhancing the scale of pretraining and the stages of reinforcement learning, resulting in a suite of hybrid models that boast around 1.2 billion parameters while effectively balancing instruction adherence, reasoning skills, and multimodal functionalities for practical applications. The LFM2.5 series comprises various models including Base (for fine-tuning and personalization), Instruct (designed for general-purpose instruction), Japanese-optimized, Vision-Language, and Audio-Language variants, all meticulously crafted for rapid on-device inference even with stringent memory limitations. These models are also made available as open-weight options, facilitating deployment through platforms such as llama.cpp, MLX, vLLM, and ONNX, thus ensuring versatility for developers. With these enhancements, LFM2.5 positions itself as a robust solution for diverse AI-driven tasks in real-world environments.