{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-z-ai-glm-5v-turbo","slug":"z-ai-glm-5v-turbo","name":"Z.ai: GLM 5V Turbo","type":"model","url":"https://openrouter.ai/models/z-ai~glm-5v-turbo","page_url":"https://unfragile.ai/z-ai-glm-5v-turbo","categories":["ai-agents"],"tags":["z-ai","api-access","text","image","video"],"pricing":{"model":"paid","free":false,"starting_price":"$1.20e-6 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-z-ai-glm-5v-turbo__cap_0","uri":"capability://image.visual.native.multimodal.input.processing.with.vision.language.fusion","name":"native multimodal input processing with vision-language fusion","description":"GLM-5V-Turbo processes image, video, and text inputs through a unified multimodal encoder that fuses visual and linguistic representations at the token level, enabling the model to reason across modalities without separate vision-text bridges. The architecture natively handles variable-length video sequences by temporally sampling frames and encoding them with spatial-temporal attention mechanisms, allowing the model to understand motion, scene changes, and temporal context without post-hoc video summarization.","intents":["analyze screenshots and code snippets simultaneously to understand UI implementation details","process multi-frame video sequences to understand step-by-step workflows or debugging sessions","extract structured information from documents containing mixed text and diagrams","understand visual design intent from mockups while reasoning about implementation constraints"],"best_for":["AI agents performing vision-based code generation and debugging","teams building document understanding systems that require visual context","developers automating UI testing and visual regression detection"],"limitations":["Maximum video length and frame sampling rate not publicly specified — may introduce temporal aliasing for fast-motion content","No explicit support for 3D point clouds or volumetric data — limited to 2D images and 2D video frames","Multimodal fusion adds computational overhead compared to text-only inference — exact latency multiplier unknown"],"requires":["API access via OpenRouter or Z.ai endpoints","Image formats: JPEG, PNG, WebP (typical constraints apply)","Video formats: MP4, WebM (frame extraction required for processing)","Text input: UTF-8 encoded strings up to model context window"],"input_types":["image (JPEG, PNG, WebP)","video (MP4, WebM with frame sampling)","text (UTF-8 strings)","mixed multimodal sequences (image + text, video + text)"],"output_types":["text (natural language reasoning)","code (generated or refactored)","structured data (JSON, YAML for extracted information)"],"categories":["image-visual","text-generation-language","multimodal-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-z-ai-glm-5v-turbo__cap_1","uri":"capability://planning.reasoning.long.horizon.agent.planning.with.visual.state.tracking","name":"long-horizon agent planning with visual state tracking","description":"GLM-5V-Turbo implements chain-of-thought reasoning extended across multi-step agent tasks by maintaining visual state representations across planning steps. The model decomposes complex goals into intermediate subgoals while tracking visual changes (e.g., UI state transitions, code modifications) through image comparisons, enabling it to verify plan execution and adapt when visual outcomes diverge from expectations. This is implemented through attention mechanisms that compare current visual state against previous states to detect anomalies or plan failures.","intents":["decompose complex coding tasks into multi-step agent workflows with visual verification at each step","automatically detect when a UI automation task has failed by comparing expected vs actual screenshots","generate step-by-step debugging workflows that include visual inspection of intermediate states","plan and execute multi-file refactoring tasks with visual code review at each stage"],"best_for":["autonomous coding agents that need to verify task completion visually","UI automation frameworks that require adaptive planning based on visual feedback","teams building self-correcting AI workflows for complex development tasks"],"limitations":["Planning depth and branching factor not specified — may struggle with >10-step workflows or high-branching scenarios","Visual state comparison relies on pixel-level or semantic similarity metrics — sensitive to minor rendering differences or anti-aliasing artifacts","No explicit rollback or backtracking mechanism documented — failed plans may require manual intervention","Context window constraints limit the number of intermediate visual states that can be tracked in a single planning session"],"requires":["API access to GLM-5V-Turbo via OpenRouter","Ability to capture or provide sequential screenshots/images of task execution","Task specification in natural language or structured format","Sufficient context window for multi-step planning (exact requirement unknown)"],"input_types":["text (task description, goal specification)","image (current visual state, screenshots)","sequence of images (visual history for state tracking)"],"output_types":["text (step-by-step plan with reasoning)","code (generated actions or commands)","structured plan (JSON with steps, preconditions, postconditions)"],"categories":["planning-reasoning","image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-z-ai-glm-5v-turbo__cap_2","uri":"capability://code.generation.editing.vision.grounded.code.generation.and.refactoring","name":"vision-grounded code generation and refactoring","description":"GLM-5V-Turbo generates or refactors code by analyzing visual representations of the target state (screenshots, diagrams, design mockups) alongside textual specifications. The model uses visual grounding to understand UI layouts, component hierarchies, and styling intent, then generates implementation code that matches the visual specification. For refactoring, it analyzes code screenshots or syntax-highlighted snippets to understand existing structure and generates improved versions that maintain visual/functional equivalence while improving quality metrics (readability, performance, maintainability).","intents":["generate React/Vue/HTML components from design mockups or screenshots without manual specification","refactor legacy code by analyzing visual representations to understand intent and structure","implement UI features by comparing current screenshots against desired visual