{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-z-ai-glm-4-32b","slug":"z-ai-glm-4-32b","name":"Z.ai: GLM 4 32B ","type":"model","url":"https://openrouter.ai/models/z-ai~glm-4-32b","page_url":"https://unfragile.ai/z-ai-glm-4-32b","categories":["llm-apis"],"tags":["z-ai","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$1.00e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-z-ai-glm-4-32b__cap_0","uri":"capability://text.generation.language.multi.turn.conversational.reasoning.with.context.retention","name":"multi-turn conversational reasoning with context retention","description":"Maintains conversation history across multiple exchanges, building context through a sliding window of prior messages. The model processes the full conversation thread to generate contextually-aware responses, enabling coherent multi-step dialogues without explicit state management. This is implemented via transformer attention mechanisms that weight recent and relevant prior turns more heavily than distant ones.","intents":["I need an AI assistant that understands the full context of my multi-step problem without me repeating myself","I want to have a natural back-and-forth conversation where the model remembers what we discussed earlier","I need to refine my requests iteratively and have the model build on previous clarifications"],"best_for":["developers building conversational AI agents and chatbots","teams prototyping interactive debugging assistants","non-technical users needing natural dialogue interfaces"],"limitations":["context window is finite — very long conversations (>32K tokens) may lose early context","no persistent memory across separate conversation sessions","attention mechanism adds latency proportional to conversation length"],"requires":["API access via OpenRouter or compatible endpoint","HTTP client capable of streaming or polling responses","conversation history management on client side"],"input_types":["text (natural language queries)","code snippets (for debugging/analysis)","structured prompts with role definitions"],"output_types":["text (natural language responses)","code (generated or refactored)","structured reasoning traces"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-z-ai-glm-4-32b__cap_1","uri":"capability://code.generation.editing.code.generation.and.completion.with.language.specific.patterns","name":"code generation and completion with language-specific patterns","description":"Generates syntactically correct code across 40+ programming languages by learning language-specific idioms, libraries, and patterns from training data. The model understands context from partial code, docstrings, and type hints to predict the most likely next tokens, supporting both completion-in-place and full-function generation. Implementation leverages transformer architecture with language-aware tokenization and embedding spaces.","intents":["I need to auto-complete a function based on its signature and docstring","I want to generate boilerplate code for a specific framework or library","I need to write code in a language I'm less familiar with and want intelligent suggestions"],"best_for":["solo developers using IDE plugins or API-based editors","teams building internal code generation tools","developers working across multiple languages in polyglot codebases"],"limitations":["no real-time AST validation — generated code may have syntax errors in edge cases","limited to patterns seen in training data — novel or very recent library APIs may be incomplete","no built-in refactoring or optimization — generated code may not follow project style guides"],"requires":["API key for OpenRouter or compatible LLM provider","code context (file, function signature, or snippet) as input","HTTP client for API calls"],"input_types":["code (partial function, class definition, or snippet)","text (docstrings, comments, type hints)","structured metadata (language identifier, framework name)"],"output_types":["code (completed function, generated class, or full module)","text (explanatory comments or docstrings)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-z-ai-glm-4-32b__cap_10","uri":"capability://planning.reasoning.instruction.following.and.task.decomposition.for.complex.workflows","name":"instruction-following and task decomposition for complex workflows","description":"Understands complex, multi-step instructions and breaks them into executable subtasks, maintaining state across steps. The model learns to follow detailed specifications, handle edge cases, and adapt to variations in input. Implementation uses instruction-tuning on task datasets with explicit step-by-step reasoning, enabling the model to plan, execute, and verify each step of a workflow.","intents":["I need the model to follow detailed instructions for a complex process without losing track of requirements","I want to decompose a large task into subtasks and have the model execute them in order","I need the model to handle edge cases and variations while maintaining consistency"],"best_for":["developers building task automation systems or workflow engines","teams creating AI agents for complex business processes","builders needing reliable instruction-following for multi-step operations"],"limitations":["instruction following is probabilistic — complex or ambiguous instructions may be misinterpreted","no persistent state across separate API calls — requires client-side state management","task decomposition may miss dependencies or create suboptimal execution order","error recovery is limited — no built-in retry logic or fallback strategies"],"requires":["API key for OpenRouter","detailed instructions or task specification","optional: examples or reference implementations"],"input_types":["text (detailed instructions, specifications)","structured data (task parameters, constraints)","code (reference implementations or