List of the Top AI Models for ZooClaw in 2026

GPT-6 Astra is OpenAI’s advanced frontier model for computer use, coding, browsing, scientific research, cybersecurity, professional knowledge work, and long-running agentic tasks. It is designed to combine high-level reasoning with the ability to directly operate software and tools rather than only generating text responses. Astra can navigate websites, complete forms, update business systems, organize calendars, conduct research, analyze data, generate plots, test applications, and troubleshoot problems that appear on screen. For professional users, the model can create documents, spreadsheets, presentations, analyses, websites, and other artifacts while following existing templates, formatting requirements, and organizational styles. Its software engineering capabilities include codebase analysis, implementation, debugging, verification, browser testing, system configuration, and other terminal-based development workflows. In Codex, Astra can preserve notes across context windows and search earlier requirements, test results, messages, and tool outputs during lengthy development sessions. The model also combines scientific reasoning with computer use so researchers can work with specialized applications, inspect data, explore results, and assist with computational research processes. OpenAI reports substantial advances in Astra’s cybersecurity capabilities, while the production model applies safeguards to restrict higher-risk activities such as advanced exploit creation. Alignment improvements focus on interpreting user intent, respecting authorization boundaries, avoiding attempts to circumvent system restrictions, and communicating more accurately about what the model can and cannot do. GPT-6 Astra supports enterprise-oriented deployment features including eligible Zero Data Retention API configurations and is available through ChatGPT, the OpenAI API, Amazon Web Services, and Amazon Bedrock.

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-6 Luna is OpenAI’s efficiency-focused GPT-6 model for developers and users who need capable reasoning, coding, computer use, and agentic workflows at very low inference cost. It is positioned below GPT-6 Sol and GPT-6 Astra in the model family while bringing many of the GPT-6 generation’s improvements to applications that prioritize scale and affordability. The model supports configurable reasoning effort so developers can allocate additional computation to complex tasks while keeping simpler interactions fast and economical. GPT-6 Luna can power business automation across applications used for sales, marketing, finance, operations, customer support, and human resources. Its coding capabilities support work on real software repositories, including multi-step engineering tasks that require analysis, modification, testing, and iteration. Luna can also operate in computer-use environments, allowing agents to navigate graphical interfaces and complete extended workflows across software applications. OpenAI reports that GPT-6 Luna substantially improves factual reliability compared with GPT-5.6 Luna and can approach the capabilities of more expensive models on some tasks when used at higher reasoning levels. The model also benefits from GPT-6’s improved collaboration style, with clearer technical communication, less unnecessary jargon, and fewer low-value details. Enhanced prompt caching allows applications to reuse previously processed context at a discount while preserving cache reuse when reasoning effort or available tools change. These efficiency improvements make Luna suitable for high-volume agents, coding assistants, automated workflows, customer-facing applications, and other systems where per-request cost is important. GPT-6 Luna is available through the OpenAI API as gpt-6-luna, as well as through ChatGPT Work, Codex, and supported ChatGPT desktop experiences.

GPT-6 Sol is an advanced OpenAI model positioned between the cost-efficient GPT-6 Luna and the higher-capability GPT-6 Astra for demanding professional and agentic workloads. The model is designed for coding, knowledge work, business automation, computer use, research, and other tasks that require sustained reasoning across multiple steps. It inherits advances from the GPT-6 generation while emphasizing a balance of intelligence, speed, and operating cost for applications that need to run at scale. GPT-6 Sol supports multiple reasoning-effort levels so applications can spend more computation on difficult tasks and reduce effort for straightforward requests. In software development, it can handle complex real-codebase tasks, generate merge-ready changes, debug software, work through terminal workflows, and operate as part of coding agents. Its professional-work capabilities support multi-application processes spanning functions such as finance, operations, sales, marketing, customer support, and human resources. Computer-use abilities allow agents powered by GPT-6 Sol to interact with graphical interfaces and complete long-horizon workflows involving everyday and professional software. OpenAI has also improved the model’s factual reliability, communication style, and alignment compared with GPT-5.6 Sol, including lower rates of misleading claims in challenging coding evaluations. GPT-6 prompt caching provides higher cache-hit rates, supports changing reasoning effort or available tools without invalidating earlier cached context, and offers substantial discounts for cached input tokens. Developers can monitor caching behavior, configure prompt-cache breakpoints, and incorporate Sol into persistent agents that repeatedly reuse large amounts of context. GPT-6 Sol is accessible through ChatGPT Work, Codex, and the OpenAI API under the gpt-6-sol model identifier.