state","generate CSS or styling code that matches visual mockups or design specifications"],"best_for":["frontend developers building UI from design systems or mockups","teams automating code generation from visual specifications","developers refactoring codebases where visual context is critical to understanding intent"],"limitations":["Code generation accuracy depends on visual clarity — low-resolution or ambiguous screenshots may produce incorrect implementations","No explicit support for complex interactive behaviors that aren't visually obvious (e.g., async state management, event handling logic)","Generated code may require manual review for accessibility, performance, or security considerations","Limited to visual patterns the model has seen during training — novel UI paradigms may not be recognized"],"requires":["API access to GLM-5V-Turbo","High-quality screenshots or design mockups (minimum 1024x768 recommended)","Target language specification (JavaScript, Python, etc.)","Optional: existing code for refactoring tasks"],"input_types":["image (design mockup, screenshot, wireframe)","text (code specification, refactoring instructions)","code (existing implementation for refactoring)","mixed (visual + textual specification)"],"output_types":["code (generated or refactored implementation)","text (explanation of changes, reasoning)","structured data (component hierarchy, styling rules)"],"categories":["code-generation-editing","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-z-ai-glm-5v-turbo__cap_3","uri":"capability://image.visual.complex.reasoning.over.mixed.modality.documents","name":"complex reasoning over mixed-modality documents","description":"GLM-5V-Turbo analyzes documents containing text, diagrams, tables, and images by maintaining unified semantic representations across modalities. It performs reasoning tasks like answering questions, extracting structured information, or summarizing content by understanding relationships between visual elements (diagrams, charts) and textual content (captions, body text). The model uses cross-modal attention to align visual and textual information, enabling it to answer questions that require understanding both the visual structure and textual content simultaneously.","intents":["extract structured data from PDFs or documents with mixed text and diagrams","answer questions about technical documentation that includes code snippets, architecture diagrams, and explanatory text","summarize research papers or reports that rely on figures and tables for key insights","understand and explain complex system architectures described through diagrams and textual annotations"],"best_for":["teams building document understanding systems for technical or scientific content","developers automating information extraction from mixed-format documentation","researchers analyzing papers or reports that require visual and textual understanding"],"limitations":["Document layout understanding may fail on complex multi-column layouts or non-standard formatting","Table and chart interpretation depends on visual clarity — small fonts or low contrast may reduce accuracy","No explicit support for handwritten annotations or non-standard notation systems","Cross-modal alignment may struggle with documents where visual and textual information is spatially separated"],"requires":["API access to GLM-5V-Turbo","Document images or screenshots (PDF conversion to images required)","Query or task specification in natural language","Sufficient context window for multi-page documents (exact limits unknown)"],"input_types":["image (document page, screenshot)","text (query, task specification)","sequence of images (multi-page documents)"],"output_types":["text (answer, summary, explanation)","structured data (extracted information in JSON/YAML)","code (if document contains code snippets)"],"categories":["image-visual","text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-z-ai-glm-5v-turbo__cap_4","uri":"capability://image.visual.video.based.workflow.understanding.and.automation","name":"video-based workflow understanding and automation","description":"GLM-5V-Turbo analyzes video sequences to understand multi-step workflows (e.g., debugging sessions, UI interactions, development processes) by extracting temporal patterns and causal relationships between frames. The model identifies key frames, detects state transitions, and generates descriptions or automation scripts based on observed behavior. It uses temporal attention mechanisms to understand motion, scene changes, and event sequences, enabling it to recognize patterns like 'user opens file → searches for function → navigates to definition' and generate corresponding automation code.","intents":["generate automation scripts by analyzing screen recordings of manual workflows","understand debugging workflows from video recordings and suggest improvements","extract step-by-step instructions from tutorial or demonstration videos","detect and classify UI interactions from video to build test automation scenarios"],"best_for":["teams automating repetitive workflows by analyzing video demonstrations","developers building test automation from recorded user interactions","technical writers extracting step-by-step instructions from tutorial videos"],"limitations":["Temporal sampling may miss fast interactions or brief state changes — frame rate and sampling strategy not specified","Understanding of implicit intent (e.g., 'why' a user performed an action) is limited to observable behavior","No explicit support for audio track analysis — relies solely on visual information","Video length constraints not documented — may struggle with hour-long recordings"],"requires":["API access to GLM-5V-Turbo","Video file in supported format (MP4, WebM, etc.)","Optional: task specification or context about the workflow","Sufficient context window for temporal sequence encoding"],"input_types":["video (screen recording, tutorial, workflow demonstration)","text (optional context or task specification)"],"output_types":["text (workflow description, step-by-step instructions)","code (automation script, test case)","structured data (sequence of actions, state transitions)"],"categories":["image-visual","automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-z-ai-glm-5v-turbo__cap_5","uri":"capability://tool.use.integration.api.based.inference.with.streaming.and.batch.processing","name":"api-based inference with streaming and batch processing","description":"GLM-5V-Turbo is accessed