examples)"],"output_types":["text (step-by-step execution plan)","structured data (task results, intermediate outputs)","code (generated implementation)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-z-ai-glm-4-32b__cap_2","uri":"capability://tool.use.integration.tool.invocation.and.function.calling.with.schema.based.routing","name":"tool invocation and function calling with schema-based routing","description":"Accepts structured tool definitions (function signatures, parameter schemas, descriptions) and generates function calls with correctly-typed arguments when the model determines a tool is needed. The model learns to route requests to appropriate tools by matching user intent against tool descriptions, then formats output as structured JSON or code that can be directly executed. This is implemented via instruction-tuning on tool-use datasets and constrained decoding to ensure valid schema compliance.","intents":["I want the model to decide when to call external APIs or functions and generate the correct parameters","I need to integrate the model with my existing tool ecosystem without custom prompt engineering","I want the model to chain multiple tool calls to solve complex tasks"],"best_for":["developers building AI agents with external tool dependencies","teams integrating LLMs into existing API-driven systems","builders creating autonomous workflows that need to interact with databases or services"],"limitations":["tool selection is probabilistic — model may choose wrong tool or hallucinate tool names not in schema","parameter generation may fail validation if schema is ambiguous or tool descriptions are unclear","no built-in error handling or retry logic — requires wrapper code to handle failed tool calls"],"requires":["API key for OpenRouter or compatible provider","tool definitions in JSON schema format (OpenAPI or similar)","client-side code to execute generated function calls and return results"],"input_types":["text (natural language request)","structured tool definitions (JSON schema)","prior tool call results (for chaining)"],"output_types":["structured function calls (JSON with function name and parameters)","text (reasoning about which tool to use)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-z-ai-glm-4-32b__cap_3","uri":"capability://search.retrieval.online.search.integration.and.real.time.information.retrieval","name":"online search integration and real-time information retrieval","description":"Can query the internet to retrieve current information when the model determines that real-time data is needed to answer a user query. The model learns to recognize when its training data is insufficient (e.g., current events, recent product releases, live prices) and generates search queries, then synthesizes results into coherent answers. Implementation involves decision logic to determine search necessity, query generation, and result ranking/synthesis.","intents":["I need the model to answer questions about current events or recent news without hallucinating","I want real-time information like stock prices, weather, or product availability integrated into responses","I need the model to cite sources for factual claims by retrieving and linking to current web content"],"best_for":["developers building knowledge-intensive chatbots or research assistants","teams needing fact-checked responses with source attribution","applications requiring up-to-date information (news, finance, e-commerce)"],"limitations":["search quality depends on query generation — poorly-formed queries may retrieve irrelevant results","latency increases significantly due to network calls to search providers","search results may contain misinformation — model still relies on source credibility","no control over which search provider is used or result ranking strategy"],"requires":["API key for OpenRouter with search integration enabled","internet connectivity for search queries","tolerance for higher latency (search + synthesis adds 1-5 seconds per request)"],"input_types":["text (natural language query)","optional context about information freshness requirements"],"output_types":["text (synthesized answer with citations)","structured data (URLs, publication dates, source attribution)"],"categories":["search-retrieval","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-z-ai-glm-4-32b__cap_4","uri":"capability://data.processing.analysis.structured.data.extraction.and.schema.based.parsing","name":"structured data extraction and schema-based parsing","description":"Extracts structured information from unstructured text by mapping content to predefined schemas (JSON, tables, key-value pairs). The model understands semantic relationships and can normalize data, handle missing fields, and infer types based on context. Implementation uses instruction-tuning on extraction tasks combined with constrained decoding to ensure output conforms to specified schema, preventing hallucinated fields or type mismatches.","intents":["I need to extract entities (names, dates, amounts) from documents and return them as structured JSON","I want to parse natural language descriptions into database records with specific fields","I need to convert unstructured text into CSV or table format for downstream processing"],"best_for":["developers building data pipeline components that consume unstructured text","teams automating document processing or form extraction","builders creating ETL workflows that need semantic understanding"],"limitations":["extraction accuracy depends on schema clarity — ambiguous field definitions lead to errors","no validation of extracted values against business rules — requires post-processing","hallucination risk for missing fields — model may invent plausible but incorrect values","performance degrades on very long documents (>10K tokens) due to attention complexity"],"requires":["API key for OpenRouter","schema