List of the Top AI Models for Windows in 2026 - Page 4

Perplexity AI has unveiled R1 1776 as an open-source large language model (LLM) constructed on the DeepSeek R1 framework, aimed at promoting transparency and facilitating collaborative endeavors in AI development. This release allows researchers and developers to delve into the model's architecture and source code, enabling them to refine and adapt it for various applications. Through the public availability of R1 1776, Perplexity AI aspires to stimulate innovation while maintaining ethical principles within the AI industry. This initiative not only empowers the community but also cultivates a culture of shared knowledge and accountability among those working in AI. Furthermore, it represents a significant step towards democratizing access to advanced AI technologies.

SmolLM2 features a sophisticated range of compact language models designed for effective on-device operations. This assortment includes models with various parameter counts, such as a substantial 1.7 billion, alongside more efficient iterations at 360 million and 135 million parameters, which guarantees optimal functionality on devices with limited resources. The models are particularly adept at text generation and have been fine-tuned for scenarios that demand quick responses and low latency, ensuring they deliver exceptional results in diverse applications, including content creation, programming assistance, and understanding natural language. The adaptability of SmolLM2 makes it a prime choice for developers who wish to embed powerful AI functionalities into mobile devices, edge computing platforms, and other environments where resource availability is restricted. Its thoughtful design exemplifies a dedication to achieving a balance between high performance and user accessibility, thus broadening the reach of advanced AI technologies. Furthermore, the ongoing development of such models signals a promising future for AI integration in everyday technology.

QwQ-Max-Preview represents an advanced AI model built on the Qwen2.5-Max architecture, designed to demonstrate exceptional abilities in areas such as intricate reasoning, mathematical challenges, programming tasks, and agent-based activities. This preview highlights its improved functionalities across various general-domain applications, showcasing a strong capability to handle complex workflows effectively. Set to be launched as open-source software under the Apache 2.0 license, QwQ-Max-Preview is expected to feature substantial enhancements and refinements in its final version. In addition to its technical advancements, the model plays a vital role in fostering a more inclusive AI landscape, which is further supported by the upcoming release of the Qwen Chat application and streamlined model options like QwQ-32B, aimed at developers seeking local deployment alternatives. This initiative not only enhances accessibility for a broader audience but also stimulates creativity and progress within the AI community, ensuring that diverse voices can contribute to the field's evolution. The commitment to open-source principles is likely to inspire further exploration and collaboration among developers.

Gemma 3, introduced by Google, is a state-of-the-art AI model built on the Gemini 2.0 architecture, specifically engineered to provide enhanced efficiency and flexibility. This groundbreaking model is capable of functioning effectively on either a single GPU or TPU, which broadens access for a wide array of developers and researchers. By prioritizing improvements in natural language understanding, generation, and various AI capabilities, Gemma 3 aims to advance the performance of artificial intelligence systems significantly. With its scalable and durable design, Gemma 3 seeks to drive the progression of AI technologies across multiple fields and applications, ultimately holding the potential to revolutionize the technology landscape. As such, it stands as a pivotal development in the continuous integration of AI into everyday life and industry practices.

Mistral AI has unveiled the Mistral Large 2, an advanced AI model engineered to perform exceptionally well across various fields, including code generation, multilingual comprehension, and complex reasoning tasks. Boasting a remarkable 128k context window, this model supports a vast selection of languages such as English, French, Spanish, and Arabic, as well as more than 80 programming languages. Tailored for high-throughput single-node inference, Mistral Large 2 is ideal for applications that demand substantial context management. Its outstanding performance on benchmarks like MMLU, alongside enhanced abilities in code generation and reasoning, ensures both precision and effectiveness in outcomes. Moreover, the model is equipped with improved function calling and retrieval functionalities, which are especially advantageous for intricate business applications. This versatility positions Mistral Large 2 as a formidable asset for developers and enterprises eager to harness cutting-edge AI technologies for innovative solutions, ultimately driving efficiency and productivity in their operations.

Meta’s Llama 4 Behemoth is an advanced multimodal AI model that boasts 288 billion active parameters, making it one of the most powerful models in the world. It outperforms other leading models like GPT-4.5 and Gemini 2.0 Pro on numerous STEM-focused benchmarks, showcasing exceptional skills in math, reasoning, and image understanding. As the teacher model behind Llama 4 Scout and Llama 4 Maverick, Llama 4 Behemoth drives major advancements in model distillation, improving both efficiency and performance. Currently still in training, Behemoth is expected to redefine AI intelligence and multimodal processing once fully deployed.

