Alternatives to Nemotron 3.5 Lightning

Claude Mythos 5.1 represents Anthropic's latest advancement in the Mythos-class of models, tailored for sophisticated applications in cybersecurity, biology, scientific investigation, programming, and extensive knowledge-oriented tasks. While it shares the same foundational architecture as Claude Fable 5.1, it is differentiated by unique safety measures: Fable 5.1 is widely accessible, whereas Mythos 5.1 is limited to select trusted access programs designed with specific safeguards for cybersecurity and life sciences research. This model establishes a new benchmark in performance for autonomous coding and showcases unparalleled cyber capabilities among all Anthropic models released thus far. In the realm of scientific research, Mythos 5.1 can effectively handle specialized tools and intricate workflows related to molecular design, computational biology, and various technical fields. During testing by Anthropic, it successfully engineered high-affinity protein binders for multiple targets, achieving its highest recorded hit rate to date. Additionally, it demonstrated proficiency in optimizing seven different open-source deep learning models focused on protein and genomics. By pushing the boundaries of what is possible, Mythos 5.1 is positioned to make significant contributions to future research and development endeavors.

Claude Fable 5.1 is a frontier AI model from Anthropic built for demanding coding, research, knowledge work, and autonomous multi-step workflows. It delivers higher performance than Claude Fable 5 across benchmarks covering scientific research, terminal-based coding, business automation, computer use, multidisciplinary reasoning, and agentic software development. The model is particularly suited to long-running tasks where it must investigate problems, use tools, maintain context, verify intermediate work, and continue operating with limited supervision. Early evaluations highlighted improvements in areas such as root-cause analysis, code review, browser automation, financial research, document drafting, slide creation, and complex engineering workflows. Fable 5.1 also introduces lower cache-read pricing, which Anthropic says can reduce costs by roughly 25% for typical workloads and considerably more for context-heavy agentic tasks. Enterprise customers can use new privacy-oriented safeguard options that are designed to support zero-data-retention-style deployments while still maintaining misuse protections. Cybersecurity safeguards have also been refined to intervene less often on legitimate defensive work while continuing to restrict higher-risk activities such as exploit generation. Anthropic offers the model through Claude Code, Claude Cowork, Claude.ai, the Claude API, and supported cloud platforms. Claude Fable 5.1 is intended for developers, researchers, enterprises, and professional teams that need strong reasoning and coding performance without relying on Anthropic’s most restricted-access model tier.

GLM-5.3 is Z.ai’s advanced coding and agentic reasoning model built through scaled post-training on top of the GLM-5.2 base model. The release focuses on frontier coding, long-horizon software engineering, agent tasks, cyber evaluation, and reinforcement learning at scale. GLM-5.3 improves significantly over GLM-5.2 on complex coding benchmarks, real-world engineering environments, Terminal Bench 3.0, DeepSWE, Agents’ Last Exam, and Z.ai’s internal Code Bench. The model is trained on environments that resemble real professional work, including tasks involving codebases, infrastructure, documentation, compute clusters, experiments, bottleneck diagnosis, implementation, testing, and measurable optimization. Z.ai’s post-training stack includes IndexShare for efficient long-context processing, SAO for reinforcement learning on long-horizon tasks, and slime for large-scale asynchronous training. GLM-5.3 supports three thinking effort levels, including low, high, and max, with max recommended for coding tasks. The model also demonstrates emergent cyber capabilities across vulnerability discovery and exploitation benchmarks, prompting continued safety evaluation and hardening before weights are released. GLM-5.3 can be used through the GLM Coding Plan, ZCode, Claude Code, OpenCode, and other coding agent workflows. By combining stronger coding performance, long-horizon task execution, post-training scale, cyber evaluation, reasoning effort controls, and coding-agent integrations, GLM-5.3 supports advanced developer and research workflows.

Claude Fable 5 is Anthropic’s most capable generally available AI model, built to tackle demanding tasks across software development, research, business analysis, scientific exploration, and enterprise productivity. The model demonstrates state-of-the-art performance in coding, reasoning, visual understanding, long-context processing, and autonomous task execution. Claude Fable 5 can analyze large codebases, interpret complex documents and datasets, generate detailed reports, and assist with advanced decision-making processes. Its enhanced memory capabilities allow it to remain effective during long-running workflows and multi-step projects. The model also delivers strong performance in image analysis, chart interpretation, scientific reasoning, and technical problem-solving. Anthropic has incorporated advanced safety classifiers that detect certain high-risk topics and automatically redirect those interactions to a more restricted model experience. These safeguards are designed to reduce misuse while still providing productive assistance for legitimate users. Claude Fable 5 is available through the Claude platform and API, enabling developers and organizations to integrate advanced AI capabilities into their applications and workflows. The platform is designed to help businesses improve productivity, accelerate innovation, and streamline complex knowledge work.

