List of the Top AI Models for Nonprofit in 2026 - Page 13

Qwen3.6-27B stands as an open-source, dense multimodal language model within the Qwen3.6 lineup, crafted to deliver exceptional capabilities in coding, reasoning, and workflows driven by agents, all while utilizing a streamlined parameter count of 27 billion. This model is distinguished by its performance, often surpassing or closely rivaling larger models on critical benchmarks, especially in tasks that involve agent-based coding. It operates in two distinct modes—thinking and non-thinking—allowing it to adjust the depth of its reasoning and the speed of its responses to align with the specific demands of various tasks. Furthermore, it accommodates a broad range of input formats, which includes text, images, and video, demonstrating its adaptability. As an integral part of the Qwen3.6 series, this model emphasizes practical functionality, reliability, and the boost of developer efficiency, drawing on feedback from the community and the practical needs of real-world applications. Its forward-thinking design not only addresses current user requirements but also foresees future developments in the realm of artificial intelligence, ensuring that it remains relevant and effective over time. Thus, Qwen3.6-27B represents a significant step forward in the evolution of language models, integrating innovative features that enhance user interaction and streamline workflows.

Sarvam-30B is a cutting-edge open-source language model designed as a robust platform for real-time conversational AI and intricate reasoning tasks, highlighting its effectiveness in multilingual environments and practical applications. With its impressive 30 billion parameters, the model leverages a Mixture-of-Experts (MoE) approach that activates only a fraction of its parameters for each interaction, enabling high efficiency and minimal latency, making it ideal for use in resource-constrained settings such as local devices and edge computing systems. It stands out in a variety of conversational scenarios, programming challenges, and logical reasoning tasks, delivering remarkable performance in more than 20 Indian languages, which highlights its versatility for multilingual use and voice recognition systems. Its dual-tier architecture positions it as a rapid and easily deployable "conversational workhorse," employing MoE strategies to reduce computational demands while maintaining top-notch performance. This innovative model not only improves the overall user experience but also expands its accessibility across a wide range of linguistic contexts, making it a valuable tool for developers and businesses aiming to engage diverse audiences effectively. Additionally, Sarvam-30B's design allows for continuous improvement and adaptation, ensuring that it remains relevant in the ever-evolving landscape of AI technology.

Sarvam-105B is recognized as the leading large language model in Sarvam's collection of open-source tools, crafted to deliver outstanding reasoning skills, multilingual understanding, and agent-driven functionality within a cohesive and scalable system. This Mixture-of-Experts (MoE) architecture features an astonishing 105 billion parameters, activating only a portion for each token processed, which ensures remarkable computational efficiency while handling complex tasks. It is specifically tailored for sophisticated reasoning, programming, mathematical problem-solving, and agentic functions, making it ideal for situations that require multi-step solutions and structured outputs instead of just basic dialogue. With an impressive capacity to process lengthy contexts of around 128K tokens, Sarvam-105B is adept at managing extensive texts, lengthy conversations, and intricate analytical tasks, maintaining coherence throughout these engagements. Furthermore, its versatile design allows for a wide array of applications, equipping users with powerful tools to address a multitude of intellectual challenges. This flexibility enhances its utility across various domains, further solidifying its status as a premier choice for advanced language model needs.

BharatGPT represents a state-of-the-art generative AI platform specifically designed for the rich linguistic, cultural, and operational diversity found in India, effectively merging the functionalities of large language models with multimodal interaction capabilities that include text, voice, and video formats. This groundbreaking project has emerged from a partnership between academic institutions, industry leaders, and governmental support, with the goal of creating a sustainable AI ecosystem that addresses the distinct requirements of the Indian community and various business applications. By emphasizing communication and automation across multiple Indian languages, it caters to practical usage scenarios, such as code-mixed speech like Hinglish and numerous regional dialects, thus enhancing its reach beyond the conventional English-centric models. Serving a dual function as both a conversational AI and a solution ready for enterprise use, BharatGPT is tailored to integrate seamlessly with business frameworks like ERP and CRM, enabling smooth and efficient real-time transactional activities. Furthermore, its design underscores a dedication to inclusivity, ensuring that individuals from diverse linguistic backgrounds can fully utilize and benefit from its advanced features, fostering a more connected and technologically adept society. Ultimately, BharatGPT stands as a testament to the potential of AI to transform communication and business practices in a country as multifaceted as India.