Meta’s Llama 4 Maverick is a state-of-the-art multimodal AI model that packs 17 billion active parameters and 128 experts into a high-performance solution. Its performance surpasses other top models, including GPT-4o and Gemini 2.0 Flash, particularly in reasoning, coding, and image processing benchmarks. Llama 4 Maverick excels at understanding and generating text while grounding its responses in visual data, making it perfect for applications that require both types of information. This model strikes a balance between power and efficiency, offering top-tier AI capabilities at a fraction of the parameter size compared to larger models, making it a versatile tool for developers and enterprises alike.

Llama 4 Scout represents a leap forward in multimodal AI, featuring 17 billion active parameters and a groundbreaking 10 million token context length. With its ability to integrate both text and image data, Llama 4 Scout excels at tasks like multi-document summarization, complex reasoning, and image grounding. It delivers superior performance across various benchmarks and is particularly effective in applications requiring both language and visual comprehension. Scout's efficiency and advanced capabilities make it an ideal solution for developers and businesses looking for a versatile and powerful model to enhance their AI-driven projects.

The Max Plan from Claude offers high-powered usage for those who depend heavily on Claude for daily work and large-scale tasks. This plan provides users with up to 20 times more usage than the standard Pro plan, making it ideal for individuals or teams that require consistent and intensive AI collaboration. Whether for long, ongoing conversations, data-heavy tasks, or quick, high-stakes decision-making, the Max Plan ensures Claude is available without disruption. It includes priority access to new features, automated task support, and the ability to scale your usage based on fluctuating project demands, making it a flexible solution for professionals who rely on AI-powered assistance.

Qwen3, the latest large language model from the Qwen family, introduces a new level of flexibility and power for developers and researchers. With models ranging from the high-performance Qwen3-235B-A22B to the smaller Qwen3-4B, Qwen3 is engineered to excel across a variety of tasks, including coding, math, and natural language processing. The unique hybrid thinking modes allow users to switch between deep reasoning for complex tasks and fast, efficient responses for simpler ones. Additionally, Qwen3 supports 119 languages, making it ideal for global applications. The model has been trained on an unprecedented 36 trillion tokens and leverages cutting-edge reinforcement learning techniques to continually improve its capabilities. Available on multiple platforms, including Hugging Face and ModelScope, Qwen3 is an essential tool for those seeking advanced AI-powered solutions for their projects.

Mistral Medium 3 is a breakthrough in AI technology, offering the perfect balance of cutting-edge performance and significantly reduced costs. This model introduces a new era of enterprise AI, with a focus on simplifying deployments while still providing exceptional performance. Its ability to deliver high-level results at just a fraction of the cost of its competitors makes it a game-changer in industries that rely on complex AI tasks. Mistral Medium 3 is particularly strong in professional use cases like coding, where it competes closely with larger models that are typically more expensive and slower. The model supports hybrid and on-premises deployments, offering enterprise users full control over customization and integration into their systems. Businesses can leverage Mistral Medium 3 for both large-scale deployments and fine-tuned, domain-specific training, allowing for enhanced efficiency in industries such as healthcare, financial services, and energy. The addition of continuous learning and the ability to integrate with enterprise knowledge bases makes it a flexible, future-proof solution. Customers in beta are already using Mistral Medium 3 to enrich customer service, personalize business processes, and analyze complex datasets, demonstrating its real-world value. Available through various cloud platforms like Amazon Sagemaker, IBM WatsonX, and Google Cloud Vertex, Mistral Medium 3 is now ready to be deployed for custom use cases across a range of industries.