Kimi K3.1 Pro is a multimodal large language model developed by Moonshot AI to support advanced reasoning, autonomous AI agents, software development, 3D creation, and complex knowledge work. The model is based on a mixture-of-experts architecture containing approximately 2.8 trillion total parameters and 104 billion active parameters. Its one-million-token context window enables the processing of extensive documents, source code repositories, technical materials, and long-running task histories. Kimi K3.1 Pro builds on the Kimi K3 foundation with additional reinforcement learning and post-training optimization across programming, analytical reasoning, and agentic tasks. Its software engineering capabilities include code generation, debugging, terminal operations, automated testing, application development, and multistep problem-solving across existing repositories. The model can develop interactive websites, animated interfaces, browser-based 3D environments, and game projects using tools and frameworks such as Three.js, Unity, Unreal Engine, and Godot. Its multimodal reasoning capabilities allow it to interpret images, sketches, and visual references when generating technical designs or structured outputs. Three-dimensional modeling features include CAD script generation, parametric geometry creation, asset modification, and integration with tools such as Blender. Financial reasoning capabilities support complex spreadsheet models, financial statement analysis, investment research, cross-document verification, and evidence-based reporting. Kimi K3.1 Pro also supports tool-assisted workflows in which AI agents plan tasks, execute actions, evaluate results, and refine outputs across multiple steps. The model is available through Kimi's web and mobile applications, Kimi Work, Kimi Code, and the Kimi API, with a Highspeed variant providing faster inference and an open-weight release planned for November 4, 2026.

GPT-6.1 Sol is OpenAI's upgraded Sol model for developers and professionals who need advanced reasoning and agentic capabilities without the higher cost of GPT-6 Astra. It is designed for coding, professional knowledge work, computer use, scientific research, factual question answering, and multi-step business workflows. OpenAI describes GPT-6.1 Sol as approaching GPT-6 Astra's intelligence across several important workloads while charging one-fifth of Astra's standard input and output token prices. On DeepSWE v1.1, which evaluates long-horizon software engineering in real codebases, GPT-6.1 Sol matches GPT-6 Astra at approximately one-fifth of the cost and exceeds GPT-6 Sol's best score by 6.4 percentage points. Its professional-work capabilities include understanding complex PDFs containing tables, charts, diagrams, and fine-print details across fields such as finance, healthcare, and legal work. On AutomationBench, GPT-6.1 Sol improves on GPT-6 Sol by 4.8 percentage points at the same reasoning setting and scores 2.2 points above Opus 5.5 at medium reasoning effort. Computer-use performance also advances significantly, with GPT-6.1 Sol outperforming GPT-6 Sol by seven percentage points on the OSWorld 2.0 offline set at maximum reasoning effort and coming within 2.1 points of GPT-6 Astra. For scientific research, the model can work with code and terminal tools on workflows involving data analysis, simulations, model fitting, and theorem proving, more than doubling GPT-6 Sol's Terminal-Bench Science 0.1 score at maximum effort. OpenAI also reports improved factual accuracy, including a reduction in the factual-error rate from 11.4% with GPT-6 Sol to 7.7% with GPT-6.1 Sol at low reasoning effort on its deliberately difficult factuality evaluation.