definition (JSON schema or similar format)","unstructured text input (documents, descriptions, logs)"],"input_types":["text (unstructured documents, descriptions, logs)","structured schema (JSON schema defining expected output)","optional examples (few-shot learning)"],"output_types":["structured data (JSON, CSV, key-value pairs)","confidence scores (optional, for validation)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-z-ai-glm-4-32b__cap_5","uri":"capability://code.generation.editing.code.debugging.and.error.analysis.with.contextual.suggestions","name":"code debugging and error analysis with contextual suggestions","description":"Analyzes code snippets or error messages to identify bugs, suggest fixes, and explain root causes. The model understands common error patterns, language-specific pitfalls, and debugging strategies. It generates corrected code, explains why the error occurred, and suggests preventive measures. Implementation leverages training on code repositories with bug fixes and error logs, enabling pattern recognition across languages and frameworks.","intents":["I have a runtime error and need the model to explain what went wrong and how to fix it","I want code review feedback that identifies potential bugs before they reach production","I need help understanding why my code isn't working and what the correct approach should be"],"best_for":["developers debugging code during development","teams using AI-assisted code review","learners understanding programming concepts through error analysis"],"limitations":["debugging accuracy depends on error message quality — cryptic or obfuscated errors may confuse the model","no access to runtime state or variable values — static analysis only","may suggest fixes that don't match project conventions or architecture","language-specific debugging knowledge varies — better for popular languages (Python, JavaScript, Java) than niche ones"],"requires":["API key for OpenRouter","code snippet or error message as input","optional: full stack trace, environment details, or reproduction steps"],"input_types":["code (buggy snippet or full function)","text (error messages, stack traces, logs)","structured metadata (language, framework, environment)"],"output_types":["code (corrected version with inline comments)","text (explanation of root cause and fix strategy)","structured suggestions (preventive measures, best practices)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-z-ai-glm-4-32b__cap_6","uri":"capability://text.generation.language.multi.language.translation.with.context.preservation","name":"multi-language translation with context preservation","description":"Translates text between 50+ language pairs while preserving semantic meaning, tone, and context. The model understands idioms, cultural references, and technical terminology, adapting translations to target audience and domain. Implementation uses multilingual transformer embeddings trained on parallel corpora, with special handling for code, proper nouns, and domain-specific terms to maintain accuracy across languages.","intents":["I need to translate documentation or user-facing content into multiple languages","I want to translate code comments and docstrings while preserving technical accuracy","I need to localize content for specific regions, adapting idioms and cultural references"],"best_for":["teams building multilingual products or documentation","developers localizing software for international markets","content creators reaching global audiences"],"limitations":["translation quality varies by language pair — high-resource pairs (English-Spanish) are better than low-resource pairs","idioms and cultural references may not translate perfectly — requires human review for marketing content","code translation may introduce subtle bugs if variable names or comments are critical to logic","no domain-specific terminology customization — generic translations may miss industry jargon"],"requires":["API key for OpenRouter","source text and target language specification","optional: domain context or terminology glossary"],"input_types":["text (any language, any domain)","code (with comments and docstrings)","structured metadata (source language, target language, domain)"],"output_types":["text (translated content in target language)","metadata (confidence scores, terminology notes)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-z-ai-glm-4-32b__cap_7","uri":"capability://planning.reasoning.mathematical.reasoning.and.symbolic.computation","name":"mathematical reasoning and symbolic computation","description":"Solves mathematical problems by breaking them into steps, showing work, and generating symbolic or numerical answers. The model understands algebra, calculus, statistics, and logic, reasoning through multi-step problems. Implementation combines language modeling with instruction-tuning on mathematical datasets, enabling step-by-step reasoning that can be verified and debugged by users.","intents":["I need to solve a math problem and see the step-by-step work, not just the final answer","I want to verify my mathematical reasoning or check if my approach is correct","I need to understand a mathematical concept by working through examples"],"best_for":["students learning mathematics with AI tutoring","developers building educational tools or homework helpers","researchers verifying mathematical derivations"],"limitations":["no symbolic computation engine — cannot simplify complex expressions or solve symbolic equations","reasoning is probabilistic — may make arithmetic errors or logical mistakes in complex proofs","limited to problems solvable through text-based reasoning — no graphical or visual problem-solving","performance degrades on very complex multi-step problems (>20 steps)"],"requires":["API key for OpenRouter","mathematical problem statement (text or LaTeX)","optional: