Best AI Models for Hermes Agent - Page 2

Qwen3.8-Max is a frontier AI model from the Qwen family designed for advanced coding, coworking, research, multimodal reasoning, and long-horizon autonomous tasks. It is described as Qwen’s most capable model to date and the first Qwen-Max-class model with open weights announced for release. The model scales to 2.4 trillion parameters with 95 billion active parameters and is accessible through QwenCloud. Qwen3.8-Max is built to answer difficult questions and complete complex deliverables from start to finish. Its coding capabilities include autonomous project creation, self-testing, issue dispatch, CI validation, pull request workflows, and long-running feedback loops. The model is also designed for real-world work across legal review, UI/UX design, restaurant operations, structural engineering, rehabilitation visualization, sports analytics, and quantitative research. Its multimodal capabilities support images, documents, videos, interface reconstruction, visual production, application recreation, and visual feedback loops. QwenCloud supports industry-standard API protocols, including OpenAI-compatible chat completions and responses APIs as well as an Anthropic-compatible interface. By combining large-scale reasoning, agentic coding, multimodal intelligence, API access, long-context workflows, and open-weight availability, Qwen3.8-Max gives teams a powerful foundation for building advanced AI systems.

GPT-5.6 Terra is OpenAI’s balanced GPT-5.6 model for users who need strong performance across everyday work, development tasks, enterprise workflows, and technical analysis. The model is part of the GPT-5.6 family alongside Sol and Luna, with Terra positioned as the middle tier for capable, cost-efficient use. Terra is described as having competitive performance to GPT-5.5 while being 2x cheaper, making it useful for teams that want advanced capability without always using the flagship model. It supports coding workflows, agentic tasks, cybersecurity-related defensive work, biology workflows, knowledge work, and tool-assisted automation. In benchmark previews, Terra appears alongside Sol and Luna in evaluations for coding, biology, ExploitBench, and ExploitGym. The model benefits from the GPT-5.6 safeguard stack, which includes model-level refusals for prohibited cyber assistance, real-time cyber and biology misuse classifiers, and account-level risk review. These safeguards are designed to preserve access to legitimate work such as code review, debugging, vulnerability research, patch development, security education, and defensive testing. GPT-5.6 Terra is planned for availability through the API, Codex, and broader OpenAI products after the limited preview period. GPT-5.6 Terra helps teams get a balanced model for high-quality AI work when they need strong reasoning and automation at a lower cost than Sol.

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.

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.

GPT-5.5 is a next-generation AI system built for execution-heavy workflows across coding, research, business analysis, and scientific tasks. It can interpret complex instructions, break them into actionable steps, and carry them through to completion while interacting with tools and systems. The model supports creating applications, generating reports, analyzing datasets, and navigating software environments seamlessly. It also integrates with workspace agents—custom AI agents that automate recurring and multi-step processes across teams. These agents can handle tasks such as lead research, reporting, and workflow automation, either on demand or on schedules. GPT-5.5 enhances productivity by reducing manual effort and enabling continuous task execution across tools. With enterprise-grade safeguards and monitoring, it ensures secure and controlled automation. It is well-suited for organizations looking to scale operations and improve efficiency through AI-driven 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.

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.

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.

Seed2.1 represents a groundbreaking advancement in productivity tools, featuring two distinct AI models, Pro and Turbo, tailored for varying levels of user needs. Designed to address intricate challenges encountered in everyday tasks, workplace responsibilities, and innovative ventures, this agent significantly enhances capabilities in areas such as general assistance, code development, multimodal comprehension, knowledge application, and reasoning processes. For demanding office tasks and intricate daily consultations, Seed2.1 adeptly manages a range of multi-step processes, including project management, document handling, tool utilization, data analysis, solution formulation, content organization, and synthesis of outcomes. In the realm of software development, Seed2.1 optimizes end-to-end processes within enterprise-level workflows, covering aspects like requirement gathering, software architecture, feature development, debugging, environment configuration, and quality assurance. Additionally, the model is proficient in comprehending entire codebases, effectively coordinating updates across numerous files, and ensuring the delivery of sustainable, production-ready software engineering solutions. Ultimately, Seed2.1 not only enhances productivity but also empowers users to tackle complex challenges with confidence.

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.

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.

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.