KAT-Coder is an advanced AI coding solution that goes beyond traditional autocomplete features by enabling a thorough software development workflow that incorporates reasoning, planning, and execution. This innovative system is recognized as the leading coding model in the KAT ecosystem, designed specifically for "agentic coding," which empowers the model to generate code snippets while also diagnosing issues, proposing solutions, performing tests, and refining various files throughout an ongoing development cycle. Through its seamless integration into developer environments via API endpoints and proxy layers compatible with tools like Claude Code, developers can retain their familiar workflows without the need to change their interfaces. KAT-Coder utilizes a sophisticated multi-stage training pipeline that merges supervised fine-tuning with extensive reinforcement learning, allowing it to understand programming contexts and effectively manage complex tasks. As a result, KAT-Coder significantly boosts productivity and equips developers with the freedom to concentrate on the more creative elements of their projects. Moreover, its adaptive capabilities ensure that developers can continuously improve their coding practices, which leads to even more innovative solutions.

Gemini Deep Research showcases Google's cutting-edge autonomous research agent designed to intelligently plan, implement, and compile complex, multi-step research projects by utilizing both online information and proprietary data sources, which ultimately leads to high-quality and well-organized results. By harnessing the power of advanced Gemini models, including Gemini 3.1 Pro, the system breaks down a user's inquiry into smaller, manageable tasks, diligently searches various information sources, evaluates their relevance, and refines the findings through a series of iterative steps before presenting a comprehensive and well-cited report. This innovative tool is recognized as a noteworthy leap forward in research methodologies, enabling thorough exploration of not just public web information but also customized enterprise data, while maintaining clarity and coherence throughout intricate reasoning processes. In addition to its foundational features, it incorporates enhancements such as MCP (Model Context Protocol) integration, dynamic visualizations, and significant improvements in analytical capabilities, which empower users to effectively derive meaningful insights. Consequently, these advancements not only streamline research workflows but also ensure that the outcomes are both detailed and actionable, ultimately transforming the way research is conducted. Furthermore, this tool empowers researchers to adapt their approaches based on the evolving landscape of information, reinforcing its value in the modern research environment.

GPT-Realtime-1.5 is OpenAI’s flagship real-time voice model, designed to deliver high-quality audio interactions for applications like voice assistants, customer support systems, and conversational AI platforms. It supports multimodal inputs, including text, audio, and images, and can generate both text and audio outputs for seamless communication. The model is optimized for fast response times, making it ideal for live, interactive environments where latency is critical. With a 32,000-token context window, it can handle extended conversations and maintain context across multiple turns. It is capable of powering complex workflows by integrating with external tools through function calling. The model is accessible عبر multiple API endpoints, including realtime, chat completions, and responses, providing flexibility for developers. Pricing is based on token usage, with distinct rates for text, audio, and image inputs and outputs. It supports scalable deployment with tiered rate limits that increase based on usage levels. While it does not support features like fine-tuning or structured outputs, it remains highly effective for real-time applications. Its ability to process and respond to audio input makes it particularly valuable for voice-driven interfaces. Developers can use it to build interactive systems that respond instantly to user input. The model’s performance and speed make it suitable for high-demand environments such as call centers and live support systems. Overall, gpt-realtime-1.5 provides a robust foundation for building responsive, scalable, and intelligent voice applications.

DeepSeek-V4-Pro is a next-generation Mixture-of-Experts language model designed to deliver high performance across reasoning, coding, and long-context AI tasks. It features a massive architecture with 1.6 trillion total parameters and 49 billion activated parameters, enabling efficient computation while maintaining strong capabilities. The model supports an industry-leading context window of up to one million tokens, allowing it to process extremely large datasets, documents, and workflows. Its hybrid attention mechanism combines advanced techniques to optimize long-context efficiency and reduce computational requirements. DeepSeek-V4-Pro is trained on over 32 trillion tokens, enhancing its knowledge base and reasoning abilities. It incorporates advanced optimization methods to improve training stability and convergence. The model supports multiple reasoning modes, including fast responses and deep analytical thinking for complex problem solving. It performs strongly across benchmarks in coding, mathematics, and knowledge-based tasks. The architecture is designed for agentic workflows, enabling it to handle multi-step tasks and tool-based interactions. As an open-source model, it offers flexibility for customization and deployment across various environments. It also supports efficient memory usage and reduced inference costs compared to previous versions. The model’s capabilities make it suitable for both research and enterprise applications. Overall, DeepSeek-V4-Pro represents a significant advancement in scalable, high-performance AI with long-context intelligence.

