List of the Top AI Models for OpenClaw in 2026 - Page 6

Qwen3.5-Plus is a next-generation multimodal large language model built for scalable, enterprise-grade reasoning and agentic applications. It combines linear attention mechanisms with a sparse mixture-of-experts architecture to maximize inference efficiency while maintaining performance comparable to leading frontier models. The system supports text, image, and video inputs, generating high-quality text outputs suited for analysis, synthesis, and tool-augmented workflows. With a 1 million token context window and support for up to 64K output tokens, Qwen3.5-Plus enables deep, long-form reasoning across extensive documents and datasets. Its optional deep thinking mode allows for expanded chain-of-thought reasoning up to 80K tokens, making it ideal for complex analytical and multi-step problem-solving tasks. Developers can integrate structured outputs, function calling, prefix continuation, batch processing, and explicit caching to optimize both performance and cost efficiency. Built-in tool support through the Responses API includes web search, web extraction, image search, and code interpretation for dynamic multi-agent systems. High throughput limits and OpenAI-compatible API endpoints make deployment straightforward across global applications. With transparent token-based pricing and enterprise-level monitoring, Qwen3.5-Plus provides a powerful foundation for building intelligent assistants, multimodal analyzers, and scalable AI services.

The GLM-5V-Turbo stands as a cutting-edge multimodal coding foundation model, expertly designed for scenarios necessitating visual inputs, proficient in interpreting various formats including images, videos, texts, and files to produce text-based results. This model is particularly optimized for agent workflows, enabling it to grasp environments effectively, devise suitable actions, and execute tasks, while also maintaining compatibility with agent frameworks such as Claude Code and OpenClaw. Notably, it excels in managing long-context interactions, offering an impressive context capacity of 200K tokens alongside an output limit of up to 128K tokens, making it exceptionally suited for complex, long-duration projects. Moreover, it presents an array of thinking modes tailored for different situations, demonstrates strong visual understanding of both images and videos, and streams outputs in real-time to improve user interaction. It also incorporates advanced function-calling capabilities that allow seamless integration of external tools, with its context caching feature significantly enhancing performance during extended dialogues. In real-world applications, the model is capable of skillfully converting design mockups into operational frontend projects, highlighting its adaptability and depth in practical coding environments. Furthermore, this adaptability empowers users to approach a diverse array of intricate tasks with assurance and effectiveness, greatly enhancing their productivity.

Qwen3.6 is a next-generation large language model developed by Alibaba, designed to deliver advanced reasoning, coding, and multimodal capabilities. It builds on the Qwen3.5 series with a strong emphasis on stability, efficiency, and real-world usability. The model supports multimodal inputs, enabling it to process text, images, and video for more complex analysis and decision-making. One of its key strengths is agentic AI, allowing it to perform multi-step tasks and operate more autonomously in workflows. Qwen3.6 is particularly optimized for coding, capable of handling complex engineering tasks at a repository level rather than just individual functions. It uses a mixture-of-experts architecture, with billions of parameters but only a subset activated during each inference, improving efficiency. The model is available in both open-weight and proprietary versions, giving developers flexibility in deployment and customization. It can be integrated into enterprise systems, APIs, and cloud environments for production use. Qwen3.6 also offers strong multimodal reasoning, enabling it to analyze documents, visuals, and structured data together. It is designed to support a wide range of applications, from software development to data analysis and automation. The model includes enhancements in performance, scalability, and usability compared to earlier versions. It reflects a broader shift toward agent-based AI systems that can execute tasks rather than just provide responses. Overall, Qwen3.6 represents a powerful and versatile AI model for modern enterprise and developer use cases.