via OpenRouter's API, supporting both streaming and batch inference modes. Streaming mode returns tokens incrementally, enabling real-time response display for interactive applications. Batch processing mode accepts multiple requests and returns results asynchronously, optimizing throughput for non-interactive workloads. The API abstracts underlying model deployment details, handling load balancing, rate limiting, and fallback mechanisms transparently. Integration is straightforward via standard HTTP requests with JSON payloads containing text and base64-encoded image/video data.","intents":["integrate GLM-5V-Turbo into web applications with real-time streaming responses","process large batches of documents or images asynchronously for bulk analysis","build chatbots or agents that stream responses incrementally to users","automate batch jobs that analyze multiple files or images without blocking"],"best_for":["developers building interactive applications requiring real-time AI responses","teams processing large document or image batches with asynchronous workflows","startups integrating AI capabilities without managing infrastructure"],"limitations":["API latency depends on OpenRouter infrastructure — no SLA or guaranteed response time published","Streaming mode may introduce additional latency compared to batch processing","Rate limiting and quota constraints not publicly specified — may require backoff logic","Image/video encoding to base64 adds overhead — large files may exceed request size limits","No local inference option — all requests must traverse the network"],"requires":["OpenRouter API key or Z.ai API credentials","HTTP client library (Python requests, Node.js fetch, etc.)","Network connectivity to OpenRouter endpoints","Base64 encoding capability for image/video data"],"input_types":["text (JSON request body with prompt)","image (base64-encoded JPEG, PNG, WebP)","video (base64-encoded MP4, WebM)","mixed (text + images/video in single request)"],"output_types":["text (streaming tokens or complete response)","structured data (JSON response with metadata)","code (if code generation is requested)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-z-ai-glm-5v-turbo__cap_6","uri":"capability://code.generation.editing.context.aware.code.understanding.and.explanation","name":"context-aware code understanding and explanation","description":"GLM-5V-Turbo analyzes code (provided as text or screenshots) within visual and textual context to generate explanations, identify issues, or suggest improvements. When code is provided as screenshots, the model understands syntax highlighting, indentation, and visual structure to infer language and intent. It performs reasoning about code semantics by analyzing variable names, function signatures, and control flow patterns, then generates explanations that account for the broader codebase context (if provided) or visual context (if analyzing screenshots of an IDE with visible file structure).","intents":["generate explanations of code snippets or functions for documentation or learning","identify bugs or code smells by analyzing code screenshots with IDE context visible","suggest refactoring improvements based on visual code structure and context","understand legacy code by analyzing screenshots that show file structure, imports, and dependencies"],"best_for":["developers learning unfamiliar codebases or languages","teams documenting code or generating API documentation automatically","code reviewers seeking AI-assisted analysis of complex functions"],"limitations":["Code understanding accuracy depends on visual clarity — small fonts or poor contrast reduce accuracy","Limited to visible context in screenshots — cannot analyze full file or project structure unless explicitly provided","Semantic understanding may fail for domain-specific languages or non-standard syntax","No execution or type-checking — analysis is based on static code structure only"],"requires":["API access to GLM-5V-Turbo","Code as text or screenshot (syntax highlighting recommended for clarity)","Optional: surrounding context (imports, function signatures, documentation)"],"input_types":["text (code snippet)","image (code screenshot, IDE screenshot with visible context)","mixed (code + textual context or questions)"],"output_types":["text (explanation, analysis, suggestions)","code (refactored version or example)","structured data (identified issues, metrics)"],"categories":["code-generation-editing","image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"low","permissions":["API access via OpenRouter or Z.ai endpoints","Image formats: JPEG, PNG, WebP (typical constraints apply)","Video formats: MP4, WebM (frame extraction required for processing)","Text input: UTF-8 encoded strings up to model context window","API access to GLM-5V-Turbo via OpenRouter","Ability to capture or provide sequential screenshots/images of task execution","Task specification in natural language or structured format","Sufficient context window for multi-step planning (exact requirement unknown)","API access to GLM-5V-Turbo","High-quality screenshots or design mockups (minimum 1024x768 recommended)"],"failure_modes":["Maximum video length and frame sampling rate not publicly specified — may introduce temporal aliasing for fast-motion content","No explicit support for 3D point clouds or volumetric data — limited to 2D images and 2D video frames","Multimodal fusion adds computational overhead compared to text-only inference — exact latency multiplier unknown","Planning depth and branching factor not specified — may struggle with >10-step workflows or high-branching scenarios","Visual state comparison relies on pixel-level or semantic similarity metrics — sensitive to minor rendering differences or anti-aliasing artifacts","No explicit rollback or backtracking mechanism documented — failed plans may require manual intervention","Context window constraints limit the number of intermediate visual states that can be tracked in a single planning session","Code generation accuracy depends on visual clarity — low-resolution or ambiguous screenshots may produce incorrect implementations","No explicit support for complex interactive behaviors that aren't visually obvious (e.g., async state management, event handling logic)","Generated code may require manual review for accessibility, performance, or security considerations","builder identity is not verified yet","no observed match outcomes 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