DeepSeek-V4-Flash is a next-generation Mixture-of-Experts language model engineered for high efficiency, scalability, and long-context intelligence. It consists of 284 billion total parameters with 13 billion activated parameters, enabling optimized performance with reduced computational overhead. The model supports an industry-leading context window of up to one million tokens, allowing it to process extensive datasets and complex workflows seamlessly. Its hybrid attention architecture combines advanced techniques to improve long-context efficiency and reduce memory usage. DeepSeek-V4-Flash is trained on over 32 trillion tokens, enhancing its capabilities in reasoning, coding, and knowledge-based tasks. It incorporates advanced optimization methods for stable training and faster convergence. The model supports multiple reasoning modes, including fast responses and deeper analytical processing for complex problems. While slightly less powerful than its Pro counterpart, it achieves comparable reasoning performance when given more computation budget. It is designed for agentic workflows, enabling multi-step reasoning and tool-based interactions. The model is well-suited for scalable deployments where performance and cost efficiency are both important. As an open-source solution, it offers flexibility for customization across various environments. It also reduces inference cost and resource usage compared to larger models. Overall, DeepSeek-V4-Flash delivers a strong balance of speed, efficiency, and capability for real-world AI use cases.

The Cartesia Sonic-3 represents a cutting-edge advancement in real-time text-to-speech (TTS) technology, delivering remarkably lifelike and expressive voice outputs with minimal latency, thus facilitating AI systems to participate in discussions that closely mimic human dialogue. Employing a complex state space model architecture, this innovative solution ensures high-quality speech synthesis, allowing audio generation to initiate within a rapid timeframe of 40 to 100 milliseconds, which fosters a seamless conversational flow devoid of any perceptible interruptions. Designed explicitly for conversational AI scenarios, Sonic-3 acts as the vocal interface for AI agents, transforming written language into speech that captures a wide array of emotions such as enthusiasm, compassion, and even laughter. Furthermore, with its support for over 40 languages and the capability to adapt to various accents, developers are equipped to create applications that deliver outstanding quality and accessibility for users worldwide. This adaptability not only fulfills the diverse requirements of numerous markets but also significantly boosts user engagement through its remarkably realistic vocal outputs. As a result, the Sonic-3 model stands out as a powerful tool in enhancing communication between AI and users.

Cartesia Ink offers a collection of advanced real-time streaming speech-to-text (STT) models that enable quick and fluid conversations in voice AI applications, acting as the vital "voice input" layer that accurately converts spoken language into text instantly. The standout model, Ink-Whisper, is designed specifically for conversational environments, achieving an impressive transcription latency of only 66 milliseconds, which promotes fluid, human-like exchanges without noticeable delays. Unlike traditional transcription systems that focus on batch processing, Ink is specifically engineered for real-time communication, skillfully handling fragmented and diverse audio using a pioneering dynamic chunking technique that reduces errors and boosts responsiveness, especially during pauses, interruptions, or rapid dialogues. As a result, this cutting-edge technology guarantees that users enjoy a more seamless and interactive experience, catering to the evolving requirements of contemporary communication. Furthermore, the ability of Ink to adapt to various speaking styles and environments makes it an invaluable tool in the realm of voice AI.

Velma is a cutting-edge AI model developed by Modulate, operating within an extensive voice intelligence framework that interprets conversations directly from audio input instead of relying on text transcriptions. Unlike traditional approaches that convert spoken language into text for analysis by language models, Velma utilizes an Ensemble Listening Model (ELM) characterized by a distinctive architecture that can simultaneously process various dimensions of voice, including tone, emotion, pacing, intent, and behavioral signals. This sophisticated ability allows it to capture the full essence of a conversation, transcending mere words to recognize subtle cues such as stress, deceit, sarcasm, or escalation as they unfold. Velma accomplishes this feat by integrating numerous specialized detectors, each focused on particular aspects of speech, such as emotional context, inappropriate behaviors, or indications of synthetic voices, and then consolidating these signals to extract deeper insights regarding the conversational dynamics. As a result, it enables a more profound understanding of interactions in real time, significantly improving the potential for effective communication analysis and fostering better engagement. Its unique design positions Velma as a leader in the realm of voice intelligence, pushing the boundaries of how we perceive and interact with spoken language.