Ling 2.6 signifies a series of large language models that have been independently developed and made open-source by Ant Group, leveraging a Mixture of Experts (MoE) architecture to optimize inference efficiency, manage long context modeling, improve training methodologies, and facilitate collaborative reasoning among AI agents. Through the implementation of this MoE architecture, Ling adeptly channels each token to interact solely with the most relevant expert subnetworks, which markedly decreases computational demands while maintaining the model's extensive functional capabilities. Notably, this series achieves significant advancements in long-sequence modeling, as demonstrated by Ling-2.6-1T, which supports a native context window of up to 1 million tokens and provides a 256K context window via its official API; further, Ling-2.6-flash is designed with a native 256K context window, allowing it to process approximately 200,000 characters in large inputs. These models are designed with great precision to ensure the reliable retrieval of information over long distances without any noticeable degradation in quality, regardless of the position of the data within the context. This cutting-edge methodology in long-context processing establishes a new standard for both efficiency and reliability in the performance of language models. The implications of such advancements could revolutionize how AI systems interact with extensive data sets, enabling more sophisticated applications in various fields.

The Ling 2.6 Flash is the latest and most cost-effective member of the Ling series, featuring a Mixture of Experts architecture that boasts 104 billion parameters, with 7.4 billion of these actively utilized. Designed to achieve an optimal balance between inference speed and resource costs, this model excels in various applications that require robust reasoning, high throughput, and efficient deployment. Its MoE framework allows the model to engage only the most relevant expert subnetworks for each token, thereby significantly lowering the computational burden while still leveraging the model's extensive capacity. With a native context window of 256K, Ling 2.6 Flash can process approximately 200,000 characters of lengthy input, effectively retrieving essential long-range information no matter where it appears in the context. Additionally, its benchmark performance competes with or even surpasses that of dense models with 40 billion parameters, showcasing its strong position within the AI landscape. This combination of efficiency and high performance positions the Ling 2.6 Flash as a compelling choice for developers who desire sophisticated capabilities without placing undue strain on their resources. As technology continues to evolve, the Ling 2.6 Flash stands out as a prime candidate for future innovations in artificial intelligence.

Ring represents an advanced trillion-parameter model developed by Ant Group, designed to optimize real-world Agent workflows. Utilizing a Mixture of Experts architecture akin to that of Ling, it activates around 63 billion parameters for each inference and is adept at performing tasks such as coding agents, using tools, collaborating with diverse instruments, software engineering, conducting research, and managing long-term projects. Rather than simply aiming for more intelligent outcomes, Ring focuses on ensuring the dependable execution of complex tasks while keeping costs manageable, thereby achieving a harmonious balance of quality, speed, and efficiency in production environments. The most recent version, Ring-2.6-1T, features a customizable Reasoning Effort mechanism with high and xhigh reasoning intensity levels that adjust the reasoning budget based on task complexity. The high mode is specifically designed for frequent Agent workflows, leading to reduced token costs and expedited multi-step processes, while also promoting multi-turn conversations, tool collaboration, and task breakdown. This evolution significantly boosts the operational capabilities of agents, making them more effective across various domains and enhancing their overall performance in dynamic environments. Consequently, Ring stands as a pivotal advancement in the realm of intelligent agents, showcasing its versatility and reliability.

Ling 3.0 Flash is an evolved language model specifically designed for long-term agent tasks, featuring rapid response capabilities, low activation levels, and reliable tool utilization. With a Mixture-of-Experts architecture, it encompasses an impressive total of 124 billion parameters, activating 5.1 billion parameters for each token, which optimizes its performance while ensuring efficient inference. The model showcases a remarkable native context window of 256K tokens, expandable to a maximum of 1 million tokens, facilitating effective information retrieval from extensive contexts. In comparison to its earlier version, the original Flash model, Ling 3.0 Flash offers superior stability for extended operations, enhances tool-calling accuracy, adheres more closely to instructions, and shows improved compatibility with agent harnesses and coding tasks. Furthermore, its advanced spatial awareness capabilities allow it to construct grids of physical scenes and assess relative positions with precision, while its hybrid reasoning abilities increase success rates across various task complexities. This model not only represents a substantial advancement in language modeling technology but also ensures users can attain exceptional performance across a wide array of applications, thus broadening its potential use cases. Overall, Ling 3.0 Flash stands out as a groundbreaking development in the field, likely to influence future applications significantly.