Qwen3-Coder is a multifaceted coding model available in different sizes, prominently showcasing the 480B-parameter Mixture-of-Experts variant with 35B active parameters, which adeptly manages 256K-token contexts that can be scaled up to 1 million tokens. It demonstrates remarkable performance comparable to Claude Sonnet 4, having been pre-trained on a staggering 7.5 trillion tokens, with 70% of that data comprising code, and it employs synthetic data fine-tuned through Qwen2.5-Coder to bolster both coding proficiency and overall effectiveness. Additionally, the model utilizes advanced post-training techniques that incorporate substantial, execution-guided reinforcement learning, enabling it to generate a wide array of test cases across 20,000 parallel environments, thus excelling in multi-turn software engineering tasks like SWE-Bench Verified without requiring test-time scaling. Beyond the model itself, the open-source Qwen Code CLI, inspired by Gemini Code, equips users to implement Qwen3-Coder within dynamic workflows by utilizing customized prompts and function calling protocols while ensuring seamless integration with Node.js, OpenAI SDKs, and environment variables. This robust ecosystem not only aids developers in enhancing their coding projects efficiently but also fosters innovation by providing tools that adapt to various programming needs. Ultimately, Qwen3-Coder stands out as a powerful resource for developers seeking to improve their software development processes.

GPT-5 mini is a faster, more affordable variant of OpenAI’s advanced GPT-5 language model, specifically tailored for well-defined and precise tasks that benefit from high reasoning ability. It accepts both text and image inputs (image input only), and generates high-quality text outputs, supported by a large 400,000-token context window and a maximum of 128,000 tokens in output, enabling complex multi-step reasoning and detailed responses. The model excels in providing rapid response times, making it ideal for use cases where speed and efficiency are critical, such as chatbots, customer service, or real-time analytics. GPT-5 mini’s pricing structure significantly reduces costs, with input tokens priced at $0.25 per million and output tokens at $2 per million, offering a more economical option compared to the flagship GPT-5. While it supports advanced features like streaming, function calling, structured output generation, and fine-tuning, it does not currently support audio input or image generation capabilities. GPT-5 mini integrates seamlessly with multiple API endpoints including chat completions, responses, embeddings, and batch processing, providing versatility for a wide array of applications. Rate limits are tier-based, scaling from 500 requests per minute up to 30,000 per minute for higher tiers, accommodating small to large scale deployments. The model also supports snapshots to lock in performance and behavior, ensuring consistency across applications. GPT-5 mini is ideal for developers and businesses seeking a cost-effective solution with high reasoning power and fast throughput. It balances cutting-edge AI capabilities with efficiency, making it a practical choice for applications demanding speed, precision, and scalability.

GPT-5 nano is OpenAI’s fastest and most cost-efficient version of the GPT-5 model, engineered to handle high-speed text and image input processing for tasks such as summarization, classification, and content generation. It features an extensive 400,000-token context window and can output up to 128,000 tokens, allowing for complex, multi-step language understanding despite its focus on speed. With ultra-low pricing—$0.05 per million input tokens and $0.40 per million output tokens—GPT-5 nano makes advanced AI accessible to budget-conscious users and developers working at scale. The model supports a variety of advanced API features, including streaming output, function calling for interactive applications, structured outputs for precise control, and fine-tuning for customization. While it lacks support for audio input and web search, GPT-5 nano supports image input, code interpretation, and file search, broadening its utility. Developers benefit from tiered rate limits that scale from 500 to 30,000 requests per minute and up to 180 million tokens per minute, supporting everything from small projects to enterprise workloads. The model also offers snapshots to lock performance and behavior, ensuring consistent results over time. GPT-5 nano strikes a practical balance between speed, cost, and capability, making it ideal for fast, efficient AI implementations where rapid turnaround and budget are critical. It fits well for applications requiring real-time summarization, classification, chatbots, or lightweight natural language processing tasks. Overall, GPT-5 nano expands the accessibility of OpenAI’s powerful AI technology to a broader user base.

NVIDIA Cosmos is an innovative platform designed specifically for developers, featuring state-of-the-art generative World Foundation Models (WFMs), sophisticated video tokenizers, robust safety measures, and an efficient data processing and curation system that enhances the development of physical AI technologies. This platform equips developers engaged in fields like autonomous vehicles, robotics, and video analytics AI agents with the tools needed to generate highly realistic, physics-informed synthetic video data, drawing from a vast dataset that includes 20 million hours of both real and simulated footage. As a result, it allows for the quick simulation of future scenarios, the training of world models, and the customization of particular behaviors. The architecture of the platform consists of three main types of WFMs: Cosmos Predict, capable of generating up to 30 seconds of continuous video from diverse input modalities; Cosmos Transfer, which adapts simulations to function effectively across varying environments and lighting conditions, enhancing domain augmentation; and Cosmos Reason, a vision-language model that applies structured reasoning to interpret spatial-temporal data for effective planning and decision-making. Through these advanced capabilities, NVIDIA Cosmos not only accelerates the innovation cycle in physical AI applications but also promotes significant advancements across a wide range of industries, ultimately contributing to the evolution of intelligent technologies.

