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

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

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

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

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

Muse Code is a beta terminal coding agent from Meta designed to help developers complete complex software engineering work across large codebases. Powered by Muse Spark 1.2, the agent can plan repository changes, write code, run validation steps, and coordinate persistent subagents for difficult development tasks. Muse Code uses a simple main agent loop supported by async background agents that remain active throughout each session. These background agents can gather information, carry out next steps, and decide when to report back to the main agent, reducing latency and unnecessary user steering. The runtime is built around a local event log where every model call, tool run, approval, and edit is appended. This event log makes Muse Code replay-exact and restart-safe, allowing it to resume from the point of failure after a crash. Muse Code also ships with default skills, including /plan, /grill, and /goal, to support structured planning, plan validation, and objective completion. It can be installed on macOS or Linux and is integrated with Meta’s AI developer ecosystem. By combining terminal-based coding, persistent subagents, replay-safe execution, bundled skills, and Muse Spark 1.2, Muse Code helps developers automate larger and longer software engineering workflows.

Fugu Cyber is an advanced orchestration model designed specifically for contemporary cyber defense, operating as a unified entity through a single API endpoint while adeptly managing multiple specialized agents to tackle intricate security challenges. This innovative model does not rely on a single provider and is tailored for two main defense operations: assessing complex codebases to identify genuine vulnerabilities and converting raw cyber threat intelligence into actionable detection rules. Its performance on CyberGym, which tests vulnerability analysis and validation, resulted in an impressive success rate of 86.9%, whereas on CTI-REALM, which evaluates the generation of detection rules from threat intelligence reports, it achieved a score of 72.1%. These results position Fugu Cyber among the top-tier models focused on cybersecurity innovations. Rather than functioning as an isolated tool, Fugu Cyber is designed to serve as the cognitive engine within larger security infrastructures, enhancing overall defense capabilities against evolving cyber threats. This integration allows for a more holistic approach to cyber defense, enabling organizations to respond more effectively to potential attacks.

MiniMax M3 is a frontier open-weight AI model built for coding, agentic work, multimodal understanding, and ultra-long-context tasks. The model supports up to a 1 million token context window, allowing it to work across large codebases, long documents, logs, project histories, and complex task environments. MiniMax M3 introduces MiniMax Sparse Attention, a sparse attention architecture designed to make long-context processing more efficient. The model is natively multimodal, with training that supports deeper semantic fusion across text, image, and video inputs. It is designed to support software engineering tasks, repository analysis, terminal-style work, browser-style retrieval, tool use, and autonomous workflows. MiniMax M3 has a mixture-of-experts architecture with hundreds of billions of total parameters and a smaller activated parameter count for more efficient inference. Developers can use it for AI coding assistants, workflow automation, research agents, document analysis, visual reasoning, and enterprise AI systems. Its long-context capability makes it especially useful when tasks require many files, references, instructions, or interaction histories to stay available at once. MiniMax M3 helps teams build more capable AI agents that can understand larger problems, work across multiple modalities, and execute complex tasks with stronger context awareness.

Cursor has introduced Composer 2.5, a next-generation AI coding assistant built to deliver stronger reasoning, better collaboration, and improved reliability during software development tasks. The upgraded model performs better on long-running coding workflows and can manage complicated instructions with greater consistency than earlier Composer versions. Cursor expanded the training process by scaling compute resources, generating more advanced reinforcement learning environments, and refining behavioral traits that improve the developer experience. One of the key innovations in Composer 2.5 is its targeted textual feedback system, which helps the model learn from localized mistakes inside long coding trajectories instead of relying only on broad reward signals. This training method allows the AI to improve coding style, communication quality, and tool usage accuracy in a more focused way. The company also increased the amount of synthetic coding data by 25 times compared to Composer 2, giving the model exposure to more difficult and realistic programming tasks. During development, the system demonstrated sophisticated reasoning abilities by uncovering hidden implementation details and reverse-engineering deleted functionality inside synthetic environments. Composer 2.5 additionally uses advanced distributed training methods such as Sharded Muon and dual mesh HSDP to optimize large-scale model training performance. Available directly inside Cursor, the model comes in both standard and fast variants with different pricing tiers designed for developers, teams, and enterprise-scale engineering workflows.