Qwen3.8-Flash-Next is a pioneering open-weight multimodal Mixture-of-Experts architecture that offers an initial look at the design meant for its successor, Qwen4. This model has been expertly crafted to enhance various aspects such as attention mechanisms, residual pathways, embeddings, and optimization strategies, thereby increasing its overall functionality, enhancing computational efficiency, expanding its model capacity, and ensuring stability during training. Its unique hybrid structure combines Gated DeltaNet, which effectively condenses historical information, with Qwen Sparse Attention, facilitating the selection of meaningful context on a micro-block scale to reduce both attention and indexing expenses for lengthy sequences. The Gated Residual feature enhances the residual pathway by incorporating four streams, which helps in dynamically regulating the information flow across different layers. Moreover, the N-gram Embedding cleverly merges large-scale local-pattern memory with minimal computational overhead for each token, with the capability to transfer to host memory for added efficiency. The entire model is built around a main network comprising 125 billion parameters, supplemented by an additional 51 billion parameters specifically for N-gram embeddings, activating only 6 billion parameters for each token processed. This advanced framework underscores the continuous evolution in machine learning architectures, laying the groundwork for exciting future innovations, and it exemplifies the increasing sophistication and potential of multimodal models in various applications.

Step 5 Preview epitomizes the apex of StepFun’s offerings for agentic tasks, specifically designed for practical applications in the realms of software engineering and professional knowledge, with particular excellence in financial settings. This model is adept at processing text, images, and videos, featuring an impressive 1M-token context window that suits tasks requiring extensive data, tool utilization, and continuous advancement toward specific objectives. It can effectively analyze extensive documents, amalgamate information from diverse sources, and leverage conversation threads for efficient cross-document queries and research organization. In the field of programming and software development, it showcases proficiency in a range of programming languages, assisting with debugging, code adjustments, verification tasks, and test generation. Moreover, its sophisticated multi-step agent capabilities allow applications to leverage tools for information retrieval, document analysis, detailed research, and the development of analytical reports. The model's multimodal understanding enables it to integrate images, videos, and text, facilitating tasks like chart analysis and responding to queries based on screenshots. This extensive suite of capabilities not only enhances productivity but also solidifies Step 5 Preview as an essential tool for professionals across a multitude of industries, ensuring they remain at the forefront of their respective fields.

Black Forest Labs has unveiled the FLUX1.1 Pro, an innovative model in the realm of AI-powered image creation that sets a new benchmark for both speed and quality. This latest iteration surpasses its predecessor, the FLUX.1 Pro, by achieving speeds that are six times faster while also enhancing image fidelity, prompt accuracy, and creative diversity. Among its standout features is the ability to render ultra-high-resolution images up to 4K, along with a Raw Mode that enables the production of more realistic and organic visuals. Users can access FLUX1.1 Pro via the BFL API, and it is seamlessly integrated with platforms like Replicate and Freepik, making it the top choice for professionals seeking advanced and scalable AI-generated imagery. Moreover, its cutting-edge capabilities ensure it serves as a versatile asset for a wide range of creative projects, further expanding its appeal across different industries. This model not only reflects technological advancement but also caters to the evolving needs of creators in today's digital landscape.

FLUX.1 Kontext represents a groundbreaking suite of generative flow matching models developed by Black Forest Labs, designed to empower users in both the generation and modification of images using text and visual prompts. This cutting-edge multimodal framework simplifies in-context image creation, enabling the seamless extraction and transformation of visual concepts to produce harmonious results. Unlike traditional text-to-image models, FLUX.1 Kontext uniquely integrates immediate text-based image editing alongside text-to-image generation, featuring capabilities such as maintaining character consistency, comprehending contextual elements, and facilitating localized modifications. Users can execute targeted adjustments on specific elements of an image while preserving the integrity of the overall design, retain unique styles derived from reference images, and iteratively refine their works with minimal latency. Additionally, this level of adaptability fosters new creative possibilities, encouraging artists to delve deeper into their visual narratives and innovate in their artistic expressions. Ultimately, FLUX.1 Kontext not only enhances the creative process but also redefines the boundaries of artistic collaboration and experimentation.