Muse Spark 1.2 is Meta’s newest coding-focused model, released alongside Muse Code as part of Meta’s AI developer platform. The model improves on Muse Spark 1.1 with stronger code generation, complex debugging, codebase understanding, and full developer workflow performance. Muse Spark 1.2 powers Muse Code, a terminal coding agent that can plan changes, write code, validate results, and coordinate persistent background subagents. The model was co-trained with Muse Code so it performs well inside the agentic coding runtime and tool environment. Its training included scaled coding compute, broader training environment diversity, rejection-sampled harness trajectories, recipe optimizations, and Muse Code toolset integration. Muse Spark 1.2 is designed for long-horizon coding tasks such as whole-repository generation, large end-to-end projects, auto-research, and extended optimization work. It uses planning to sequence work, goal conditioning to stay aligned with the user’s objective, and context compaction to preserve useful knowledge over long sessions. The model also benefits from a self-improvement loop where Muse Spark 1.1 generated challenging coding environments and instruction-following templates for training. By combining coding specialization, agentic workflow support, long-horizon training, subagent compatibility, and Meta Model API availability, Muse Spark 1.2 helps developers build, debug, and optimize software more effectively.

GLM-5.3-Flash is a multimodal foundation model from Z.ai built for high-efficiency reasoning, coding, agents, and visual understanding. The model contains 320 billion parameters in total but activates only 18 billion parameters during inference, helping reduce compute requirements. Its architecture combines linear attention with sparse attention so it can efficiently handle both local dependencies and relevant information spread across very long contexts. Z.ai also introduced IndexPool to reduce the memory and latency overhead associated with long-context retrieval at context lengths reaching one million tokens. The model was pretrained on a 30-trillion-token multimodal dataset that incorporates both textual and visual information. GLM-5.3-Flash is designed for software engineering tasks, autonomous workflows, frontend development, computer use, document analysis, and other professional workloads that benefit from visual reasoning. Its visual coding capabilities allow it to inspect rendered interfaces, identify layout or interaction problems, and use those observations to revise its work. Benchmark results published by Z.ai show that it improves substantially over GLM-5.2 on multiple coding and agentic tests while remaining competitive with more expensive frontier models. GLM-5.3-Flash can be accessed through Z.ai services and is also available as downloadable model weights for deployment through supported open inference frameworks.

Laguna S 2.1 is an advanced open weight coding model that emphasizes long-term project completion and efficient reasoning capabilities. Featuring a 118-billion-parameter Mixture-of-Experts architecture, it activates 8 billion parameters for each token and accommodates a context window of up to one million tokens in both thinking and non-thinking modes. The model’s streamlined active size allows it to perform intricate tasks on local machines while still competing favorably against significantly larger models across various benchmarks, including terminal usage, software engineering, codebase question answering, and tool utilization. Designed for resilience, Laguna S 2.1 excels in tackling challenging assignments with enhanced persistence, meticulous verification, and a readiness to backtrack rather than prematurely claim success. In practical applications, it has successfully created and validated a browser rendering engine from scratch, optimized an agent harness for improved execution speed and reduced memory usage, and conducted extensive mathematical research using the available tools within its environment, demonstrating its versatility and effectiveness. This combination of features positions Laguna S 2.1 as a powerful tool for developers seeking innovative solutions.

Nemotron 3 Nano is a small yet powerful large language model from NVIDIA's Nemotron 3 series, specifically crafted for effective agentic reasoning, interactive dialogue, and programming assignments. Its innovative Mixture-of-Experts Mamba-Transformer framework selectively activates a limited set of parameters for each token, ensuring rapid inference times without sacrificing accuracy or reasoning capabilities. With roughly 31.6 billion parameters in total, including about 3.2 billion active ones (or 3.6 billion when factoring in embeddings), it surpasses the performance of the previous Nemotron 2 Nano model while requiring less computational effort for each forward pass. The model is equipped to manage long-context processing of up to one million tokens, which allows it to efficiently process extensive documents, complex workflows, and detailed reasoning sequences in a single cycle. Moreover, it is engineered for high-throughput, real-time performance, making it particularly adept at handling multi-turn dialogues, invoking tools, and executing agent-based workflows that involve intricate planning and reasoning tasks. This versatility positions Nemotron 3 Nano as a leading choice for applications requiring advanced cognitive capabilities.