The NVIDIA Nemotron 3 Nano Omni is an innovative open foundation model that seamlessly combines multiple modes of perception and reasoning—such as text, images, audio, video, and documents—into one cohesive architecture. By removing the need for separate models dedicated to each modality, it significantly reduces inference delays, streamlines orchestration, and cuts costs while maintaining a unified cross-modal context. Designed specifically for agentic AI systems, this model acts as a perception and context sub-agent, enabling larger AI frameworks to recognize and interpret their environments in real-time through various formats, including screens, recordings, and both structured and unstructured data. Its advanced capabilities cater to complex multimodal reasoning tasks, which include document analysis, speech recognition, comprehensive audio-video assessments, and sophisticated computer workflows, thereby equipping agents to navigate intricate interfaces and varied environments effortlessly. With a hybrid architecture that is meticulously optimized for long context handling and high throughput, the Nemotron 3 Nano Omni excels at processing large inputs, including multi-page documents, rendering it an invaluable asset in AI development. Moreover, this model not only consolidates different modalities but also boosts the overall efficiency of intelligent systems, enabling them to effectively process and comprehend a wide array of data types, ultimately enhancing their operational capabilities. As the landscape of AI continues to evolve, such advancements are vital for fostering more intelligent interactions with technology.

The OpenAI Moderation API provides a dedicated endpoint for developers to automatically evaluate text and images for potentially dangerous or policy-breaching content, thereby fostering safer AI practices through immediate classification and filtering. This system analyzes both incoming and, optionally, outgoing content, offering structured feedback that indicates whether the material has been flagged and includes detailed category labels like hate speech, harassment, self-harm, sexual content, or violence. Designed for easy integration into application processes, this API empowers developers to swiftly implement actions such as blocking, filtering, or escalating content before it reaches users. Moderation models, including “omni-moderation-latest,” have been refined for speed and accuracy, allowing for scalable deployment in high-traffic environments while maintaining consistent safety standards. By leveraging this powerful moderation resource, developers not only improve the user experience but also build greater trust in their platforms, ultimately leading to a safer online environment for all users. Furthermore, the proactive measures enabled by this API can help create a more positive digital landscape.

OpenAI’s GPT-Realtime-Translate is an innovative translation model designed to enhance multilingual voice communication, allowing users to engage in conversations in their preferred languages while receiving instant translations and transcriptions. Capable of processing more than 70 input languages and translating into 13 output languages, it serves a wide range of uses, such as customer service, international commerce, educational environments, events, media, and platforms that serve varied global demographics. Its architecture is engineered to preserve the essence of the original message, while also adapting to the speaker's rhythm, accommodating natural speech patterns, shifts in context, regional dialects, and technical jargon. By offering quick-response times and improved fluency, GPT-Realtime-Translate provides a seamless API for real-time speech translation, promoting more natural cross-lingual conversations. This advanced technology not only delivers immediate translations during exchanges but also guarantees that spoken content is accessible to a broad audience, significantly improving communication efficiency. Furthermore, it empowers individuals from different linguistic backgrounds to connect and collaborate more effectively, ultimately fostering a sense of inclusivity in diverse settings. The overarching goal of this model is to eliminate language barriers, creating smoother and more engaging interactions for all participants.

OpenAI's GPT-Realtime-Whisper represents a groundbreaking advancement in streaming transcription technology, aimed at providing rapid speech-to-text functionalities for live scenarios. This model captures spoken words in real-time, enhancing the experience of voice-enabled applications by making them feel swifter, more interactive, and fluid, whether through immediate captioning or by creating notes that correspond with current conversations. By facilitating live speech integration into business workflows, it empowers teams to produce captions suitable for various contexts such as meetings, educational settings, broadcasts, and events, while also generating summaries and notes during discussions. Furthermore, it contributes to the development of voice agents that need to continuously understand user inputs, thereby streamlining follow-up processes in interactions characterized by extensive verbal exchanges. As an integral component of a state-of-the-art suite of real-time voice models within the API, it not only transcribes but also engages in reasoning and translation during conversations, elevating real-time audio interactions from simple exchanges to advanced voice interfaces that can listen, interpret, transcribe, and dynamically respond as dialogues unfold. This significant technological progress is poised to revolutionize our engagement with voice-driven systems, enhancing their intuitiveness and effectiveness in managing live communication, ultimately leading to more productive and seamless interactions. The potential applications of this technology are vast, promising improvements across various industries and enhancing user experiences across different platforms.