Kimi K3 is Moonshot AI’s most advanced model, designed for high-end reasoning, software engineering, multimodal understanding, knowledge work, and agentic AI applications. The model has 2.8 trillion parameters and is built on Kimi Delta Attention, a hybrid linear attention mechanism created for long-context performance. It also uses Attention Residuals and supports a native context window of up to 1 million tokens. This makes Kimi K3 suitable for tasks involving large codebases, long research materials, enterprise documentation, multi-file analysis, legal documents, technical manuals, and complex workflows. Kimi K3 always has thinking mode enabled, with reasoning effort configured through the reasoning_effort field and maximum effort currently supported as the default. Developers can use the model through an OpenAI-compatible API, making it easier to integrate with existing SDKs, clients, and application infrastructure. The model supports streaming responses with separate reasoning and final-answer deltas, allowing applications to display reasoning progress and final content differently. Kimi K3 also supports strict structured output with JSON Schema, partial mode for continuing from a prefix, custom tool calling, required tool use, and dynamic tool loading through system messages. Its vision capabilities support image and video inputs through base64 or uploaded files, enabling analysis of visual content alongside text. Automatic context caching helps workflows that reuse long prefixes, such as large knowledge bases or persistent system context, without requiring developers to manage cache IDs manually. By combining frontier-scale parameters, long-context processing, visual input, structured outputs, tool orchestration, and developer-friendly API compatibility, Kimi K3 gives teams a strong foundation for advanced AI agents, coding assistants, research systems, enterprise automation, and multimodal applications.

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.

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.

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.

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.

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.

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.

DeepSeek-V4.1-Flash is an exceptionally efficient and versatile AI model designed for tackling complex tasks in programming, creativity, agentic functions, and spatial reasoning. Building upon the foundation laid by DeepSeek-V4-Flash, this iteration emphasizes swift output generation while maintaining strong performance on challenging tasks, achieving an impressive rate of over 400 tokens per second, with peak performance reaching around 427 tokens per second in testing environments. The model is adept at managing advanced programming assignments, creating immersive 3D worlds, producing voxel-based projects, and interpreting spatially complex scenes and simulations. Remarkable demonstrations highlight its adaptability through various environments, such as Minecraft-inspired landscapes, traditional Chinese gardens, racing circuits, dungeon explorations, and exploded camera perspectives, showcasing the fusion of coding abilities and spatial understanding. Its sophisticated features make it an excellent option for rapid prototyping, game development, 3D modeling, architectural design, academic inquiries, and a wide array of other technical or artistic projects where quick iteration is essential. Moreover, the model’s innovative functionalities enable it to seamlessly adjust to various project demands, significantly increasing its effectiveness across numerous fields and enhancing collaboration between creative and technical disciplines.

DeepSeek-V4.1-Flash-1017 is an open-weight artificial intelligence model developed by DeepSeek for multimodal understanding, advanced reasoning, autonomous agent workflows, and software engineering. Introduced on October 17, 2026, the release updates DeepSeek-V4.1-Flash with improvements in reasoning accuracy, coding capabilities, agent performance, and computational efficiency. The model builds on DeepSeek's mixture-of-experts architecture and supports native visual understanding alongside natural language processing. Its one-million-token context window enables applications to process extensive documents, large code repositories, and long sequences of interactions. Developers can configure reasoning effort and use the model for tasks involving mathematical reasoning, programming, information analysis, and complex problem-solving. Software engineering capabilities include code generation, debugging, repository analysis, automated testing, and implementation of features across existing projects. DeepSeek also trained the model using reinforcement learning with self-feedback to improve its ability to create complete projects, including web applications, interactive games, and 3D environments. Its agentic capabilities support multi-turn conversations, external tool calling, task planning, and integration with automated development environments. DeepSeek reports improvements over the previous version on benchmarks including Terminal-Bench, DeepSWE, ProgramBench, Automation-Bench, and Agents' Last Exam. The model includes reference prompt encoding tools and supports deployment through Hugging Face Transformers, vLLM, SGLang, and compatible inference infrastructure. DeepSeek-V4.1-Flash-1017 is available through the DeepSeek API under the deepseek-flash model identifier and through downloadable model weights licensed under the MIT License.