DeepSeek-V4-Pro is an advanced Mixture-of-Experts language model built for high-performance reasoning, coding, and large-scale AI applications. With 1.6 trillion total parameters and 49 billion activated parameters, it delivers strong capabilities while maintaining computational efficiency. The model supports a massive context window of up to one million tokens, making it ideal for handling long documents and complex workflows. Its hybrid attention architecture improves efficiency by reducing computational overhead while maintaining accuracy. Trained on more than 32 trillion tokens, DeepSeek-V4-Pro demonstrates strong performance across knowledge, reasoning, and coding benchmarks. It includes advanced training techniques such as improved optimization and enhanced signal propagation for better stability. The model offers multiple reasoning modes, allowing users to choose between faster responses or deeper analytical thinking. It is designed to support agentic workflows and complex multi-step problem solving. As an open-source model, it provides flexibility for developers and organizations to customize and deploy at scale. Overall, DeepSeek-V4-Pro delivers a balance of performance, efficiency, and scalability for demanding AI applications.

Inkling is Thinking Machines’ open-weights foundation model built for customization, multimodal reasoning, and agentic AI workflows. The model uses a Mixture-of-Experts architecture with 975 billion total parameters and 41 billion active parameters, making it large in capacity while activating only a subset of experts per token. Inkling supports up to a 1 million token context window and was pretrained on 45 trillion tokens spanning text, images, audio, and video. It is designed as a broad generalist model with strengths across coding, reasoning, instruction following, factuality, tool use, vision, audio understanding, forecasting, and safety. Developers can tune its thinking effort to trade off latency, cost, and performance, which is useful for production systems that need efficient reasoning at scale. Inkling can be fine-tuned on Tinker, tested in the Inkling Playground, and deployed through partners such as TogetherAI, Fireworks, Modal, Databricks, Baseten, vLLM, SGLang, llama.cpp, and Hugging Face transformers. The model can generate applications, operate tools, create styled artifacts, reason over visual and audio inputs, and support long refinement loops for collaborative work. Thinking Machines also previewed Inkling-Small, a lighter Mixture-of-Experts model with 276 billion total parameters and 12 billion active parameters for lower-cost and lower-latency workloads. By combining open weights, multimodal training, agentic capabilities, efficient reasoning, and fine-tuning support, Inkling gives builders a flexible AI foundation for specialized products and workflows.

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.

Muse Spark 1.1 is Meta’s upgraded multimodal reasoning model designed to support advanced agentic workflows, coding tasks, computer use, and complex tool orchestration. Developed by Meta Superintelligence Labs, it builds on Muse Spark with major gains in planning, tool use, long-context reasoning, multimodal perception, and real-world task execution. The model can work across external apps and services, native tools, MCP servers, custom skills, browsers, scripts, images, video, PDFs, and audio inputs. Muse Spark 1.1 can act as a main agent by gathering context, creating a plan, and delegating work to parallel subagents, or operate as a subagent that follows instructions and escalates when needed. Its 1 million token context window allows it to retain earlier actions, retrieve information from long workflows, and compact context while preserving critical details. The model is also trained for computer-use tasks, deciding when to automate with scripts and when to interact directly with an interface. In coding workflows, Muse Spark 1.1 can diagnose bugs, implement features, migrate large codebases, generate web applications, take screenshots, identify UI issues, and validate fixes. Its multimodal strengths include visual-to-code generation, detailed image and video captioning, grounded perception, and workflows where seeing, reasoning, and acting happen together. Available through the Meta Model API public preview and in Thinking mode inside Meta AI, Muse Spark 1.1 gives developers and users a more capable foundation for building agents, automations, coding assistants, and multimodal productivity tools.

Muse Glimmer is an open-weights model featuring 30 billion parameters, developed by Meta Superintelligence Labs, and is fine-tuned for continuous local agent operations. Its compact design allows it to function on a standard Mac or PC equipped with a single consumer GPU, making it ideal for various tasks such as local agent management, function calling, programming, and LLM-as-a-judge evaluations without reliance on cloud services or internet connectivity. This innovative model integrates advanced capabilities such as long-horizon execution, accurate tool invocation, multimodal comprehension, extended memory for context, and effective instruction following. It is proficient in accomplishing end-to-end tasks as an agent, maintains the ability to engage in multi-step reasoning over lengthy processes, can recover gracefully from failed or unanticipated tool engagements, and interprets interleaved text and images using a specialized perception encoder designed for analyzing screenshots, graphs, and document files. Furthermore, Muse Glimmer is compatible with OpenClaw and other orchestration frameworks, allowing for adjustable reasoning efforts, and has been developed with a diverse dataset encompassing over 100 languages. The model's versatility ensures that it can adapt to various applications, thus enhancing its utility in different domains.

