List of the Top 25 AI Models for Vercel AI Gateway in 2026

Gemini 3.5 Flash is Google’s next-generation frontier AI model engineered to combine advanced reasoning, multimodal intelligence, agentic automation, and high-speed performance for developers, enterprises, and everyday users. As the first publicly released model in the Gemini 3.5 family, the platform is designed to execute complex long-horizon workflows while delivering fast response speeds and strong performance across coding, reasoning, multimodal understanding, and AI-driven automation tasks. Gemini 3.5 Flash significantly advances Google’s agentic AI capabilities by enabling AI systems to plan, execute, iterate, and manage multi-step workflows such as software engineering, codebase maintenance, financial analysis, application development, infrastructure operations, and large-scale enterprise automation. Powered by the updated Antigravity harness, the model can coordinate collaborative subagents that work together to complete demanding workflows under supervision while maintaining high reliability and operational efficiency. Gemini 3.5 Flash also demonstrates advanced multimodal capabilities by generating dynamic graphics, interactive web interfaces, animations, and visually rich experiences that support developers and businesses building AI-powered applications and user experiences. The model achieves frontier-level performance across multiple coding, agentic, and multimodal benchmarks while operating at significantly faster output speeds compared to many competing frontier AI systems, helping reduce workflow latency and operational costs. Google has integrated Gemini 3.5 Flash across a broad ecosystem that includes the Gemini app, AI Mode in Google Search, Google AI Studio, Android Studio, Gemini Enterprise Agent Platform, and enterprise AI products to provide global access to advanced AI automation capabilities.

Kimi K2.7 Code is an open-source agentic coding model from Moonshot AI designed for developers, engineering teams, and AI coding workflows that require long-context understanding and multi-step execution. It is built for real-world software engineering tasks, including code generation, code review, debugging, repository navigation, tool use, and long-horizon development work. The model is described by Moonshot AI as a coding-focused agentic model with stronger performance on complex coding tasks than earlier Kimi K2 releases. Kimi K2.7 Code supports a 256K context window, allowing it to process large codebases, technical requirements, logs, documentation, and multi-file development context in a single workflow. It is available through Kimi Code, which provides developer-oriented tools for using the model in coding tasks. The model can also be accessed through Moonshot’s API platform, where Kimi K2.7 Code and Kimi K2.7 Code Highspeed are offered alongside earlier Kimi models. For developers who want more control, Kimi K2.7 Code is listed on Hugging Face with deployment support for inference engines such as vLLM, SGLang, and KTransformers. It uses OpenAI- and Anthropic-compatible API options, helping teams connect it to existing applications, coding tools, and agent systems more easily. Third-party model listings describe it as using a 1T-parameter mixture-of-experts architecture with 32B active parameters, native INT4 quantization, and reduced thinking-token usage compared with Kimi K2.6. The model is designed to improve efficiency by using fewer reasoning tokens while still supporting demanding programming workflows. Kimi K2.7 Code is a strong fit for developers who want an open, long-context, tool-friendly AI model for software engineering automation and AI-assisted development.

Claude Opus 4.7 represents a major step forward in AI model development, focusing on advanced reasoning, coding, and enterprise-level task execution. It improves significantly over Opus 4.6 by delivering stronger performance on complex and high-effort software engineering challenges. The model is particularly effective at managing long-running processes, maintaining consistency, and producing reliable outputs over time. Its enhanced instruction-following capabilities ensure that it interprets prompts more literally and executes tasks with greater precision. Opus 4.7 also features advanced self-checking mechanisms, enabling it to validate its own responses before completion. A major highlight is its improved multimodal support, allowing it to process high-resolution images and extract fine visual details. This capability is especially useful for tasks like analyzing technical screenshots, interpreting diagrams, and supporting computer-based workflows. The model produces high-quality professional outputs, including refined documents, presentations, and UI designs that meet business standards. It also demonstrates strong performance across industries such as finance, legal services, and data analysis. Enhanced memory capabilities allow it to retain important context across sessions, making it more efficient for ongoing projects. Opus 4.7 includes safety and alignment improvements, with systems in place to detect and block potentially harmful or restricted use cases. It introduces new controls for balancing reasoning depth and response speed, giving users flexibility based on task complexity. Widely accessible through APIs and major cloud platforms, Opus 4.7 is designed to support scalable, high-performance AI applications for modern enterprises.