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.

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.

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.

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.

Grok is a powerful AI chatbot developed by xAI, designed to deliver real-time, intelligent, and conversational assistance. It is uniquely integrated with the X platform, enabling access to live data and trending topics for more relevant responses. Grok is built to handle a wide range of tasks, including answering questions, generating content, and assisting with research. The platform combines advanced reasoning capabilities with a conversational tone, often incorporating humor and personality. It uses large-scale language models to understand context and provide accurate, meaningful answers. Grok is particularly useful for staying updated on current events and social trends. Its real-time data access sets it apart from traditional AI assistants that rely on static knowledge. The platform is designed for both casual users and professionals seeking quick insights. It continuously evolves with updates and improvements from xAI. Overall, Grok delivers a modern AI experience focused on relevance, engagement, and real-time intelligence.

SuperGrok is an upgraded AI subscription tier from xAI that unlocks access to its most advanced Grok models. It is built on newer versions of Grok, offering improved reasoning, coding, and analytical capabilities compared to earlier models. The platform is designed for users who need more than basic AI assistance, including researchers, developers, and business professionals. SuperGrok supports multimodal functionality, enabling it to handle text, images, and other forms of input. It provides higher usage limits, faster processing speeds, and longer conversation sessions than lower-tier plans. Users can take advantage of features like AI agents, deep search tools, and enhanced creative generation capabilities. The service also includes early access to new features and ongoing updates from xAI. It can be used for complex workflows such as data analysis, technical problem-solving, and content production. SuperGrok integrates with web platforms and applications to extend its usability. Its architecture allows it to handle more demanding tasks efficiently. With its expanded capabilities, it is positioned as a high-performance AI solution for advanced use cases.

Claude Opus 4.7 is an advanced AI model built to push the boundaries of software engineering, automation, and complex reasoning tasks. Compared to Opus 4.6, it delivers notable improvements in handling challenging coding workflows and executing long-duration tasks with consistency. The model excels at strictly following user instructions, reducing ambiguity and improving output accuracy. It also introduces stronger self-verification capabilities, allowing it to check and refine its own results before presenting them. One of its key upgrades is enhanced multimodal functionality, particularly its ability to process higher-resolution images with greater clarity. This enables more precise analysis of visuals such as technical diagrams, dense screenshots, and structured data layouts. Opus 4.7 is also more refined in generating professional content, including polished documents, presentations, and interface designs. In real-world applications, it performs effectively across domains like finance, legal analysis, and business workflows. The model incorporates improved memory features, allowing it to retain context across extended sessions and reduce repetitive input requirements. It also introduces built-in safeguards to detect and prevent misuse, especially in sensitive cybersecurity scenarios. With broad availability across APIs and cloud platforms, Opus 4.7 offers developers and enterprises a powerful, scalable AI solution.

Muse Glimmer is an open-weights model featuring 30 billion parameters, developed by Meta Superintelligence Labs, and is fine-tuned for continuous local agent operations. Its compact design allows it to function on a standard Mac or PC equipped with a single consumer GPU, making it ideal for various tasks such as local agent management, function calling, programming, and LLM-as-a-judge evaluations without reliance on cloud services or internet connectivity. This innovative model integrates advanced capabilities such as long-horizon execution, accurate tool invocation, multimodal comprehension, extended memory for context, and effective instruction following. It is proficient in accomplishing end-to-end tasks as an agent, maintains the ability to engage in multi-step reasoning over lengthy processes, can recover gracefully from failed or unanticipated tool engagements, and interprets interleaved text and images using a specialized perception encoder designed for analyzing screenshots, graphs, and document files. Furthermore, Muse Glimmer is compatible with OpenClaw and other orchestration frameworks, allowing for adjustable reasoning efforts, and has been developed with a diverse dataset encompassing over 100 languages. The model's versatility ensures that it can adapt to various applications, thus enhancing its utility in different domains.