Hy4 preview represents a cutting-edge open source Mixture-of-Experts flagship model tailored for a variety of real-world productivity tasks, including software engineering, office activities, game development, and scientific exploration. This model boasts a staggering total of 770 billion parameters, with 49 billion activated per token, and features an impressive 1 million-token context window, allowing it to efficiently manage large codebases, vast document collections, and complex multi-step processes. The architecture consists of 78 layers that integrate Gated DeepSeek Sparse Attention alongside IndexCache for reusing sparse indices across layers, while also employing identity Hyper-Connections to enhance the flow of information between layers. Additionally, a dedicated Multi-Token Prediction layer facilitates speculative decoding, further enhancing its capabilities. Hy4 preview is crafted to comprehend, plan, debug, and validate intricate engineering projects, while also achieving notable improvements in the quality of front-end visuals and interaction design, thereby making it an invaluable asset for professionals across various domains.

The Nemotron 3 Nano stands out as the tiniest model within NVIDIA's Nemotron 3 lineup, specifically designed for agentic AI tasks that require robust reasoning and conversational skills while maintaining cost-effective inference. This hybrid Mamba-Transformer Mixture-of-Experts model boasts 3.2 billion active parameters, 3.6 billion when including embeddings, and a total of 31.6 billion parameters. NVIDIA asserts that this model offers greater accuracy compared to its predecessor, the Nemotron 2 Nano, all while utilizing less than half of the parameters during each forward pass, thus enhancing efficiency without compromising on performance. It is also claimed to surpass the accuracy of both GPT-OSS-20B and Qwen3-30B-A3B-Thinking-2507 across various widely-used benchmarks. With an 8K input and 16K output setting utilizing a single H200, the model achieves an inference throughput that is 3.3 times greater than that of Qwen3-30B-A3B and 2.2 times that of GPT-OSS-20B. Additionally, the Nemotron 3 Nano is capable of handling context lengths of up to 1 million tokens, further establishing its superiority over GPT-OSS-20B and Qwen3-30B-A3B-Instruct-2507. This remarkable combination of features positions it as a leading choice for advanced AI applications that demand both precision and efficiency.

Muse Spark 1.3 represents an advanced AI model that has enhanced capabilities for both agentic and coding tasks, making it more intelligent and practical for use in everyday applications. It excels in maintaining focus on extended tasks through active collaboration with users while efficiently managing various workflows within a single, continuous thread. When faced with an open-ended goal, the model adeptly utilizes tools to create context from disorganized or contradictory information, rectify any gaps in its strategy, track its learning progress, and ultimately generate a final product. In situations where prompts lack clarity, it is proactive in seeking clarification, asking for assistance when it encounters obstacles, and confirming its actions before proceeding with significant decisions. The model demonstrates a high level of reliability in following intricate, long-form instructions, ensuring that detailed requirements are maintained throughout complex, multi-step tasks without losing critical constraints or deviating from the desired workflow. With its enhanced multitasking capabilities, it effectively aligns incoming prompts with the appropriate tasks, even in cases where users interject or shift the focus of previous requests, allowing for a seamless user experience. This makes Muse Spark 1.3 a versatile tool for a wide range of applications.

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.

The Nemotron-3 Super is an innovative member of NVIDIA's Nemotron 3 series of open models, specifically crafted to facilitate sophisticated agentic AI systems that can effectively reason, plan, and carry out multi-step workflows in intricate environments. This model features a unique hybrid Mamba-Transformer Mixture-of-Experts architecture that merges the streamlined efficiency of Mamba layers with the contextual depth provided by transformer attention mechanisms, which allows it to adeptly manage extended sequences and intricate reasoning tasks with impressive accuracy and throughput. By activating only a portion of its parameters for each token, this architecture significantly enhances computational efficiency while preserving robust reasoning capabilities, making it ideal for scalable inference under heavy workloads. The Nemotron-3 Super comprises approximately 120 billion parameters, with around 12 billion being active during inference, which substantially boosts its ability to handle multi-step reasoning and collaborative interactions among agents within extensive contexts. Such advancements make it a powerful tool for tackling diverse challenges in AI applications.

gpt-oss-120b is a text-only reasoning model with 120 billion parameters, released under the Apache 2.0 license and managed by OpenAI’s usage policy, developed with insights from the open-source community and compatible with the Responses API. It is particularly proficient in following instructions, utilizing tools like web search and Python code execution, and allowing for adjustable reasoning effort, thereby producing comprehensive chain-of-thought and structured outputs that can be integrated into various workflows. While it has been designed to adhere to OpenAI's safety policies, its open-weight characteristics present a risk that skilled individuals might fine-tune it to circumvent these safeguards, necessitating that developers and enterprises apply additional measures to ensure safety comparable to that of hosted models. Evaluations indicate that gpt-oss-120b does not achieve high capability thresholds in areas such as biological, chemical, or cyber domains, even following adversarial fine-tuning. Furthermore, its release is not seen as a significant leap forward in biological capabilities, marking a cautious approach to its deployment. As such, users are encouraged to remain vigilant about the potential implications of its open-weight nature.