GPT-5.5 represents a new class of AI built to transform how work is done across digital environments. It combines advanced reasoning, tool usage, and task execution capabilities to manage complex, multi-step workflows with minimal human intervention. The model performs strongly in software engineering, data analysis, business operations, and scientific research, where it can plan tasks, gather information, test solutions, and refine outputs iteratively. It supports generating documents, building applications, analyzing large datasets, and navigating software systems as part of a unified workflow. A key capability is its integration with workspace agents—customizable AI agents that can be created once and deployed across teams to automate entire processes. These agents can run continuously, interact with tools like CRM systems, messaging platforms, and document editors, and keep workflows moving without constant supervision. Organizations can define permissions, approval checkpoints, and monitoring to maintain full control over automation. GPT-5.5 also improves collaboration by standardizing workflows and scaling best practices across teams. With enterprise-grade security and governance, it is designed for safe deployment in complex environments. Its ability to persist through ambiguity and long-running tasks makes it highly effective for execution-heavy work. By reducing manual intervention and increasing speed, GPT-5.5 enables teams to focus on higher-value activities and operate at a significantly higher level of productivity.

Claude Opus 4.8 is Anthropic’s latest frontier AI model engineered to deliver advanced coding intelligence, reasoning capabilities, autonomous workflows, and enterprise-grade collaboration for developers, technical teams, and organizations building AI-powered systems. As the successor to Claude Opus 4.7, the model introduces improvements across software engineering, agentic execution, practical knowledge work, benchmark performance, and alignment behavior while retaining the same standard pricing structure. Claude Opus 4.8 is specifically optimized for complex coding tasks, large-scale workflow orchestration, long-running automation processes, and advanced reasoning scenarios where reliability, transparency, and contextual judgment are critical. One of the model’s defining advancements is its improved honesty and uncertainty awareness, making it significantly less likely to produce unsupported conclusions or overlook defects in generated code, reasoning chains, and operational outputs. Anthropic’s alignment assessments also report stronger prosocial behavior, lower rates of deceptive or unsafe actions, and improved adherence to user intent compared to earlier Opus releases. The release introduces configurable effort controls that allow users to determine how much computational reasoning the model applies to a task, enabling flexible tradeoffs between speed, token consumption, and response depth depending on workflow complexity. Claude Opus 4.8 also powers new “dynamic workflows” functionality in Claude Code, where the model can coordinate hundreds of parallel AI subagents during a single session to execute large-scale software engineering operations such as repository-wide migrations, testing workflows, and multi-step automation tasks. Anthropic further expanded the platform with lower-cost fast mode processing, enabling the model to operate at significantly higher speeds while remaining more affordable than previous high-performance configurations.

GPT-5.6 Terra is a balanced model in OpenAI’s GPT-5.6 series, designed to provide strong performance for everyday work while keeping costs lower than the flagship Sol tier. The GPT-5.6 family includes Sol for the highest capability, Terra for balanced work, and Luna for fast and affordable use cases. Terra is positioned as a practical option for developers, businesses, and enterprise teams that need capable reasoning, coding, automation, research support, and defensive security assistance without always using the most expensive model. According to the pasted preview text, Terra offers competitive performance to GPT-5.5 while being 2x cheaper. It appears in GPT-5.6 benchmark previews for Terminal-Bench 2.1, GeneBench v1, ExploitBench, and ExploitGym, showing that the model is intended for technical and long-horizon tasks as well as general work. Terra can support coding workflows that require planning, iteration, command-line reasoning, and tool coordination. It can also support legitimate cybersecurity workflows such as code review, vulnerability research, patch development, debugging, security education, and defensive testing. The model is developed with layered safeguards matched to its capabilities, including trained refusals, real-time checks, misuse classifiers, monitoring, enforcement, and account-level review. OpenAI also describes automated red-teaming and third-party human expert red-teaming as part of the broader GPT-5.6 safety process. Terra is priced below Sol in the pasted API pricing structure, with lower input and output costs per 1 million tokens. GPT-5.6 Terra helps organizations use a capable GPT-5.6 model for production workflows where performance, cost efficiency, and safety controls all matter.