Claude Opus 4.8 is Anthropic’s newest flagship AI model built to improve coding performance, reasoning accuracy, agentic task execution, and collaborative AI workflows for developers, enterprises, and advanced productivity use cases. The model serves as an upgrade to Claude Opus 4.7, delivering measurable improvements across benchmarks related to coding, practical reasoning, software engineering, and autonomous task management while maintaining the same pricing structure for standard usage. One of the most significant improvements in Claude Opus 4.8 is its enhanced honesty and judgment during complex tasks, reducing the likelihood of unsupported claims, hidden errors, or overlooked flaws in generated code and analytical outputs. Anthropic’s evaluations show that Opus 4.8 is substantially less likely than previous versions to allow software defects or reasoning mistakes to pass without flagging uncertainty or requesting clarification. The platform introduces new effort control settings that allow users to adjust how deeply the model reasons through tasks, balancing response quality, processing depth, speed, and token usage depending on workflow requirements. Claude Opus 4.8 also powers new dynamic workflow functionality in Claude Code, enabling the model to coordinate hundreds of parallel subagents within a single session to handle large-scale software engineering tasks such as codebase migrations and extensive automation projects. The model supports high-speed fast mode processing, now significantly more affordable than previous versions, while also offering higher-effort reasoning modes optimized for difficult coding and operational workflows.

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

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

Gemini 3.5 Flash is Google’s high-performance multimodal AI model built to deliver frontier-level intelligence, fast execution speeds, and advanced agentic capabilities for coding, automation, and enterprise workflows. As the first release in the Gemini 3.5 series, the model is designed to help developers, businesses, and users execute complex long-horizon tasks through AI-powered reasoning, workflow orchestration, and intelligent automation. Gemini 3.5 Flash combines powerful coding performance, multimodal understanding, and real-time responsiveness while outperforming earlier Gemini models and competing frontier AI systems across several coding and reasoning benchmarks. The model is optimized for agentic workflows, allowing it to plan, execute, and manage multi-step tasks such as software development, infrastructure management, document preparation, and business process automation through the updated Antigravity harness. Gemini 3.5 Flash can also deploy collaborative subagents that work together under supervision to complete demanding workflows more efficiently and at lower operational cost. Beyond coding and automation, the platform generates richer graphics, dynamic web interfaces, interactive animations, and advanced multimodal experiences that support developers and enterprise users building AI-driven applications. Google has integrated Gemini 3.5 Flash across the Gemini app, AI Mode in Google Search, Google AI Studio, Android Studio, Gemini Enterprise Agent Platform, and enterprise AI services to expand access to advanced AI capabilities globally. The model also powers Gemini Spark, Google’s new personal AI agent designed to operate continuously and assist users with digital life management and automated task execution.

Kimi K2.7 Code is a Moonshot AI coding model built to help developers handle software engineering, code generation, debugging, and agent-based development workflows. It focuses on long-horizon coding tasks, where an AI assistant needs to understand goals, work across many files, and complete multi-step development work. The model builds on the Kimi K2.6 architecture and is described as improving agentic capabilities while reducing thinking-token usage by about 30% compared with K2.6. Kimi K2.7 Code offers a 256K context window, which helps developers work with larger repositories, longer prompts, and more detailed project instructions. It can be accessed through Kimi Code, Moonshot’s API platform, and third-party model providers such as Together AI. The model also supports OpenAI- and Anthropic-compatible APIs, making it easier for teams to test it as a replacement or addition to existing coding assistant workflows. Developers who want to self-host or experiment with the model can access it through Hugging Face, where deployment guidance references vLLM, SGLang, and KTransformers. Kimi K2.7 Code is especially relevant for teams interested in open-source coding agents, long-context software tasks, and tool-integrated development. While some third-party commentary notes that benchmark claims should be reviewed carefully, the model is positioned as a strong option for developers seeking flexible, agentic coding support.

Grok Imagine Video 1.5 represents xAI's enhanced model for transforming images into videos, designed to deliver superior quality and improved speed. Now accessible through the Imagine API under the name grok-imagine-video-1.5, it offers creators and developers the ability to initiate from a single image, articulate the desired motion, and select both the resolution and duration of the resulting video. Described as xAI’s most advanced image-to-video models to date, Grok Imagine Video 1.5 and its fast counterpart, Video 1.5 Fast, excel in producing superior motion, realistic physics, enhanced audio, and quicker generation times, making them ideal for genuine creative endeavors. Notably, audio and speech generation occurs simultaneously with the visuals, allowing for sound effects, background ambience, and dialogue to align seamlessly with the action, resulting in clearer and better-timed speech. Additionally, enhancements in motion and physics ensure that movements remain coherent throughout the clip, minimizing distortions while providing a more authentic sense of weight and momentum. With Grok Imagine Video 1.5 Fast, the generation speed is nearly doubled, enabling the creation of 6-second, 720p videos in approximately 25 seconds, greatly enhancing efficiency for users. This innovation not only streamlines the creative process but also opens up new possibilities for content creation.