Qwen3.5-35B-A3B is a member of the Qwen3.5 "Medium" model series, meticulously crafted as an effective multimodal foundation model that strikes a balance between robust reasoning capabilities and practical application needs. Utilizing a Mixture-of-Experts (MoE) architecture, it boasts a total of 35 billion parameters, yet activates only around 3 billion for each token, enabling it to achieve performance levels similar to much larger models while significantly cutting down on computational expenses. The model employs a hybrid attention mechanism that merges linear attention with traditional attention layers, which enhances its ability to handle extensive context and boosts scalability for intricate tasks. As an inherently vision-language model, it processes both textual and visual data, catering to a variety of applications, including multimodal reasoning, programming, and automated workflows. Furthermore, it is engineered to operate as a versatile "AI agent," proficient in planning, utilizing tools, and systematically solving problems, extending its functionality beyond mere conversational interactions. This capability positions it as a valuable asset across diverse domains, where advanced AI-driven solutions are increasingly required.

NVIDIA's Nemotron 3 represents a collection of open large language models crafted to drive advanced reasoning, conversational AI, and autonomous AI agents. This series consists of three distinct models tailored for varying scales of AI workloads, all while ensuring remarkable efficiency and precision. Emphasizing "agentic AI" features, these models are capable of executing multi-step reasoning, collaborating with tools, and functioning as integral parts of multi-agent systems utilized across automation, research, and enterprise sectors. The underlying architecture employs a hybrid mixture-of-experts (MoE) approach paired with transformer techniques, enabling the activation of only specific parameter subsets for each task, thereby enhancing performance and minimizing computational expenses. Designed to excel in reasoning, dialogue, and strategic planning, the Nemotron 3 models are optimized for high throughput, making them suitable for extensive deployment across diverse applications. Additionally, their innovative architecture allows for greater adaptability and scalability, ensuring they meet the evolving demands of modern AI challenges.

gpt-oss-20b is a powerful text-only reasoning model consisting of 20 billion parameters, made available under the Apache 2.0 license and influenced by OpenAI’s gpt-oss usage guidelines, designed to facilitate effortless integration into personalized AI workflows through the Responses API without depending on proprietary systems. It has been specifically trained to excel in instruction following and offers features like adjustable reasoning effort, comprehensive chain-of-thought outputs, and the ability to utilize native tools such as web search and Python execution, resulting in structured and clear responses. Developers are responsible for establishing their own deployment precautions, including input filtering, output monitoring, and adherence to usage policies, to ensure that they align with the protective measures typically found in hosted solutions and to reduce the chance of malicious or unintended actions. Additionally, its open-weight architecture makes it particularly suitable for on-premises or edge deployments, emphasizing the importance of control, customization, and transparency to meet specific user needs. This flexibility allows organizations to tailor the model according to their unique requirements while maintaining a high level of operational integrity.

The Laguna XS 2.1 is an enhanced coding model that operates as an open weight agentic system, ideal for long-duration tasks on local machines. Featuring a 33-billion-parameter Mixture-of-Experts framework with 3 billion parameters activated per token, this model maintains the efficient architecture of Laguna XS.2 while significantly advancing performance in multilingual software engineering and terminal-style tasks. It is specifically engineered to assist coding agents in reviewing repositories, reasoning through intricate changes, utilizing various tools, executing commands, and maintaining continuity throughout extended projects. With a generous 256K context window, the model enables agents to effectively manage extensive codebases, lengthy histories, and complex multi-step workflows. Laguna XS 2.1 benefits from support from platforms like vLLM, SGLang, NVIDIA TensorRT-LLM, Hugging Face Transformers, and Ollama, with plans for native integration with llama.cpp in the future. The model is offered in various checkpoint formats, including BF16, FP8, INT4, and NVFP4, granting developers the flexibility to select between high fidelity and configurations optimized for limited VRAM or computational resources. This adaptability makes it an excellent choice for a wide range of development environments and requirements.

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.