Grok Build 0.1 is a developer-focused AI model from xAI that has been specifically trained for agentic software engineering workflows. The model is designed to go beyond traditional code generation by supporting multi-step problem solving, planning, implementation, testing, and iterative refinement. It can process both text and image inputs, allowing developers to provide code snippets, architecture diagrams, screenshots, and technical documents as context. Grok Build 0.1 is optimized for interactive coding environments where AI agents need to perform complex actions across multiple stages of development. The model supports advanced capabilities such as tool calling, structured JSON outputs, and workflow automation, making it suitable for integration into modern engineering pipelines. With a 256,000-token context window, it can analyze large codebases and maintain awareness of extensive project histories. The platform is designed to work effectively with autonomous coding agents that require planning and reasoning abilities to complete sophisticated tasks. xAI has positioned the model as a successor to Grok Code Fast models, focusing on long-running development workflows rather than simple coding assistance. Grok Build 0.1 is available through API access, enabling organizations to incorporate its capabilities into custom applications and developer tools. Its architecture supports scenarios such as debugging, refactoring, code reviews, automation, and collaborative software development. The model helps developers increase productivity by providing AI assistance that can understand, reason about, and execute complex engineering tasks at scale.

Claude Fable 5 is a frontier AI model developed by Anthropic to deliver advanced reasoning, coding, research, and multimodal capabilities for enterprise and professional users. As a Mythos-class model adapted for broad availability, it combines high-level intelligence with safety-focused deployment controls. The model excels at software engineering tasks, including large-scale code analysis, migrations, debugging, architecture review, and autonomous project execution. Claude Fable 5 also demonstrates strong performance in knowledge work, helping users analyze documents, evaluate financial information, interpret charts and tables, conduct research, and generate actionable insights. Its vision capabilities enable sophisticated image understanding, visual reasoning, and screenshot-based analysis. The model supports long-context workflows and persistent memory utilization, allowing it to work effectively on extended tasks involving millions of tokens of information. Anthropic has implemented a layered safety framework that includes specialized classifiers for cybersecurity, biology, chemistry, and model distillation-related requests. When these areas are detected, requests may be handled by a different model with stricter operational controls. Claude Fable 5 is available through the Claude API and Anthropic’s product ecosystem, providing developers and enterprises with access to advanced AI-powered assistance. The model is designed to enhance productivity, accelerate research, improve software development workflows, and support complex analytical tasks. By combining powerful reasoning, multimodal intelligence, and enterprise-focused safeguards, Claude Fable 5 enables organizations to scale AI adoption responsibly and effectively.

Claude Mythos 5 is Anthropic’s restricted-access Mythos-class AI model built for trusted organizations that require the highest level of Claude capability. The model shares the same underlying architecture as Claude Fable 5, but is offered with certain safeguards removed for approved use cases and vetted users. Claude Mythos 5 is designed for advanced cybersecurity, software engineering, scientific discovery, long-context reasoning, and autonomous research workflows. It is initially deployed through Project Glasswing for cyberdefenders and critical infrastructure providers. The model is intended to help security teams analyze complex systems, support defensive cybersecurity work, and protect important software environments. Claude Mythos 5 also demonstrates major potential in life sciences, where it can assist with protein design, binding-site selection, bioinformatics workflows, and research hypothesis generation. Anthropic reports that the model can carry out extended technical tasks, recover from failures, and operate with a high degree of autonomy. Its capabilities in genomics include assembling large-scale single-cell datasets and designing custom machine learning approaches for biological research. Because these capabilities may be dual-use, Anthropic limits access through trusted programs and applies a 30-day retention policy for Mythos-class traffic. The model is priced at $10 per million input tokens and $50 per million output tokens. Claude Mythos 5 helps vetted organizations apply frontier AI to critical defense, infrastructure, and scientific problems while maintaining controlled access and oversight.