Z.ai has launched its newest flagship model, GLM-4.5, which features an astounding total of 355 billion parameters (with 32 billion actively utilized) and is accompanied by the GLM-4.5-Air variant, which includes 106 billion parameters (12 billion active) tailored for advanced reasoning, coding, and agent-like functionalities within a unified framework. This innovative model is capable of toggling between a "thinking" mode, ideal for complex, multi-step reasoning and tool utilization, and a "non-thinking" mode that allows for quick responses, supporting a context length of up to 128K tokens and enabling native function calls. Available via the Z.ai chat platform and API, and with open weights on sites like HuggingFace and ModelScope, GLM-4.5 excels at handling diverse inputs for various tasks, including general problem solving, common-sense reasoning, coding from scratch or enhancing existing frameworks, and orchestrating extensive workflows such as web browsing and slide creation. The underlying architecture employs a Mixture-of-Experts design that incorporates loss-free balance routing, grouped-query attention mechanisms, and an MTP layer to support speculative decoding, ensuring it meets enterprise-level performance expectations while being versatile enough for a wide array of applications. Consequently, GLM-4.5 sets a remarkable standard for AI capabilities, pushing the boundaries of technology across multiple fields and industries. This advancement not only enhances user experience but also drives innovation in artificial intelligence solutions.

Nano Banana is the go-to model for fast, enjoyable image creation inside Gemini, giving users a simple yet powerful way to experiment visually. It shines when you want to remix a photo quickly, add something whimsical, or transform an ordinary picture into something imaginative with a single prompt. The model is especially good at maintaining facial and character consistency, making edits feel natural even when placed in stylized or fantastical scenes. Users can combine multiple photos into a single image, allowing for fun mashups, creative collages, or side-by-side portrait merges. Nano Banana also supports localized tweaks, like changing out a background, adjusting a small detail, or enhancing a specific part of your image. Its fast generation makes it ideal for playful experimentation—trying new hairstyles, turning photos into figurines, or recreating nostalgic photo styles. With each update, creators can explore more themes and visual ideas without needing specialized software. Nano Banana’s simplicity keeps the focus on creativity rather than technical setup. Whether you're making mall-style portraits, retro edits, or quirky social content, the process is fast, friendly, and intuitive. This model makes image creation accessible to everyone looking for quick, fun results.

Qwen3-Omni represents a cutting-edge multilingual omni-modal foundation model adept at processing text, images, audio, and video, and it delivers real-time responses in both written and spoken forms. It features a distinctive Thinker-Talker architecture paired with a Mixture-of-Experts (MoE) framework, employing an initial text-focused pretraining phase followed by a mixed multimodal training approach, which guarantees superior performance across all media types while maintaining high fidelity in both text and images. This advanced model supports an impressive array of 119 text languages, alongside 19 for speech input and 10 for speech output. Exhibiting remarkable capabilities, it achieves top-tier performance across 36 benchmarks in audio and audio-visual tasks, claiming open-source SOTA on 32 benchmarks and overall SOTA on 22, thus competing effectively with notable closed-source alternatives like Gemini-2.5 Pro and GPT-4o. To optimize efficiency and minimize latency in audio and video delivery, the Talker component employs a multi-codebook strategy for predicting discrete speech codecs, which streamlines the process compared to traditional, bulkier diffusion techniques. Furthermore, its remarkable versatility allows it to adapt seamlessly to a wide range of applications, making it a valuable tool in various fields. Ultimately, this model is paving the way for the future of multimodal interaction.

Claude Sonnet 4.5 marks a significant milestone in Anthropic's development of artificial intelligence, designed to excel in intricate coding environments, multifaceted workflows, and demanding computational challenges while emphasizing safety and alignment. This model establishes new standards, showcasing exceptional performance on the SWE-bench Verified benchmark for software engineering and achieving remarkable results in the OSWorld benchmark for computer usage; it is particularly noteworthy for its ability to sustain focus for over 30 hours on complex, multi-step tasks. With advancements in tool management, memory, and context interpretation, Claude Sonnet 4.5 enhances its reasoning capabilities, allowing it to better understand diverse domains such as finance, law, and STEM, along with a nuanced comprehension of coding complexities. It features context editing and memory management tools that support extended conversations or collaborative efforts among multiple agents, while also facilitating code execution and file creation within Claude applications. Operating at AI Safety Level 3 (ASL-3), this model is equipped with classifiers designed to prevent interactions involving dangerous content, alongside safeguards against prompt injection, thereby enhancing overall security during use. Ultimately, Sonnet 4.5 represents a transformative advancement in intelligent automation, poised to redefine user interactions with AI technologies and broaden the horizons of what is achievable with artificial intelligence. This evolution not only streamlines complex task management but also fosters a more intuitive relationship between technology and its users.