Qwen3.8-Flash-Next represents an open-weight multimodal Mixture-of-Experts architecture and serves as an initial glimpse into the design intended for Qwen4. This model strategically enhances attention mechanisms, residual pathways, embeddings, and optimization techniques to boost its capabilities, improve computational efficiency, expand model capacity, and ensure training stability. Its innovative hybrid architecture merges Gated DeltaNet, which adeptly compresses past information, with Qwen Sparse Attention, enabling the selection of significant context at a micro-block level to lessen both attention and indexing costs associated with lengthy sequences. The Gated Residual feature broadens the residual pathway into four streams, dynamically managing the flow of information across different layers. Additionally, the N-gram Embedding integrates large-scale local-pattern memory with minimal added computation per token, and it can be transferred to host memory for further efficiency. The model is structured around a 125B-parameter main network supplemented by 51B parameters dedicated to N-gram embeddings, activating only 6B parameters for each token processed. This sophisticated framework highlights the ongoing advancements in machine learning architectures, setting a promising stage for future developments.

Inkling-Small is an efficient Mixture-of-Experts transformer model built to provide performance comparable to Inkling while using a much smaller active parameter footprint. The model has 276 billion total parameters and 12 billion active parameters, making it designed for strong capability with more efficient compute usage. Inkling-Small was trained on NVIDIA GB300 NVL72 systems and supports native reasoning across text, images, and audio. It offers context windows of up to one million tokens, making it suitable for long documents, large codebases, multimodal context, and extended agent workflows. Users can set reasoning effort from minimal to extra high to control the balance between speed, cost, compute, and task complexity. The model benefits from improved pre-training data, post-training with on-policy distillation from Inkling, and extended agentic coding reinforcement learning. These improvements helped Inkling-Small surpass its larger counterpart on reasoning and coding benchmarks. Its encoder-free multimodal architecture processes audio as dMel spectrograms and images as 40-by-40-pixel patches alongside text tokens. By combining efficient MoE scaling, long-context reasoning, multimodal input, coding strength, and adjustable thinking effort, Inkling-Small is built for practical high-performance AI deployment.

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.

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.

Qwen3.8-2.4T-A95B stands out as the most extensive open model within the Qwen3.8 series, offering advanced Qwen-Max-class features in a publicly accessible format. Constructed upon the solid framework of Qwen3.5, this model significantly enhances performance in areas such as coding, professional tasks, research, and complex, prolonged agentic activities, emphasizing the reliability of executing intricate, multi-step workflows to completion. Utilizing a cutting-edge mixture-of-experts architecture, it boasts an impressive total of 2.4 trillion parameters, with 95 billion of those being activated, featuring 512 experts and engaging 10 routed along with one shared expert simultaneously. The model accommodates a native context length of 262,144 tokens, which can be extended to around 1.01 million tokens, thereby providing substantial flexibility for various applications. Furthermore, improvements in agent execution, such as enhanced autonomous planning and better responsiveness to environmental feedback, contribute to its efficiency, while its broader compatibility with widely used agent frameworks and development tools facilitates seamless integration into existing systems, making it a versatile choice for developers and researchers alike.

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.

Ling 3.0 Flash represents an advanced language model optimized for long-term agent workflows, characterized by swift response times, minimal activation levels, and consistent tool usage. Incorporating a Mixture-of-Experts structure, it boasts a staggering 124 billion parameters in total, with 5.1 billion parameters activated per token, which enhances its capability while maintaining efficient inference. This model features an impressive native context window of 256K tokens, which can be expanded to accommodate up to 1 million tokens, ensuring effective retrieval of information from any part of lengthy contexts. When compared to its predecessor, the original Flash model, Ling 3.0 Flash significantly enhances stability for prolonged tasks, improves the accuracy of tool-calling, better adheres to instructions, and shows greater compatibility with agent harnesses and coding tasks. Additionally, its refined spatial awareness allows it to create grids of physical scenes and evaluate relative positions effectively, while its hybrid reasoning capabilities boost success rates across a range of task complexities. Overall, Ling 3.0 Flash exemplifies a significant leap forward in language modeling technology, ensuring users can achieve superior performance across diverse applications.

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.

Step 3.5 Flash is a cutting-edge open-source foundational language model designed for advanced reasoning and agent-like capabilities, optimized for efficiency; it utilizes a sparse Mixture of Experts (MoE) architecture that activates only approximately 11 billion of its nearly 196 billion parameters per token, ensuring high-density intelligence and quick responsiveness. The model features a 3-way Multi-Token Prediction (MTP-3) mechanism that allows it to generate hundreds of tokens per second, facilitating complex multi-step reasoning and task execution while efficiently managing long contexts through a hybrid sliding window attention method that minimizes computational demands across extensive datasets or codebases. Its performance on reasoning, coding, and agentic tasks is formidable, often matching or surpassing that of much larger proprietary models, and it incorporates a scalable reinforcement learning system that enables continuous self-enhancement. Moreover, this innovative approach positions Step 3.5 Flash as a significant player in the field of AI language models, showcasing its potential to revolutionize various applications.