GLM-5.2 is a powerful AI foundation model created to help developers and organizations handle advanced reasoning, coding, automation, and agent-based workflows. It is designed for complex system engineering tasks where an AI model needs to understand goals, follow multi-step instructions, and support technical execution. The model can be used for software development, code analysis, documentation support, research assistance, workflow automation, and intelligent application development. GLM-5.2 is especially valuable for long-context tasks because it can work with large amounts of information across extended prompts, files, or conversations. This makes it useful for reviewing large codebases, summarizing technical materials, generating structured outputs, and supporting detailed problem-solving. Its mixture-of-experts architecture helps deliver strong performance while using active model resources more efficiently. Development teams can use GLM-5.2 to improve productivity by reducing repetitive work and accelerating technical decision-making. Businesses can also use it to power AI assistants, internal automation tools, research platforms, and customer-facing intelligent systems. The model’s focus on agentic capabilities allows it to support workflows that require planning, reasoning, and task completion rather than basic response generation. GLM-5.2 can help organizations build smarter products while giving technical teams a more capable AI partner for demanding projects. It is a strong option for companies that want scalable AI support across engineering, research, automation, and digital transformation initiatives.

GPT-5.6 Sol is a next-generation OpenAI model previewed as the flagship option in the GPT-5.6 family. The series includes Sol for the strongest capability, Terra for balanced everyday work, and Luna for faster, lower-cost use cases. GPT-5.6 Sol is built for demanding work across coding, agentic automation, biology, cybersecurity, research, and enterprise knowledge workflows. The model introduces a new max reasoning effort that allows it to spend more time reasoning through difficult problems. It also adds ultra mode, which coordinates subagents to help accelerate complex tasks that benefit from parallel or multi-agent execution. In coding workflows, GPT-5.6 Sol is designed for command-line tasks that require planning, iteration, testing, tool coordination, and long-horizon software engineering judgment. In biology workflows, it is positioned for genomics and quantitative-biology analysis where efficient reasoning over complex scientific tasks matters. In cybersecurity, GPT-5.6 Sol supports legitimate defensive work such as vulnerability discovery, patch development, debugging, security education, code review, and authorized testing. OpenAI describes GPT-5.6 Sol as more capable at helping users find and fix vulnerabilities than reliably carrying out end-to-end attacks under tested conditions. The model’s release is paired with a layered safeguard system that includes model-level refusals, real-time misuse classifiers, paused generation for higher-risk cases, account-level review, automated red-teaming, third-party testing, differentiated access, and enterprise safety controls. GPT-5.6 Sol helps developers, researchers, enterprises, and cyber defenders use frontier AI for advanced technical work while supporting safer deployment, stronger oversight, and phased access.

GPT-5.6 Luna is the lowest-cost model in OpenAI’s GPT-5.6 family, built for fast and affordable AI assistance across everyday and technical workflows. The GPT-5.6 lineup includes Sol as the flagship model, Terra as the balanced model for everyday work, and Luna as the efficient model for users who need strong capability at lower cost. Luna is intended for developers, businesses, and teams that need scalable AI for coding help, workflow automation, research support, analysis, customer-facing applications, and high-volume API usage. In the pasted preview text, Luna is presented as part of the same GPT-5.6 release process and benchmark set as Sol and Terra. It appears in evaluations for command-line coding workflows, long-horizon biology tasks, ExploitBench, and ExploitGym, indicating that it is designed to handle more than simple chat use cases. The model is priced at a lower per-token rate than Sol and Terra, making it more suitable for applications where cost efficiency is a major priority. GPT-5.6 Luna also supports the new GPT-5.6 prompt caching approach, including explicit cache breakpoints, a 30-minute minimum cache life, cache writes billed above the uncached input rate, and discounted cached-input reads. Like the rest of the GPT-5.6 family, Luna is developed with layered safeguards matched to model capability. These safeguards include trained refusals for prohibited cyber assistance, real-time misuse classifiers, paused generation for higher-risk cases, account-level review, monitoring, enforcement, automated red-teaming, and third-party human expert red-teaming. Luna is expected to support legitimate defensive and technical workflows such as code review, debugging, patch development, security education, and defensive testing while making prohibited misuse more difficult and detectable. GPT-5.6 Luna helps organizations deploy GPT-5.6-class AI where speed, affordability, scalability, and safe production use are the most important requirements.