Muse Spark 1.3 represents an advanced AI model that has enhanced capabilities for both agentic and coding tasks, making it more intelligent and practical for use in everyday applications. It excels in maintaining focus on extended tasks through active collaboration with users while efficiently managing various workflows within a single, continuous thread. When faced with an open-ended goal, the model adeptly utilizes tools to create context from disorganized or contradictory information, rectify any gaps in its strategy, track its learning progress, and ultimately generate a final product. In situations where prompts lack clarity, it is proactive in seeking clarification, asking for assistance when it encounters obstacles, and confirming its actions before proceeding with significant decisions. The model demonstrates a high level of reliability in following intricate, long-form instructions, ensuring that detailed requirements are maintained throughout complex, multi-step tasks without losing critical constraints or deviating from the desired workflow. With its enhanced multitasking capabilities, it effectively aligns incoming prompts with the appropriate tasks, even in cases where users interject or shift the focus of previous requests, allowing for a seamless user experience. This makes Muse Spark 1.3 a versatile tool for a wide range of applications.

Gemma 4 is an advanced AI model developed by Google as part of its Gemini architecture, designed to deliver strong performance while remaining accessible to developers. The model is optimized to run on a single GPU or TPU, allowing more organizations and researchers to experiment with powerful AI technology. Gemma 4 improves natural language understanding and generation, making it suitable for applications such as chatbots, text analysis, and automated content creation. Its architecture enables the model to process complex language patterns while maintaining efficient computational performance. Developers can integrate Gemma 4 into various AI projects that require intelligent text processing or conversational capabilities. The model is designed with scalability in mind, allowing it to support both research experiments and production systems. By offering high-performance AI in a more accessible format, Gemma 4 lowers the barrier for developing sophisticated AI solutions. Its flexibility makes it useful for industries ranging from technology and education to business automation. Researchers can also use the model to explore new AI techniques and improve language processing systems. Overall, Gemma 4 represents a step forward in making powerful AI models easier to deploy and use.

Muse Wonder Full is a frontier AI model developed by Meta to support advanced reasoning, personal assistance, professional workflows, and autonomous software development. It is the larger model variant in the Muse Wonder series, which also includes the standard Muse Wonder model. The system combines Active Memory and Active Focus to maintain relevant context and sustain progress on complex assignments. Active Memory enables the model to preserve important information across conversations and retrieve details relevant to current objectives. Active Focus supports multistep execution by allowing the model to investigate alternative solutions, respond to obstacles, and adjust its approach as tasks evolve. Muse Wonder Full can assist with legal document analysis, financial reasoning, health-related information, and other knowledge-intensive activities. Its software engineering capabilities support code generation, application development, debugging, testing, and modifications across existing codebases. Meta has evaluated the model using benchmarks such as MemoryBench, FocusBench, SWE-Marathon, DeepSWE, and professional agent assessments. Muse Wonder Full is accessible through Muse Code subscriptions and is intended for tasks that require extended reasoning, contextual continuity, and coordinated AI agent execution.

MiniMax M3.1 Flash is an advanced hosted language model designed specifically for developers who are creating coding assistants, AI agents, and various software applications. It can be accessed via MiniMax's API and MiniMax Code, allowing it to process inputs in text, image, and video formats while generating relevant text responses. Among its documented applications are functionalities such as code generation, debugging, complex reasoning tasks, tool utilization, analysis of codebases and documents, as well as enhancing computer workflows. The API boasts an impressive context window that can handle up to 1 million tokens, offers adjustable reasoning capabilities, and supports request formats that are compatible with both OpenAI and Anthropic standards. Charges for usage are based on the number of tokens used for input, output, and cached input. Comprehensive documentation is provided by MiniMax to assist with API integration and effective model utilization. M3.1 Flash is accessible through the hosted API, M Plan, and MiniMax Code, while there are plans to release model weights in the future, expanding its potential applications even further. This innovation represents a significant step forward in the integration of AI into software development processes.

Sora is an advanced AI model designed to transform text descriptions into vivid and lifelike video scenes. Our focus is on training AI to grasp and replicate the dynamics of the physical world, with the aim of developing systems that assist individuals in tackling challenges that necessitate real-world engagement. Meet Sora, our innovative text-to-video model, which has the capability to produce videos lasting up to sixty seconds while preserving high visual fidelity and closely following the user's instructions. This model excels in crafting intricate scenes filled with numerous characters, distinct movements, and precise details regarding both the subject and surrounding environment. Furthermore, Sora comprehends not only the requests made in the prompt but also the real-world contexts in which these elements exist, allowing for a more authentic representation of scenarios.