Veo 3.1 Fast is the latest evolution in Google’s generative-video suite, designed to empower creators, studios, and developers with unprecedented control and speed. Available through the Gemini API, this model transforms text prompts and static visuals into coherent, cinematic sequences complete with synchronized sound and fluid camera motion. It expands the creative toolkit with three core innovations: “Ingredients to Video” for reference-guided consistency, “Scene Extension” for generating minute-long clips with continuous audio, and “First and Last Frame” transitions for professional-grade edits. Unlike previous models, Veo 3.1 Fast generates native audio—capturing speech, ambient noise, and sound effects directly from the prompt—making post-production nearly effortless. The model’s enhanced image-to-video pipeline ensures improved visual fidelity, stronger prompt alignment, and smooth narrative pacing. Integrated natively with Google AI Studio and Gemini Enterprise Agent Platform, Veo 3.1 Fast fits seamlessly into existing workflows for developers building AI-powered creative tools. Early adopters like Promise Studios and Latitude are leveraging it to accelerate generative storyboarding, pre-visualization, and narrative world-building. Its architecture also supports secure AI integration via the Model Context Protocol, maintaining data privacy and reliability. With near real-time generation speed, Veo 3.1 Fast allows creators to iterate, refine, and publish content faster than ever before. It’s a milestone in AI media creation—fusing artistry, automation, and performance into one cohesive system.

Claude Opus 4.5 represents a major leap in Anthropic’s model development, delivering breakthrough performance across coding, research, mathematics, reasoning, and agentic tasks. The model consistently surpasses competitors on SWE-bench Verified, SWE-bench Multilingual, Aider Polyglot, BrowseComp-Plus, and other cutting-edge evaluations, demonstrating mastery across multiple programming languages and multi-turn, real-world workflows. Early users were struck by its ability to handle subtle trade-offs, interpret ambiguous instructions, and produce creative solutions—such as navigating airline booking rules by reasoning through policy loopholes. Alongside capability gains, Opus 4.5 is Anthropic’s safest and most robustly aligned model, showing industry-leading resistance to strong prompt-injection attacks and lower rates of concerning behavior. Developers benefit from major upgrades to the Claude API, including effort controls that balance speed versus capability, improved context efficiency, and longer-running agentic processes with richer memory. The platform also strengthens multi-agent coordination, enabling Opus 4.5 to manage subagents for complex, multi-step research and engineering tasks. Claude Code receives new enhancements like Plan Mode improvements, parallel local and remote sessions, and better GitHub research automation. Consumer apps gain better context handling, expanded Chrome integration, and broader access to Claude for Excel. Enterprise and premium users see increased usage limits and more flexible access to Opus-level performance. Altogether, Claude Opus 4.5 showcases what the next generation of AI can accomplish—faster work, deeper reasoning, safer operation, and richer support for modern development and productivity workflows.

GPT-5.2 ushers in a significant leap forward for the GPT-5 ecosystem, redefining how the system reasons, communicates, and interprets human intent. Built on an upgraded architecture, this version refines every major cognitive dimension—from nuance detection to multi-step problem solving. A suite of enhanced variants works behind the scenes, each specialized to deliver more accuracy, coherence, and depth. GPT-5.2 Instant is engineered for speed and reliability, offering ultra-fast responses that remain highly aligned with user instructions even in complex contexts. GPT-5.2 Thinking extends the platform’s reasoning capacity, enabling more deliberate, structured, and transparent logic throughout long or sophisticated tasks. Automatic routing ensures users never need to choose a model themselves—the system selects the ideal variant based on the nature of the query. These upgrades make GPT-5.2 more adaptive, more stable, and more capable of handling nuanced, multi-intent prompts. Conversations feel more natural, with improved emotional tone matching, smoother transitions, and higher fidelity to user intent. The model also prioritizes clarity, reducing ambiguity while maintaining conversational warmth. Altogether, GPT-5.2 delivers a more intelligent, humanlike, and contextually aware AI experience for users across all domains.