Gemma 4 is a modern AI model introduced by Google and built on the Gemini architecture to provide enhanced performance and flexibility for developers and researchers. The model is designed to run efficiently on a single GPU or TPU, which makes powerful AI capabilities more accessible without requiring large-scale infrastructure. Gemma 4 focuses heavily on improving natural language understanding and text generation, enabling it to support a wide range of AI-powered applications. These capabilities allow developers to build systems such as conversational assistants, intelligent search tools, and automated content generation platforms. The architecture behind Gemma 4 enables the model to process language with greater accuracy while maintaining efficient computational requirements. This balance between performance and efficiency allows developers to experiment with advanced AI features without the need for extremely large computing environments. Gemma 4 is designed to be scalable so it can support both small development projects and larger enterprise applications. Researchers can also use the model to explore new approaches to machine learning and language processing. The model’s ability to run on widely available hardware makes it practical for organizations that want to integrate AI into their workflows. By combining strong language capabilities with efficient deployment requirements, Gemma 4 helps broaden access to advanced AI technology. Its design reflects a growing focus on creating models that are both powerful and practical for real-world use. As a result, Gemma 4 supports the continued expansion of AI applications across industries and research fields.

Grok 4.3 is a next-generation AI model from xAI that expands on the capabilities of the Grok 4 series with improved reasoning, real-time intelligence, and automation features. It is designed to handle complex, multi-step tasks such as coding, research, and decision-making with greater accuracy and consistency. The model integrates real-time data from the web and X, allowing it to provide up-to-date answers and insights. Grok 4.3 supports multimodal functionality, enabling it to process and generate content across text, images, and other formats. It operates within the SuperGrok Heavy tier, which offers enhanced compute power and access to advanced features. The model includes long-context capabilities, allowing it to analyze large datasets and extended conversations effectively. It also supports tool use and integrations, enabling it to interact with external systems and automate workflows. Grok 4.3 benefits from the multi-agent “heavy” configuration, which improves performance on complex reasoning tasks. It is optimized for speed, responsiveness, and real-time interaction. The model can be used for a wide range of applications, including software development, research, and business analysis. It builds on Grok’s foundation as an AI assistant integrated with modern platforms and environments. The system continues to evolve with ongoing updates and feature enhancements. Overall, Grok 4.3 represents a powerful AI solution for users seeking real-time intelligence and advanced automation capabilities.

GLM-5.1 marks the newest evolution in Z.ai’s GLM lineup, designed as a state-of-the-art AI model focused on agents, specifically for tasks involving coding, logical reasoning, and overseeing long-term processes. This version builds on the foundation set by GLM-5, which utilizes a Mixture-of-Experts (MoE) framework to maximize performance while keeping inference costs low, supporting a broader vision of making weight models available to developers. A key feature of GLM-5.1 is its ability to promote agentic behavior, enabling it to plan, execute, and enhance multi-step tasks rather than just responding to single prompts. The model is meticulously crafted to handle complex workflows, such as troubleshooting code, navigating repositories, and conducting sequential tasks, all while preserving context over extended periods. Compared to earlier models, GLM-5.1 provides improved reliability during prolonged interactions, ensuring consistency throughout longer sessions and reducing errors in multi-step reasoning tasks. Furthermore, this advancement represents a significant step forward in the realm of AI, especially in its proficiency for managing intricate task workflows with ease. With its innovative features, GLM-5.1 sets a new standard for what agent-focused AI can achieve in practical applications.