Sora is a cutting-edge AI system designed to convert textual descriptions into dynamic and realistic video sequences. Our primary objective is to enhance AI's understanding of the intricacies of the physical world, aiming to create tools that empower individuals to address challenges requiring real-world interaction. Introducing Sora, our groundbreaking text-to-video model, capable of generating videos up to sixty seconds in length while maintaining exceptional visual quality and adhering closely to user specifications. This model is proficient in constructing complex scenes populated with multiple characters, diverse movements, and meticulous details about both the focal point and the surrounding environment. Moreover, Sora not only interprets the specific requests outlined in the prompt but also grasps the real-world contexts that underpin these elements, resulting in a more genuine and relatable depiction of various scenarios. As we continue to refine Sora, we look forward to exploring its potential applications across various industries and creative fields.

Nano Banana Pro represents Google DeepMind’s most sophisticated step forward in visual creation, offering a major upgrade in realism, reasoning, and creative refinement compared to the original Nano Banana. Built on the Gemini 3 Pro foundation, it leverages advanced world knowledge to produce context-aware visuals that feel accurate, purposeful, and highly customizable. The model can interpret handwritten notes, transform rough sketches into polished diagrams, convert data into rich infographics, and even generate complex scene layouts grounded in real-time Search results. One of its most powerful features is its dramatically improved text rendering—allowing for paragraphs, stylized fonts, multilingual scripts, and nuanced typography directly inside generated images. Nano Banana Pro also supports deeply controlled multi-image compositions, blending up to 14 inputs while keeping the appearance of up to five people consistent across varying angles, lighting conditions, and poses. This makes it ideal for producing editorial shoots, cinematic scenes, product designs, fashion campaigns, or lifestyle imagery that requires continuity. Its precision editing tools let users manipulate light direction, adjust depth of field, change aspect ratios, and fine-tune specific regions of an image without damaging the overall composition. With support for high-resolution 2K and 4K output, results are suitable for print, advertising, and professional creative production. The model is rolling out across multiple Google platforms—from Gemini apps and Workspace to Ads, Vertex AI, and Google AI Studio—giving consumers, creatives, developers, and enterprises powerful new ways to generate, customize, and scale visual assets. Combined with SynthID transparency tools, Nano Banana Pro offers cutting-edge creative power while maintaining Google’s commitment to safety and verification.

Claude Sonnet 4.6 is the latest evolution in Anthropic’s Sonnet model family, offering major advancements in coding, reasoning, computer interaction, and knowledge-intensive workflows. Designed as a full upgrade rather than an incremental update, it improves consistency, instruction following, and multi-step task completion across a broad range of professional applications. The model introduces a 1 million token context window in beta, enabling users to analyze entire codebases, long contracts, research archives, or complex planning documents in one cohesive session. Developers with early access reported a strong preference for Sonnet 4.6 over Sonnet 4.5 and even favored it over Opus 4.5 in many real-world coding tasks. Users highlighted its reduced overengineering tendencies, improved follow-through, and lower incidence of hallucinations during extended sessions. A major enhancement is its improved computer-use capability, allowing it to operate traditional software environments by interacting with graphical interfaces much like a human user. On benchmarks such as OSWorld, Sonnet models have shown steady gains in handling browser navigation, spreadsheets, and development tools. The model also demonstrates strategic reasoning improvements in long-horizon simulations, such as Vending-Bench Arena, where it optimizes early investments before pivoting toward profitability. On the Claude Developer Platform, Sonnet 4.6 supports adaptive thinking, extended thinking, and context compaction to maximize usable context length. API enhancements now include automated search filtering, code execution, memory, and advanced tool use capabilities for higher-quality outputs. Pricing remains consistent with Sonnet 4.5, making Opus-level performance more accessible to a broader user base. Available across Claude.ai, Cowork, Claude Code, the API, and major cloud platforms, Sonnet 4.6 becomes the new default model for Free and Pro users.

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.