Inworld AI's Realtime TTS-2 is an advanced voice generation model crafted for real-time conversation, striving to deliver a dialogue experience that closely resembles human interaction. This groundbreaking system captures every facet of a conversation, assessing the user's tone, rhythm, and emotional subtleties, while enabling developers to direct voice output through straightforward English commands, akin to directing an AI. Unlike conventional speech synthesis that functions independently, this model contextualizes previous conversations, ensuring that tone and pacing adapt dynamically, meaning that a response can evoke varied reactions based on prior context, such as humor or melancholy. Moreover, the Voice Direction feature allows developers to influence speech delivery in a way similar to a director guiding an actor, utilizing natural language instead of fixed emotion settings or sliders. Developers can also include inline nonverbal indicators like [sigh], [breathe], and [laugh] directly in the text, which the model effortlessly converts into appropriate audio responses. Importantly, Realtime TTS-2 preserves a cohesive voice identity across more than 100 languages, facilitating seamless language shifts within a single interaction, which significantly boosts its utility in various multilingual environments. As a result, this capability not only enhances the authenticity of conversations but also plays a crucial role in narrowing the divide between human communicative nuances and machine responses. The advancements of Realtime TTS-2 make it a remarkable tool in the evolution of interactive voice technology.

NeuralWing stands out as an advanced model designed for real-time neural simulation and design refinement specifically focused on transonic aircraft aerodynamics. It utilizes an extensive 3D transonic wing dataset, consisting of 30,000 steady-state CFD simulations that explore a 3D wing's behavior in the transonic regime, factoring in variations across four unique geometry parameters and two distinct inflow conditions. By employing Emmi’s AB-UPT surrogate model, which has been thoroughly trained on this vast dataset, NeuralWing allows users to seamlessly modify wing geometries, perform optimizations, and improve aerodynamic efficiency in a matter of seconds. The model is crafted to enable transonic 3D wing simulations, accommodating changes in geometry and inflow while delivering real-time inference and design parameter optimization. Users can input a geometry mesh in STL format along with speed and angle of attack, and they receive comprehensive outputs that include pressure, friction, velocity fields, and integral forces such as lift and drag. Geometry meshes are generated dynamically based on four design parameters, utilizing a differentiable approach that facilitates rapid evaluation of design changes. Moreover, NeuralWing achieves an exceptional accuracy rate of 99.5%, rendering it an essential asset for aerodynamics research and development. This remarkable level of precision instills confidence in engineers as they refine their designs, ensuring that each iteration is backed by reliable data. As a result, NeuralWing not only enhances the design process but also accelerates innovation in the field of aerodynamics.

NeuralMould, created by Emmi AI, represents a cutting-edge Large Engineering Model tailored for injection molding, establishing a new standard in AI-enhanced engineering solutions by integrating any geometry, material, and injection gate configuration all within a unified framework. This system allows users to select from an array of geometries while experimenting with various injection parameters, materials, and gate placements, thereby facilitating swift simulations of filling dynamics, quick scenario evaluations, optimization of crucial performance metrics, and avoidance of frozen flow fronts. The intricate nature of injection molding simulations is attributed to the requirement for multi-physics computations that precisely replicate the transient flow of viscous plastics through complexly designed thin-walled forms under conditions of high pressure and temperature. NeuralMould adeptly captures these vital phenomena across a range of injection scenarios and mold configurations, delivering outcomes that compete with conventional solvers, yet do so in markedly shorter computation times. Furthermore, this model accommodates multi-material applications, enabling rapid prototyping, supporting multi-gate arrangements, and managing diverse processing parameters, all thanks to its scalable transformer-based architecture. This revolutionary methodology places NeuralMould as an essential resource for engineers aiming to improve both efficiency and accuracy within the injection molding domain, ultimately paving the way for more innovative manufacturing solutions. With its advanced features, NeuralMould is set to transform the landscape of injection molding technology.

ESMC marks the latest innovation in the ESM series of protein language models, advancing the understanding of representation learning in protein biology. By training on an enormous dataset of billions of evolutionary sequences, it effectively captures representations that provide insights into the mechanistic aspects of protein structure and function. Utilizing a transformer architecture, the model prioritizes sequences as its main input and is trained on a dataset that includes up to 6 billion proteins. ESMC is designed for a range of applications within protein science, including structure prediction, functional annotation, protein design, and the investigation of evolutionary relationships among proteins. Furthermore, it has the ability to generate new proteins from partial sequences, structures, or specific functional requirements, which allows researchers to explore novel possibilities in protein design and biological research. The model is readily accessible through the Biohub Platform, enabling users to interact with it via an API and the ESM Python package, which offers quickstart resources for installation, API key generation, and connection to the platform, thus ensuring a user-friendly experience. This ease of access not only promotes wider participation in protein research but also fosters collaborative efforts across the scientific community, ultimately driving further advancements in the field. With its capabilities, ESMC opens new doors for innovation and discovery in protein science.