Seedance 2.5 is a video creation model from ByteDance Seed designed to move AI video generation from short clips toward complete creative works. Built on the multimodal audio-video joint-generation architecture introduced with Seedance 2.0, the model focuses on foundational generation, reference-based generation, long-form storytelling, and editing control. Seedance 2.5 can generate up to 30 seconds of high-quality audio-video content in one pass and supports multiple rounds of extension. This allows users to create multi-minute videos while maintaining consistency across characters, environments, shot transitions, pacing, motion, and sound. The model supports up to 30 image references, 10 video references, and 10 audio references in a single generation request. It can preserve visual composition, characters, props, voices, scene style, motion paths, and creative intent across complex multi-subject videos. Editing capabilities include timestamp-level changes, green screen workflows, camera perspective editing, reference-based editing, and targeted modifications to characters, actions, or plot elements. Seedance 2.5 is rolling out through Jimeng AI and Doubao Pro, with API access planned through BytePlus ModelArk. By combining long-form generation, multimodal references, cinematic quality, synchronized audio-video output, and professional editing control, Seedance 2.5 helps creators produce more coherent and polished AI videos.

MiniMax H3 is a versatile omni-modal generation model that comprehensively grasps multimodal contexts across text, images, video, and audio. It produces videos featuring high-quality stereo sound at resolutions of up to 2K and durations of 15 seconds, catering to various industries such as advertising, branding, e-commerce, product design, UI/UX, gaming, and creative processes. Users have the capability to merge different reference types within a single command, such as replicating camera movements from a video, integrating characters from images into new scenes, and synchronizing vocals from audio clips, all while articulating the relationships using natural language. H3 also facilitates text-to-image and text-to-video conversions, incorporating audio that is generated simultaneously, alongside multi-shot modeling and text-to-audio functionalities, enabling versatile reference and editing across media types. Additionally, voice, sound effects, and music are synthesized cohesively within the model. With a strong emphasis on following instructions accurately, delivering precise text and brand representation, and executing video-to-video motion transfer, it stands out as a powerful tool for creative endeavors. This innovative approach allows for a more seamless integration of multimedia elements, making it easier for users to bring their creative visions to life.

Gemini Omni is an AI-powered multimodal video creation and editing platform developed by Google to help users transform ideas into cinematic-quality visual content using natural language interactions. The platform combines text, image, and video inputs to generate high-quality videos while simplifying traditionally complex video editing workflows through conversational AI capabilities. Gemini Omni allows users to perform advanced editing tasks such as cinematic zooming, background replacement, scene enhancement, and template-based production without needing specialized technical expertise or professional editing equipment. Users can upload footage from their camera roll, apply AI-driven modifications, and create polished videos using simple prompts and intuitive workflows. The platform also includes AI avatar generation capabilities that allow users to create personalized digital avatars that look and sound like them for more immersive and customized content creation. Gemini Omni is designed to make professional-grade video production more accessible for creators, marketers, businesses, and everyday users seeking faster and more flexible content generation tools. By combining multimodal AI generation with conversational editing controls, the platform reduces the complexity of traditional post-production and creative workflows. Gemini Omni is rolling out to Google AI Plus, Pro, and Ultra subscribers globally as part of Google’s expanding AI-powered creative ecosystem. Through AI-driven automation, multimodal generation, and intuitive editing experiences, Gemini Omni helps users create cinematic video content with greater speed, creativity, and ease.

Google has introduced Gemini Omni, a groundbreaking family of models that merges reasoning skills with creative capabilities, starting with video production. The flagship model, Gemini Omni Flash, possesses the remarkable ability to generate content from diverse inputs such as images, audio, video, and text, resulting in high-quality videos enriched by Gemini's comprehensive knowledge of the real world. By allowing users to edit video through a conversational interface, it ensures that each instruction seamlessly builds upon the previous one, maintaining character consistency, adhering to the laws of physics, and retaining continuity in scenes. Users are empowered to modify intricate details or entire environments, reimagine actions, introduce new characters or objects, alter surroundings, adjust camera perspectives, enhance styles, and execute multi-step edits without losing sight of the original narrative. Designed to seamlessly connect photorealism with impactful storytelling, Gemini Omni skillfully reasons about subsequent actions, drawing on an innate understanding of natural forces like gravity, kinetic energy, and fluid dynamics, which enhances the overall storytelling experience. This innovative approach not only simplifies video editing but also opens new avenues for creative expression, making it accessible to a broader audience.

FLUX 3 is an advanced multimodal foundation model that integrates learning from images, video, and audio all within a cohesive framework, effectively modeling how objects connect, how movements occur, and how events produce sound. Utilizing the Self-Flow methodology, it harmonizes the generation and comprehension of multiple modalities in a singular architecture, ensuring that each modality influences the others—sound corresponds to impact, motion adheres to physical laws, and future occurrences are informed by past events. This model is capable of blending modalities, allowing for the simultaneous generation of images, video, and authentic audio based on text prompts or references such as visual and auditory inputs. Its video functionalities are extensive, featuring text-to-video capabilities, image-driven video animation, video transformation, generative continuation of video and audio, controlled transitions using keyframes, multilingual dialogue support, animated text design, and the ability to deliver various styles and aspect ratios, alongside the capacity for agentic chaining into intricate, longer multi-shot sequences. Additionally, FLUX 3 represents a significant leap forward in the field of multimodal AI, offering unprecedented flexibility and creativity in generating rich, interactive content.