NVIDIA has developed Isaac GR00T (Generalist Robot 00 Technology) as a pioneering research initiative designed to facilitate the development of adaptable humanoid robot foundation models and the relevant data processes. Among its offerings is the Isaac GR00T-N model, which is supplemented by synthetic motion templates, GR00T-Mimic for refining demonstrations, and GR00T-Dreams, a feature that produces new synthetic pathways to advance humanoid robotics swiftly. A notable recent advancement is the release of the open-source Isaac GR00T N1 foundation model, which boasts a dual cognitive architecture encompassing a quick-acting “System 1” model and a language-capable, analytical “System 2” model for reasoning. The upgraded GR00T N1.5 version incorporates substantial enhancements, such as better vision-language grounding, superior execution of language directives, heightened adaptability through few-shot learning, and compatibility with various robot forms. By leveraging tools like Isaac Sim, Lab, and Omniverse, the GR00T platform empowers developers to train, simulate, post-train, and deploy flexible humanoid agents that utilize both real and synthetic data effectively. This holistic strategy not only accelerates advancements in robotics research but also paves the way for groundbreaking innovations in the realm of humanoid robotic applications, promising to reshape the landscape of the industry.

Qwen3-Max is Alibaba's state-of-the-art large language model, boasting an impressive trillion parameters designed to enhance performance in tasks that demand agency, coding, reasoning, and the management of long contexts. As a progression of the Qwen3 series, this model utilizes improved architecture, training techniques, and inference methods; it features both thinker and non-thinker modes, introduces a distinctive “thinking budget” approach, and offers the flexibility to switch modes according to the complexity of the tasks. With its capability to process extremely long inputs and manage hundreds of thousands of tokens, it also enables the invocation of tools and showcases remarkable outcomes across various benchmarks, including evaluations related to coding, multi-step reasoning, and agent assessments like Tau2-Bench. Although the initial iteration primarily focuses on following instructions within a non-thinking framework, Alibaba plans to roll out reasoning features that will empower autonomous agent functionalities in the near future. Furthermore, with its robust multilingual support and comprehensive training on trillions of tokens, Qwen3-Max is available through API interfaces that integrate well with OpenAI-style functionalities, guaranteeing extensive applicability across a range of applications. This extensive and innovative framework positions Qwen3-Max as a significant competitor in the field of advanced artificial intelligence language models, making it a pivotal tool for developers and researchers alike.

GLM-4.6 builds on the groundwork established by its predecessor, offering improved reasoning, coding, and agent functionalities that lead to significant improvements in inferential precision, better tool application during reasoning exercises, and a smoother incorporation into agent architectures. In extensive benchmark assessments evaluating reasoning, coding, and agent performance, GLM-4.6 outperforms GLM-4.5 and holds its own against competitive models such as DeepSeek-V3.2-Exp and Claude Sonnet 4, though it still trails Claude Sonnet 4.5 regarding coding proficiency. Additionally, when evaluated through practical testing using a comprehensive “CC-Bench” suite, which encompasses tasks related to front-end development, tool creation, data analysis, and algorithmic challenges, GLM-4.6 shows superior performance compared to GLM-4.5, achieving a nearly equal standing with Claude Sonnet 4, winning around 48.6% of direct matchups while exhibiting an approximate 15% boost in token efficiency. This newest iteration is available via the Z.ai API, allowing developers to utilize it either as a backend for an LLM or as the fundamental component in an agent within the platform's API ecosystem. Moreover, the enhancements in GLM-4.6 promise to significantly elevate productivity across diverse application areas, making it a compelling choice for developers eager to adopt the latest advancements in AI technology. Consequently, the model's versatility and performance improvements position it as a key player in the ongoing evolution of AI-driven solutions.