Building upon its predecessor, ESMFold, ESMFold2 sets a new standard in the realm of single-sequence structure prediction while also enabling the design of novel functional proteins by delving into the latent space of the ESMC model. This sophisticated model can accurately predict high-resolution, all-atom 3D structures of biomolecular complexes directly from amino acid sequences and incorporates multiple sequence alignments to enhance accuracy for challenging targets. Designed to predict structures using both sequence and structural modalities, it utilizes ESM representations that power a sequence of looped folding layers, while a diffusion model converts pairwise representations into atomic-resolution results. ESMFold2 stands out in its ability to forecast protein structures from amino acid sequences, providing comprehensive structural information, including exact all-atom coordinates for backbone and side chains, as well as confidence metrics and optional distogram predictions for thorough structural analysis. In addition, its groundbreaking methodology deepens the understanding of protein folding dynamics and their functional implications, positioning it as an indispensable tool for researchers engaging in this area of study. Ultimately, ESMFold2 not only advances structural biology but also opens new avenues for the development of protein-based applications.

Ideogram 4.0 is a state-of-the-art open image model crafted to enhance design capabilities, offering features such as open weights, multilingual support, intricate layout management, customizable components, and exceptional 2K imagery. This groundbreaking model serves developers and businesses looking to create, fine-tune, and implement visual intelligence within their systems. The approach taken in Ideogram 4.0 utilizes a describe-to-structure-to-recreate methodology, which interprets scenes, backgrounds, text, and objects as structured data before reconstructing images informed by that interpretation. Such a technique significantly improves the model's understanding of composition, empowering teams with increased control over layout, object positioning, typography, and overall visual presentation. Designed for practical design needs, it shines in various fields, including branding, advertising, fashion, marketing, culinary arts, apparel, social media, photography, and illustration. Since its launch, Ideogram has been at the forefront of text rendering, and the latest version introduces bounding-box layout control to maintain the legibility of headlines, thus enhancing its functionality in professional environments. As a result, creators can utilize this model to optimize their creative workflows and achieve outstanding outcomes, making it an indispensable tool in the modern design landscape. Ultimately, Ideogram 4.0 not only improves visual projects but also encourages innovation across diverse industries.

Reve 2.0 is a cutting-edge AI creative studio designed to facilitate the generation, alteration, and remixing of images using natural language commands alongside a user-friendly drag-and-drop interface. Its main objective is to empower individuals to redefine their creative concepts, allowing them to create stunning visuals, improve existing images, and maintain a fluid workflow from initial idea to final product. Users can start with a basic text prompt or upload a picture, enabling them to make precise edits through simple language while integrating AI features with manual visual tweaks directly in the editor. This latest iteration highlights the platform's most sophisticated image generation and editing model, boasting native 4K resolution, outstanding visual quality, and improved creative control for achieving exceptional outcomes. It provides a wide array of features, including image creation, editing, and remixing, along with an interactive workflow that allows users to adjust particular scene elements, alter visual styles, explore various iterations, and expand on previous projects without the need for traditional design tools. This methodology not only simplifies the creative journey but also encourages users to push boundaries and explore innovative ideas like never before, fostering a new era of creativity.

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

Laguna M.1 is recognized as Poolside's premier model for agentic coding, meticulously designed in-house to optimize software development processes. This sophisticated model incorporates 225 billion parameters and employs a Mixture of Experts architecture with 23 billion parameters activated, all trained on a colossal dataset of 30 trillion tokens using a network of 6,144 NVIDIA H200 GPUs. Poolside committed to developing Laguna M.1 from the ground up, utilizing proprietary data, a specialized training codebase, and an asynchronous on-policy reinforcement learning strategy within its agent framework, all specifically oriented towards agentic coding applications. The model's architecture is crafted to deliver top-tier performance within Poolside's coding agent, empowering it to adeptly reason through programming tasks, engage with an array of tools, modify code, run tests, and support extensive autonomous development sessions. Tailored for developers and teams facing complex coding obstacles, Laguna M.1 boasts enhanced capabilities in reasoning, understanding architecture, managing terminal actions, and executing multi-step processes, far exceeding the abilities of lighter models. Overall, its comprehensive feature set establishes it as an indispensable tool for professionals immersed in high-stakes software projects, making it a vital component in the landscape of agentic coding solutions.