GPT-5.2-Codex is OpenAI’s most capable agentic coding model, engineered for professional software engineering and cybersecurity use cases. It builds on the strengths of GPT-5.2 while introducing optimizations for long-running coding sessions. The model excels at maintaining context across extended workflows using native context compaction. GPT-5.2-Codex performs reliably in large repositories and complex project structures. It achieves state-of-the-art results on SWE-Bench Pro and Terminal-Bench 2.0, reflecting strong real-world coding performance. Native Windows support improves reliability for cross-platform development. Enhanced vision capabilities allow the model to interpret design mocks, diagrams, and screenshots. GPT-5.2-Codex supports iterative development even when plans change or attempts fail. The model also shows substantial gains in defensive cybersecurity tasks. It can assist with vulnerability discovery and secure software development workflows. Additional safeguards are built in to address dual-use risks. GPT-5.2-Codex advances the frontier of agentic software engineering.

Nano Banana 2, officially known as Gemini 3.1 Flash Image, is Google DeepMind’s next-generation image generation model that combines Pro-level intelligence with ultra-fast performance. It integrates the advanced reasoning and world knowledge previously available only in Nano Banana Pro with the speed of Gemini Flash. The model draws on real-time web search data to enhance subject accuracy and contextual rendering. This enables users to create infographics, diagrams, marketing visuals, and data-driven imagery with greater factual grounding. Precision text rendering and multilingual translation capabilities allow for clean, legible designs across global markets. Improved instruction following ensures detailed prompts are executed faithfully, even in complex or multi-step creative tasks. Nano Banana 2 maintains subject consistency for up to five characters and numerous objects within a single project, supporting narrative and storyboard creation. It delivers production-ready assets with customizable aspect ratios and resolutions ranging from standard formats to 4K. Enhanced visual fidelity provides richer textures, improved lighting, and sharper details without sacrificing speed. The model is integrated across Google products, including the Gemini app, Search AI Mode, AI Studio, Vertex AI, Flow, and Ads. It also incorporates robust provenance tools such as SynthID and C2PA Content Credentials to support responsible AI transparency. By uniting intelligence, speed, quality, and accountability, Nano Banana 2 sets a new standard for accessible, high-performance image generation.

Kling 2.6 is an AI-powered video generation model designed to deliver fully synchronized audio-visual storytelling. It creates visuals, voiceovers, sound effects, and ambient audio in a single generation process. This approach removes the friction of manual audio layering and post-production editing. Kling 2.6 supports both text-based and image-based inputs, allowing creators to bring ideas or static visuals to life instantly. Native Audio technology aligns dialogue, sound effects, and background ambience with visual timing and emotional tone. The model supports narration, multi-character dialogue, singing, rap, environmental sounds, and mixed audio scenes. Voice Control enables consistent character voices across videos and scenes. Kling 2.6 is suitable for content creation ranging from ads and social videos to storytelling and music performances. Adjustable parameters allow creators to control duration, aspect ratio, and output variations. The system emphasizes semantic understanding to better interpret creative intent. Kling 2.6 bridges the gap between sound and visuals in AI video generation. It delivers immersive results without requiring professional editing skills.

GPT-5.3-Codex represents a major leap in agentic AI for software and knowledge work. It is designed to reason, build, and execute tasks across an entire computer-based workflow. The model combines the strongest coding performance of the Codex line with professional reasoning capabilities. GPT-5.3-Codex can handle long-running projects involving tools, terminals, and research. Users can interact with it continuously, guiding decisions as work progresses. It excels in real-world software engineering, frontend development, and infrastructure tasks. The model also supports non-coding work such as documentation, data analysis, presentations, and planning. Its improved intent understanding produces more complete and polished outputs by default. GPT-5.3-Codex was used internally to help train and deploy itself, accelerating its own development. It demonstrates strong performance across benchmarks measuring agentic and real-world skills. Advanced security safeguards support responsible deployment in sensitive domains. GPT-5.3-Codex moves Codex closer to a general-purpose digital collaborator.