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.

Kimi K2 Thinking is a sophisticated open-source reasoning model created by Moonshot AI, specifically tailored for intricate, multi-step workflows where it effectively combines chain-of-thought reasoning with tool utilization across numerous sequential tasks. Employing a cutting-edge mixture-of-experts architecture, the model encompasses a staggering total of 1 trillion parameters, although only around 32 billion parameters are utilized during each inference, which enhances efficiency while retaining significant capability. It boasts a context window that can accommodate up to 256,000 tokens, allowing it to process exceptionally long inputs and reasoning sequences without sacrificing coherence. Additionally, it features native INT4 quantization, which significantly cuts down inference latency and memory consumption without compromising performance. Designed with agentic workflows in mind, Kimi K2 Thinking is capable of autonomously invoking external tools, orchestrating sequential logic steps—often involving around 200-300 tool calls in a single chain—and ensuring consistent reasoning throughout the process. Its robust architecture makes it an ideal solution for complex reasoning tasks that require both depth and efficiency.

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.

DeepSeek-V4-Flash is an optimized Mixture-of-Experts language model built for efficient large-scale AI workloads and fast inference. With 284 billion total parameters and 13 billion activated parameters, it delivers strong performance while maintaining lower computational demands compared to larger models. The model supports a massive context length of up to one million tokens, making it suitable for handling long-form content and multi-step workflows. Its hybrid attention mechanism improves efficiency by minimizing resource consumption while preserving accuracy. Trained on a dataset exceeding 32 trillion tokens, DeepSeek-V4-Flash performs well across reasoning, coding, and knowledge benchmarks. It offers flexible reasoning modes, enabling users to switch between quick responses and more detailed analytical outputs. The architecture is designed to support agentic workflows and scalable deployment environments. As an open-source model, it provides flexibility for customization and integration. Overall, DeepSeek-V4-Flash is a cost-effective and high-performance solution for modern AI applications.

MiMo-V2-Flash is a large language model created by Xiaomi that utilizes a Mixture-of-Experts (MoE) framework, combining remarkable performance with efficient inference capabilities. With a total of 309 billion parameters, it activates just 15 billion parameters during each inference, allowing it to effectively balance reasoning quality and computational efficiency. This model is well-suited for handling lengthy contexts, making it ideal for tasks such as long-document comprehension, code generation, and multi-step workflows. Its hybrid attention mechanism integrates both sliding-window and global attention layers, which helps to minimize memory consumption while preserving the ability to understand long-range dependencies. Additionally, the Multi-Token Prediction (MTP) design enhances inference speed by enabling the simultaneous processing of batches of tokens. MiMo-V2-Flash boasts impressive generation rates of up to approximately 150 tokens per second and is specifically optimized for applications that demand continuous reasoning and multi-turn interactions. The innovative architecture of this model reflects a significant advancement in the field of language processing.

DeepSeek-Coder-V2 is an open-source model tailored for excellence in programming and mathematical reasoning tasks. Utilizing a Mixture-of-Experts (MoE) architecture, it boasts a staggering 236 billion total parameters, with 21 billion of those being activated per token, which allows for efficient processing and outstanding performance. Trained on a massive dataset comprising 6 trillion tokens, this model enhances its prowess in generating code and tackling mathematical challenges. With the ability to support over 300 programming languages, DeepSeek-Coder-V2 has consistently outperformed its competitors on various benchmarks. It is offered in several variants, including DeepSeek-Coder-V2-Instruct, which is optimized for instruction-based tasks, and DeepSeek-Coder-V2-Base, which is effective for general text generation. Additionally, the lightweight options, such as DeepSeek-Coder-V2-Lite-Base and DeepSeek-Coder-V2-Lite-Instruct, cater to environments that require less computational power. These variations ensure that developers can select the most suitable model for their specific needs, making DeepSeek-Coder-V2 a versatile tool in the programming landscape.