We are excited to present DeepSeek-V3.2-Exp, our latest experimental model that evolves from V3.1-Terminus, incorporating the cutting-edge DeepSeek Sparse Attention (DSA) technology designed to significantly improve both training and inference speeds for longer contexts. This innovative DSA framework enables accurate sparse attention while preserving the quality of outputs, resulting in enhanced performance for long-context tasks alongside reduced computational costs. Benchmark evaluations demonstrate that V3.2-Exp delivers performance on par with V3.1-Terminus, all while benefiting from these efficiency gains. The model is fully functional across various platforms, including app, web, and API. In addition, to promote wider accessibility, we have reduced DeepSeek API pricing by more than 50% starting now. During this transition phase, users will have access to V3.1-Terminus through a temporary API endpoint until October 15, 2025. DeepSeek invites feedback on DSA from users via our dedicated feedback portal, encouraging community engagement. To further support this initiative, DeepSeek-V3.2-Exp is now available as open-source, with model weights and key technologies—including essential GPU kernels in TileLang and CUDA—published on Hugging Face, and we are eager to observe how the community will leverage this significant technological advancement. As we unveil this new chapter, we anticipate fruitful interactions and innovative applications arising from the collective contributions of our user base.

HunyuanVision, a cutting-edge vision-language model developed by Tencent's Hunyuan team, utilizes a unique mamba-transformer hybrid architecture that significantly enhances performance while ensuring efficient inference for various multimodal reasoning tasks. The most recent version, Hunyuan-Vision-1.5, emphasizes the notion of "thinking on images," which empowers it to understand the interactions between visual and textual elements and perform complex reasoning tasks such as cropping, zooming, pointing, box drawing, and annotating images to improve comprehension. This adaptable model caters to a wide range of vision-related tasks, including image and video recognition, optical character recognition (OCR), and diagram analysis, while also promoting visual reasoning and 3D spatial understanding, all within a unified multilingual framework. With a design that accommodates multiple languages and tasks, HunyuanVision intends to be open-sourced, offering access to various checkpoints, a detailed technical report, and inference support to encourage community involvement and experimentation. This initiative not only seeks to empower researchers and developers to tap into the model's potential for diverse applications but also aims to foster collaboration among users to drive innovation within the field. By making these resources available, HunyuanVision aspires to create a vibrant ecosystem for further advancements in multimodal AI.

DeepSeek-V3.2 represents one of the most advanced open-source LLMs available, delivering exceptional reasoning accuracy, long-context performance, and agent-oriented design. The model introduces DeepSeek Sparse Attention (DSA), a breakthrough attention mechanism that maintains high-quality output while significantly lowering compute requirements—particularly valuable for long-input workloads. DeepSeek-V3.2 was trained with a large-scale reinforcement learning framework capable of scaling post-training compute to the level required to rival frontier proprietary systems. Its Speciale variant surpasses GPT-5 on reasoning benchmarks and achieves performance comparable to Gemini-3.0-Pro, including gold-medal scores in the IMO and IOI 2025 competitions. The model also features a fully redesigned agentic training pipeline that synthesizes tool-use tasks and multi-step reasoning data at scale. A new chat template architecture introduces explicit thinking blocks, robust tool-interaction formatting, and a specialized developer role designed exclusively for search-powered agents. To support developers, the repository includes encoding utilities that translate OpenAI-style prompts into DeepSeek-formatted input strings and parse model output safely. DeepSeek-V3.2 supports inference using safetensors and fp8/bf16 precision, with recommendations for ideal sampling settings when deployed locally. The model is released under the MIT license, ensuring maximal openness for commercial and research applications. Together, these innovations make DeepSeek-V3.2 a powerful choice for building next-generation reasoning applications, agentic systems, research assistants, and AI infrastructures.

DeepSeek-V3.2-Speciale represents the pinnacle of DeepSeek’s open-source reasoning models, engineered to deliver elite performance on complex analytical tasks. It introduces DeepSeek Sparse Attention (DSA), a highly efficient long-context attention design that reduces the computational burden while maintaining deep comprehension and logical consistency. The model is trained with an expanded reinforcement learning framework capable of leveraging massive post-training compute, enabling performance not only comparable to GPT-5 but demonstrably surpassing it in internal tests. Its reasoning capabilities have been validated through gold-winning solutions across major global competitions, including IMO 2025 and IOI 2025, with official submissions released for transparency and peer assessment. DeepSeek-V3.2-Speciale is intentionally designed without tool-calling features, focusing every parameter on pure reasoning, multi-step logic, and structured problem solving. It introduces a reworked chat template featuring explicit thought-delimited sections and a structured message format optimized for agentic-style reasoning workflows. The repository includes Python-based utilities for encoding and parsing messages, illustrating how to format prompts correctly for the model. Supporting multiple tensor types (BF16, FP32, FP8_E4M3), it is built for both research experimentation and high-performance local deployment. Users are encouraged to use temperature = 1.0 and top_p = 0.95 for best results when running the model locally. With its open MIT license and transparent development process, DeepSeek-V3.2-Speciale stands as a breakthrough option for anyone requiring industry-leading reasoning capacity in an open LLM.