Muse Video is Meta’s upcoming AI video generation model developed by Meta Superintelligence Labs as part of the company’s new media generation lineup. Previewed with Muse Image, the model is built on the same pretraining base and is designed to produce visually detailed videos with native audio support. Muse Video is focused on generating clips that follow prompts closely, maintain strong visual fidelity, and preserve temporal consistency across motion and scene changes. It can create realistic short videos with clear beginnings, actions, and payoffs, such as animals moving through a scene, handheld first-person footage, product commercials, and UGC-style social ads. The model supports audio-rich outputs that may include environmental sound, foley, music, voiceover, and synchronized spoken dialogue. Meta highlights Muse Video’s ability to handle cinematic prompts, vertical ad formats, realistic camera movement, product demonstrations, and emotionally engaging creative concepts. The company is still investing in improvements for difficult areas such as audio-video sync and physically accurate fast motion. Muse Video is expected to become available to creators and in Meta AI, expanding Meta’s generative AI tools from image creation into video. As part of Meta’s broader creative ecosystem, Muse Video is built to help users, creators, and businesses turn prompts into dynamic, shareable video content.

HappyHorse-1.1-T2V is a QwenCloud video generation model that creates videos from text descriptions. The model is designed to improve text-to-video quality through stronger semantic understanding, cinematic shot control, and dynamic motion rendering. HappyHorse-1.1-T2V helps users produce videos that better reflect the intent of a prompt, including scene atmosphere, character movement, physical dynamics, and visual consistency. It supports video generation at 480P, 720P, and 1080P, with pricing based on generated video seconds. Developers can call the model through the DashScope API and configure parameters such as resolution, ratio, and duration. The model is not open source and is offered as a hosted API through QwenCloud. Rate limits include 300 requests per minute, 5 concurrent requests, and an async queue limit of 500 tasks. QwenCloud also provides free quota for testing and API key access for production usage. By combining text-to-video generation, semantic prompt understanding, cinematic control, motion rendering, API access, and scalable rate limits, HappyHorse-1.1-T2V helps teams build AI video creation workflows.

Veo 3 is Google’s most advanced video generation tool, built to empower filmmakers and creatives with unprecedented realism and control. Offering 4K resolution video output, real-world physics, and native audio generation, it allows creators to bring their visions to life with enhanced realism. The model excels in adhering to complex prompts, ensuring that every scene or action unfolds exactly as envisioned. Veo 3 introduces powerful features such as precise camera controls, consistent character appearance across scenes, and the ability to add sound effects, ambient noise, and dialogue directly into the video. These new capabilities open up new possibilities for both professional filmmakers and enthusiasts, offering full creative control while maintaining a seamless and natural flow throughout the production.

Veo 3.1 expands upon the features of its predecessor, allowing for the creation of longer and more adaptable AI-generated videos. This upgraded version empowers users to produce multi-shot videos based on various prompts, generate sequences using three reference images, and incorporate frames in video projects that smoothly transition between a starting and ending image, all while maintaining synchronized, native audio. A notable addition is the scene extension capability, which permits the lengthening of the last second of a clip by up to an entire minute of newly generated visuals and sound. Furthermore, Veo 3.1 includes editing tools for adjusting lighting and shadow effects, enhancing realism and consistency throughout the scenes, and features advanced object removal techniques that intelligently reconstruct backgrounds to eliminate unwanted elements from the footage. These improvements render Veo 3.1 more precise in following prompts, present a more cinematic experience, and provide a broader scope compared to models designed for shorter clips. Additionally, developers can easily utilize Veo 3.1 through the Gemini API or via the Flow tool, which is specifically aimed at enhancing professional video production workflows. This new version not only refines the creative process but also opens up new avenues for innovation in video content creation.

Semrush One is a comprehensive digital visibility solution built to help brands dominate every modern search environment. It combines Semrush’s industry-leading SEO technology with powerful AI visibility and generative search optimization tools. Users can track performance across traditional search engines and AI platforms like ChatGPT and Gemini in one unified system. The platform delivers actionable insights across SEO, content, paid advertising, social media, local listings, and AI PR. With access to billions of keywords, backlinks, and domain profiles, Semrush One uncovers growth opportunities others miss. Advanced forecasting tools allow teams to predict traffic, conversions, and revenue impact. Enterprise-ready features support automation, scalability, and cross-team collaboration. Real-world benchmarks and competitor analysis help brands understand market position and gaps. Semrush One is used by Fortune 500 companies and global enterprises worldwide. It enables marketers to turn data into measurable, sustainable growth.