Qwen3.6-Max-Preview is a cutting-edge language model designed to elevate intelligence, adhere to instructions, and enhance the effectiveness of real-world agents within the Qwen ecosystem. Building on the Qwen3 series, this version features improved world knowledge, better alignment with user directives, and significant upgrades in coding capabilities for agents, enabling the model to proficiently handle complex, multi-step challenges and software development tasks. It is specifically tailored for situations that demand sophisticated reasoning and execution, allowing for an interactive approach that goes beyond simple response generation to include tool usage, management of extensive contexts, and structured problem-solving across disciplines such as coding, research, and business operations. The framework continues to reflect Qwen's dedication to creating large, efficient models capable of managing extensive context windows while ensuring dependable performance across multilingual and knowledge-driven initiatives. This innovative architecture not only aims to boost productivity but also fosters creativity in a wide range of applications, paving the way for future advancements in technology and collaboration.

Kimi K2.6 is a cutting-edge agentic AI model developed by Moonshot AI, designed to improve practical application, programming efficiency, and complex reasoning abilities beyond its forerunners, K2 and K2.5. Utilizing a Mixture-of-Experts framework, this model embodies the multimodal, agent-centric principles of the Kimi series, seamlessly combining language understanding, coding skills, and tool application into a unified system capable of planning and executing sophisticated workflows. It boasts advanced reasoning capabilities and superior agent planning, allowing it to break down tasks, coordinate multiple tools, and address challenges involving numerous files or steps with heightened accuracy and efficiency. Furthermore, it excels in tool-calling functions, ensuring a reliable connection with external platforms like web searches or APIs, while incorporating built-in validation systems to confirm the correctness of execution formats. Significantly, Kimi K2.6 marks a transformative advancement in the AI landscape, establishing new benchmarks for the intricacy and dependability of automated processes, and paving the way for future innovations in the field.

Qwen3.7-Max signifies the pinnacle of innovation in Qwen's proprietary model series, specifically designed for the agent-centric era, and acts as a solid platform for a multitude of applications such as writing and debugging code, automating office workflows, and sustaining prolonged autonomous browsing sessions. This model excels in coding performance, showcasing exceptional skills in software engineering, terminal operations, graphical user interface interactions, web surfing, and the effective use of agentic tools. By improving the synergy between the model's intelligence and actual agent execution, Qwen3.7-Max supports sophisticated planning, reasoning over extended contexts, reliable function invocation, and the management of complex, multi-step tasks in intricate workflows. Additionally, it enhances multimodal and document-oriented tasks via Qwen Studio, which facilitates chatbot interactions, interprets images and videos, creates visuals, processes documents, develops presentations, provides coding assistance, performs thorough research, and supports web development. With this extensive array of capabilities, Qwen3.7-Max is positioned as a premier solution for various operational requirements in today's dynamic digital environment, ensuring users can efficiently tackle a wide range of challenges. As technology continues to evolve, the importance of such advanced models will only grow, making Qwen3.7-Max an invaluable asset for future endeavors.

DeepSeek-V4 is a cutting-edge open-source AI model built to deliver exceptional performance in reasoning, coding, and large-scale data processing. It supports an industry-leading one million token context window, allowing it to manage long documents and complex tasks efficiently. The model includes two variants: DeepSeek-V4-Pro, which offers 1.6 trillion parameters with 49 billion active for top-tier performance, and DeepSeek-V4-Flash, which provides a faster and more cost-effective alternative. DeepSeek-V4 introduces structural innovations such as token-wise compression and sparse attention, significantly reducing computational overhead while maintaining accuracy. It is designed with strong agentic capabilities, enabling seamless integration with AI agents and multi-step workflows. The model excels in domains such as mathematics, coding, and scientific reasoning, outperforming many open-source alternatives. It also supports flexible reasoning modes, allowing users to optimize for speed or depth depending on the task. DeepSeek-V4 is compatible with popular APIs, making it easy to integrate into existing systems. Its open-source nature allows developers to customize and scale it according to their needs. The model is already being used in advanced coding agents and automation workflows. It delivers a strong balance of performance, efficiency, and scalability for real-world applications. Overall, DeepSeek-V4 represents a major advancement in accessible, high-performance AI technology.