Kling 3.0 is a powerful AI-driven video generation model built to deliver realistic, cinematic visuals from simple text or image prompts. It produces smoother motion and sharper detail, creating scenes that feel natural and immersive. Advanced physics modeling ensures believable interactions and lifelike movement within generated videos. Kling 3.0 maintains strong character consistency, preserving facial features, expressions, and identities across sequences. The model’s enhanced prompt understanding allows creators to design complex narratives with accurate camera motion and transitions. High-resolution output support makes the videos suitable for commercial and professional distribution. Faster rendering speeds reduce production bottlenecks and accelerate creative workflows. Kling 3.0 lowers the barrier to high-quality video creation by eliminating traditional filming requirements. It empowers creators to experiment freely with visual storytelling concepts. The platform is adaptable for marketing, entertainment, and digital media production. Teams can iterate quickly without sacrificing visual quality. Kling 3.0 delivers cinematic results with efficiency, flexibility, and creative control.

GPT-5.3-Codex-Spark is a specialized, ultra-fast coding model designed to enable real-time collaboration within the Codex platform. As a streamlined variant of GPT-5.3-Codex, it prioritizes latency-sensitive workflows where immediate responsiveness is critical. When deployed on Cerebras’ Wafer Scale Engine 3 hardware, Codex-Spark delivers more than 1000 tokens per second, dramatically accelerating interactive development sessions. The model supports a 128k context window, allowing developers to maintain broad project awareness while iterating quickly. It is optimized for making minimal, precise edits and refining logic or interfaces without automatically executing additional steps unless instructed. OpenAI implemented extensive infrastructure upgrades—including persistent WebSocket connections and inference stack rewrites—to reduce time-to-first-token by 50% and cut client-server overhead by up to 80%. On software engineering benchmarks such as SWE-Bench Pro and Terminal-Bench 2.0, Codex-Spark demonstrates strong capability while completing tasks in a fraction of the time required by larger models. During the research preview, usage is governed by separate rate limits and may be queued during peak demand. Codex-Spark is available to ChatGPT Pro users through the Codex app, CLI, and VS Code extension, with API access for select design partners. The model incorporates the same safety and preparedness evaluations as OpenAI’s mainline systems. This release signals a shift toward dual-mode coding systems that combine rapid interactive loops with delegated long-running tasks. By tightening the iteration cycle between idea and execution, GPT-5.3-Codex-Spark expands what developers can build in real time.

Seed2.0 Pro is a production-grade, general-purpose AI agent built to tackle sophisticated real-world challenges at scale. It is specifically optimized for long-chain reasoning, enabling it to manage complex, multi-stage instructions without sacrificing accuracy or stability. As the most advanced model in the Seed 2.0 lineup, it delivers comprehensive improvements in multimodal understanding, spanning text, images, motion, and structured data. The model consistently achieves leading results across benchmarks in mathematics, coding competitions, scientific reasoning, visual puzzles, and document comprehension. Its visual intelligence allows it to analyze intricate charts, interpret spatial relationships, and recreate complete web interfaces from a single image while generating executable front-end code. Seed2.0 Pro also supports interactive and dynamic applications, including AI-driven coaching systems and advanced real-time visual analysis. In professional settings, it can automate CAD modeling workflows, extract geometric properties, and assist with scientific algorithm refinement. The system demonstrates strong performance in research-level tasks, extending beyond competition-style evaluations into high-economic-value applications. With enhanced instruction-following accuracy, it reliably executes detailed commands across technical, business, and analytical domains. Its long-context capabilities ensure coherence and reasoning stability across extended documents and multi-step processes. Designed for enterprise deployment, it balances depth of reasoning with operational efficiency and consistency. Altogether, Seed2.0 Pro represents a convergence of multimodal intelligence, agent autonomy, and production-ready robustness for advanced AI-driven workflows.