Kimi K2.5 is an advanced multimodal AI model engineered for high-performance reasoning, coding, and visual intelligence tasks. It natively supports both text and visual inputs, allowing applications to analyze images and videos alongside natural language prompts. The model achieves open-source state-of-the-art results across agent workflows, software engineering, and general-purpose intelligence tasks. With a massive 256K token context window, Kimi K2.5 can process large documents, extended conversations, and complex codebases in a single request. Its long-thinking capabilities enable multi-step reasoning, tool usage, and precise problem solving for advanced use cases. Kimi K2.5 integrates smoothly with existing systems thanks to full compatibility with the OpenAI API and SDKs. Developers can leverage features like streaming responses, partial mode, JSON output, and file-based Q&A. The platform supports image and video understanding with clear best practices for resolution, formats, and token usage. Flexible deployment options allow developers to choose between thinking and non-thinking modes based on performance needs. Transparent pricing and detailed token estimation tools help teams manage costs effectively. Kimi K2.5 is designed for building intelligent agents, developer tools, and multimodal applications at scale. Overall, it represents a major step forward in practical, production-ready multimodal AI.

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.

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.

Qwen3.5 is an advanced open-weight multimodal AI system built to serve as the foundation for native digital agents capable of reasoning across text, images, and video. The primary release, Qwen3.5-397B-A17B, introduces a hybrid architecture that combines Gated DeltaNet linear attention with a sparse mixture-of-experts design, activating just 17 billion parameters per inference pass while maintaining a total parameter count of 397 billion. This selective activation dramatically improves decoding throughput and cost efficiency without sacrificing benchmark-level performance. Qwen3.5 demonstrates strong results across knowledge, multilingual reasoning, coding, STEM tasks, search agents, visual question answering, document understanding, and spatial intelligence benchmarks. The hosted Qwen3.5-Plus variant offers a default one-million-token context window and integrated tool usage such as web search and code interpretation for adaptive problem-solving. Expanded multilingual support now covers 201 languages and dialects, backed by a 250k vocabulary that enhances encoding and decoding efficiency across global use cases. The model is natively multimodal, using early fusion techniques and large-scale visual-text pretraining to outperform prior Qwen-VL systems in scientific reasoning and video analysis. Infrastructure innovations such as heterogeneous parallel training, FP8 precision pipelines, and disaggregated reinforcement learning frameworks enable near-text baseline throughput even with mixed multimodal inputs. Extensive reinforcement learning across diverse and generalized environments improves long-horizon planning, multi-turn interactions, and tool-augmented workflows. Designed for developers, researchers, and enterprises, Qwen3.5 supports scalable deployment through Alibaba Cloud Model Studio while paving the way toward persistent, economically aware, autonomous AI agents.

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.

Holo4 is a family of agentic AI models developed by H Company for computer use and multi-step automation across desktop, web, mobile, terminal, MCP, and API environments. The series consists of Holo4 27B, a dense 27-billion-parameter model, and Holo4 35B-A3B, a 35-billion-parameter Mixture-of-Experts model with 3 billion active parameters. Rather than specializing exclusively in graphical interfaces or tool calling, Holo4 can click and type on screens, write and run its own code, and invoke MCP or API tools as different stages of a workflow require. The same model can therefore move between desktop applications, websites, Android applications, code sandboxes, and business APIs without switching to a separate model for each interface. H Company's Agentic Task Factory generated approximately 10,000 tasks across web applications, MCP servers, desktop software, and hybrid environments to support model development and evaluation. Holo4 underwent supervised fine-tuning on 127 billion tokens, with roughly three-quarters of that training data consisting of successful agentic trajectories covering desktop, web, MCP/API, and mobile tasks. Two reinforcement-learning experts were subsequently trained for desktop/web workflows and terminal/MCP/API workflows before being merged into the final generalist model. In H Company's evaluations, Holo4 27B scored 85.2% on OSWorld, 61.7% on OSWorld 2.0, 45.4% on AutomationBench, and 85.1% on AndroidWorld, although the company notes that reference-model results can use different harnesses and effort levels. Holo4 27B supports a 256K context window and is priced through the H Models API at $0.40 per million input tokens, $0.04 per million cached input tokens, and $3.00 per million output tokens. Holo4 35B-A3B also supports 256K context and is priced at $0.30 per million input tokens, $0.03 per million cached input tokens, and $2.00 per million output tokens.