Ring is a sophisticated trillion-parameter thinking model created by Ant Group, specifically tailored for real-world Agent workflows. It employs a Mixture of Experts architecture similar to that of Ling, activating approximately 63 billion parameters during each inference, and is particularly geared towards tasks such as coding agents, utilizing tools, collaborating with multiple tools, engineering development, conducting research analysis, and executing long-term tasks. Instead of merely striving for "smarter" outcomes, Ring prioritizes the reliable completion of intricate tasks while maintaining a cost-effective approach, effectively balancing quality, speed, and efficiency in production settings. The latest iteration, Ring-2.6-1T, incorporates an adjustable Reasoning Effort mechanism that features high and xhigh reasoning intensity levels, which allocates an adaptive reasoning budget according to the complexity of the task at hand. The high mode is specifically optimized for high-frequency Agent workflows, resulting in lower token costs and quicker multi-step execution, while also facilitating multi-turn interactions, tool collaboration, and task decomposition. As a result, Ring demonstrates a significant advancement in enhancing the capabilities of agents in various operational contexts.

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.

Xiaomi MiMo-V2.5 is a next-generation open-source AI model that combines agentic intelligence with multimodal capabilities. It is designed to process and understand text, images, and audio within a single architecture. The model uses a sparse Mixture-of-Experts framework with a large parameter count to deliver efficient and scalable performance. It supports a context window of up to one million tokens, allowing it to handle long and complex workflows. MiMo-V2.5 integrates visual and audio encoders to improve perception and cross-modal reasoning. It is capable of performing tasks such as coding, reasoning, and multimodal analysis with strong accuracy. Benchmark results show competitive performance compared to leading AI models in both agentic and multimodal tasks. The model is optimized for token efficiency, balancing performance with lower computational cost. It is designed for real-world applications that require both reasoning and perception. Xiaomi has open-sourced the model, making it accessible for developers and researchers. By combining multimodality, scalability, and efficiency, MiMo-V2.5 pushes forward the development of advanced AI systems.

Qwen3-Coder is an advanced code model that comes in various sizes, prominently featuring the 480B-parameter Mixture-of-Experts version (with 35B active) that inherently accommodates 256K-token contexts, which can be extended to 1M, and demonstrates cutting-edge performance in Agentic Coding, Browser-Use, and Tool-Use activities, rivaling Claude Sonnet 4. With a pre-training phase utilizing 7.5 trillion tokens (70% of which are code) and synthetic data refined through Qwen2.5-Coder, it enhances both coding skills and general capabilities, while its post-training phase leverages extensive execution-driven reinforcement learning across 20,000 parallel environments to excel in multi-turn software engineering challenges like SWE-Bench Verified without the need for test-time scaling. Additionally, the open-source Qwen Code CLI, derived from Gemini Code, allows for the deployment of Qwen3-Coder in agentic workflows through tailored prompts and function calling protocols, facilitating smooth integration with platforms such as Node.js and OpenAI SDKs. This combination of robust features and flexible accessibility positions Qwen3-Coder as an essential tool for developers seeking to optimize their coding tasks and workflows.

Xiaomi MiMo-V2.5-Pro is a next-generation open-source AI model designed for advanced reasoning, coding, and long-horizon task execution. It uses a Mixture-of-Experts architecture with over one trillion parameters and a large active parameter set for efficient performance. The model supports an extended context window of up to one million tokens, allowing it to handle complex, multi-step workflows. It is built to perform autonomous tasks, including software development, system design, and engineering optimization. Benchmark results show strong performance across coding, reasoning, and agent-based evaluation tests. MiMo-V2.5-Pro incorporates hybrid attention mechanisms to improve efficiency while maintaining accuracy across long contexts. It is optimized for token efficiency, reducing the computational cost of running complex tasks. The model can integrate with development tools and frameworks to support real-world applications. It is designed to complete tasks that would typically require significant human effort over extended periods. Xiaomi has made the model open source, enabling developers to access and customize it. By combining performance, scalability, and efficiency, MiMo-V2.5-Pro pushes the boundaries of modern AI capabilities.

GLM-4.1V is an advanced vision-language model that offers a robust and streamlined multimodal capability for reasoning and understanding across various forms of media, including images, text, and documents. The 9-billion-parameter version, known as GLM-4.1V-9B-Thinking, is developed on the foundation of GLM-4-9B and has been improved through a unique training approach that employs Reinforcement Learning with Curriculum Sampling (RLCS). This model accommodates a context window of 64k tokens and can process high-resolution inputs, supporting images up to 4K resolution with any aspect ratio, which allows it to tackle intricate tasks such as optical character recognition, image captioning, chart and document parsing, video analysis, scene comprehension, and GUI-agent workflows, including the interpretation of screenshots and recognition of UI elements. In benchmark tests conducted at the 10 B-parameter scale, GLM-4.1V-9B-Thinking demonstrated exceptional capabilities, achieving the highest performance on 23 out of 28 evaluated tasks. Its advancements signify a substantial leap forward in the integration of visual and textual data, setting a new standard for multimodal models in various applications.