Trinity Large Thinking is a cutting-edge open-source reasoning framework developed by Arcee AI, specifically designed for tackling complex, multi-step problems and workflows that involve autonomous agents requiring extensive planning and diverse tool utilization. With an impressive sparse Mixture-of-Experts architecture, it encompasses around 400 billion parameters, activating about 13 billion for each token, which not only boosts its operational efficiency but also fortifies its reasoning capabilities across various tasks, such as mathematical computations, code generation, and thorough analysis. A significant innovation of this model is its capacity for extended chain-of-thought reasoning, enabling it to generate intermediate "thinking traces" prior to presenting final results, which significantly enhances accuracy and dependability in intricate scenarios. Additionally, Trinity Large Thinking supports a generous context window of up to 262K tokens, which empowers it to effectively handle lengthy documents, maintain context during extended interactions, and operate smoothly within continuous agent loops. This exemplary design showcases a firm commitment to advancing the limits of automated reasoning systems, paving the way for more sophisticated applications in the future. As technology evolves, the potential for further enhancements in reasoning models like this one remains vast and exciting.

GPT-5.5 Pro represents a new class of AI designed to transform how work gets done across digital environments. It combines advanced reasoning, tool usage, and task execution capabilities to handle complex, multi-step workflows with minimal human intervention. The model excels in areas such as software engineering, data analysis, business operations, and scientific research, where it can plan tasks, gather information, test solutions, and refine outputs continuously. It supports creating applications, generating reports, building spreadsheets, and navigating software systems as part of a complete workflow. A key capability is its integration with workspace agents—custom AI agents that can be built once and deployed across teams to automate entire processes. These agents can run tasks on schedules, interact with tools like CRM systems, messaging platforms, and document editors, and keep workflows moving without constant supervision. Organizations can define permissions, approval checkpoints, and monitoring to maintain control over automated processes. GPT-5.5 Pro also enhances collaboration by enabling teams to standardize workflows and scale best practices across the organization. With enterprise-grade security and governance, it ensures safe deployment in complex environments. Its ability to persist through ambiguity and long tasks makes it highly effective for execution-heavy work. By reducing manual intervention and increasing speed, it allows teams to focus on higher-value activities. Ultimately, GPT-5.5 Pro enables businesses and professionals to operate at a significantly higher level of productivity and efficiency.

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.

Qwen3.6-Plus is a cutting-edge AI model developed by Alibaba Cloud, designed to enable real-world intelligent agents, advanced coding workflows, and multimodal reasoning. It represents a major evolution in the Qwen series, offering enhanced performance across coding, reasoning, and tool-based tasks. With a default 1 million token context window, the model can process extremely large inputs and maintain context across long interactions. It excels in agentic coding, supporting tasks such as debugging, terminal operations, and large-scale repository management. The model integrates reasoning, memory, and execution capabilities, allowing it to function as a highly autonomous and reliable AI agent. Qwen3.6-Plus also features strong multimodal capabilities, enabling it to analyze images, videos, documents, and UI elements for deeper understanding and action. It supports real-world applications such as workflow automation, visual reasoning, and interactive task execution. Developers can access the model via API and integrate it with tools like OpenClaw, Qwen Code, and other coding assistants. Features like preserved reasoning context improve performance in complex, multi-step tasks and reduce redundant processing. The model is optimized for enterprise use, offering stability, scalability, and high accuracy across diverse domains. It also supports multilingual environments, making it suitable for global applications. Overall, Qwen3.6-Plus provides a powerful foundation for building next-generation AI agents capable of perception, reasoning, and action.

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