Lux marks a major leap in AI capability by giving models the ability to operate real software environments—moving a cursor, pressing buttons, filling forms, navigating dashboards, and performing full computer workflows autonomously. It combines three powerful execution modes: Tasker for strict step-by-step reliability, Actor for rapid-response actions, and Thinker for extended reasoning across complex tasks that may take minutes or hours. These modes allow Lux to support a diverse set of use cases such as Amazon marketplace data extraction, automated QA test execution in developer environments, and instant retrieval of insider trading information from Nasdaq. Developers can begin building production-grade agents in under 20 minutes using Lux’s SDKs, frameworks, and ready-made UX templates. Unlike traditional AI models that only generate outputs, Lux operates inside real interfaces, enabling automation for businesses that rely on human-facing applications. The system understands both simple instructions and vague requests, planning its actions and executing long chains of behavior with high stability. This capability unlocks new possibilities for software automation, from enterprise workflows to gaming, analytics, and back-office operations. Lux represents a broader paradigm shift in AI—from information generation to direct action—making machines capable of using computers as humans do. By democratizing a skill previously limited to the world’s largest AI labs, Lux empowers developers everywhere to build advanced computer-use agents. With Lux, AI becomes not just a tool for insights, but a workforce capable of performing digital tasks at scale.

Devstral 2 is an innovative, open-source AI model tailored for software engineering, transcending simple code suggestions to fully understand and manipulate entire codebases; this advanced functionality enables it to execute tasks such as multi-file edits, bug fixes, refactoring, managing dependencies, and generating code that is aware of its context. The suite includes a powerful 123-billion-parameter model alongside a streamlined 24-billion-parameter variant called “Devstral Small 2,” offering flexibility for teams; the larger model excels in handling intricate coding tasks that necessitate a deep contextual understanding, whereas the smaller model is optimized for use on less robust hardware. With a remarkable context window capable of processing up to 256 K tokens, Devstral 2 is adept at analyzing extensive repositories, tracking project histories, and maintaining a comprehensive understanding of large files, which is especially advantageous for addressing the challenges of real-world software projects. Additionally, the command-line interface (CLI) further enhances the model’s functionality by monitoring project metadata, Git statuses, and directory structures, thereby enriching the AI’s context and making “vibe-coding” even more impactful. This powerful blend of features solidifies Devstral 2's role as a revolutionary tool within the software development ecosystem, offering unprecedented support for engineers. As the landscape of software engineering continues to evolve, tools like Devstral 2 promise to redefine the way developers approach coding tasks.

Devstral Small 2 is a condensed, 24 billion-parameter variant of Mistral AI's groundbreaking coding-focused models, made available under the adaptable Apache 2.0 license to support both local use and API access. Alongside its more extensive sibling, Devstral 2, it offers "agentic coding" capabilities tailored for low-computational environments, featuring a substantial 256K-token context window that enables it to understand and alter entire codebases with ease. With a performance score nearing 68.0% on the widely recognized SWE-Bench Verified code-generation benchmark, Devstral Small 2 distinguishes itself within the realm of open-weight models that are much larger. Its compact structure and efficient design allow it to function effectively on a single GPU or even in CPU-only setups, making it an excellent option for developers, small teams, or hobbyists who may lack access to extensive data-center facilities. Moreover, despite being smaller, Devstral Small 2 retains critical functionalities found in its larger counterparts, such as the capability to reason through multiple files and adeptly manage dependencies, ensuring that users enjoy substantial coding support. This combination of efficiency and high performance positions it as an indispensable asset for the coding community. Additionally, its user-friendly approach ensures that both novice and experienced programmers can leverage its capabilities without significant barriers.