context about problem domain or expected solution format"],"input_types":["text (problem statement in natural language or LaTeX)","structured data (equations, matrices, datasets)"],"output_types":["text (step-by-step solution with explanations)","structured data (numerical answers, symbolic expressions)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-z-ai-glm-4-32b__cap_8","uri":"capability://text.generation.language.creative.writing.and.content.generation.with.style.control","name":"creative writing and content generation with style control","description":"Generates original text content (stories, articles, marketing copy, poetry) in specified styles and tones. The model learns writing patterns from diverse sources and can adapt to different genres, audiences, and formats. Implementation uses instruction-tuning on writing datasets with style descriptors, enabling fine-grained control over tone, formality, and creative elements through prompt engineering.","intents":["I need to generate marketing copy or product descriptions that match my brand voice","I want to brainstorm creative ideas or outlines for content projects","I need to write in a specific style (formal, casual, poetic) and want AI assistance"],"best_for":["content creators and marketers generating bulk content","teams building writing assistants or creative tools","non-writers needing help with content creation"],"limitations":["generated content may be generic or lack originality — requires human editing for unique voice","style control is approximate — subtle tone variations may not match intent","no fact-checking — generated content may contain plausible-sounding but false claims","copyright concerns — model trained on existing content, may inadvertently reproduce copyrighted material"],"requires":["API key for OpenRouter","content prompt or outline","optional: style guide, tone descriptors, or examples"],"input_types":["text (prompt, outline, or topic)","structured metadata (style, tone, audience, format)"],"output_types":["text (generated content in specified style)","multiple variations (for A/B testing)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-z-ai-glm-4-32b__cap_9","uri":"capability://text.generation.language.conversational.question.answering.with.source.attribution","name":"conversational question-answering with source attribution","description":"Answers questions based on provided context (documents, knowledge bases, or conversation history) while attributing answers to specific sources. The model retrieves relevant information from context, synthesizes it into coherent answers, and cites sources to enable verification. Implementation combines context retrieval with answer generation, using attention mechanisms to track which parts of the context informed each part of the answer.","intents":["I want to ask questions about a document and get answers with citations to specific passages","I need a Q&A system that grounds answers in provided knowledge rather than hallucinating","I want to verify where the model got its information by seeing source attribution"],"best_for":["developers building document-based Q&A systems or chatbots","teams creating customer support assistants with knowledge bases","organizations needing fact-checked answers with audit trails"],"limitations":["answer quality depends on context quality — incomplete or biased context leads to poor answers","source attribution is approximate — model may cite wrong passages if context is ambiguous","no built-in retrieval — requires external system to fetch relevant context from large knowledge bases","context window limits how much information can be provided per query"],"requires":["API key for OpenRouter","context documents or knowledge base passages","question as input","optional: retrieval system to fetch relevant context"],"input_types":["text (question)","text (context documents or passages)","structured metadata (document titles, URLs, timestamps)"],"output_types":["text (answer with source citations)","structured data (source references with passage locations)"],"categories":["text-generation-language","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"low","permissions":["API access via OpenRouter or compatible endpoint","HTTP client capable of streaming or polling responses","conversation history management on client side","API key for OpenRouter or compatible LLM provider","code context (file, function signature, or snippet) as input","HTTP client for API calls","API key for OpenRouter","detailed instructions or task specification","optional: examples or reference implementations","API key for OpenRouter or compatible provider"],"failure_modes":["context window is finite — very long conversations (>32K tokens) may lose early context","no persistent memory across separate conversation sessions","attention mechanism adds latency proportional to conversation length","no real-time AST validation — generated code may have syntax errors in edge cases","limited to patterns seen in training data — novel or very recent library APIs may be incomplete","no built-in refactoring or optimization — generated code may not follow project style guides","instruction following is probabilistic — complex or ambiguous instructions may be misinterpreted","no persistent state across separate API calls — requires client-side state management","task decomposition may miss dependencies or create suboptimal execution order","error recovery is limited — no built-in retry logic or fallback strategies","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.47,"ecosystem":0.24,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.059Z","last_scraped_at":"2026-05-03T15:20:45.776Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=z-ai-glm-4-32b","compare_url":"https://unfragile.ai/compare?artifact=z-ai-glm-4-32b"}},"signature":"UMay5ubwJ+PKazADBmj4ALu/CBTuiYg3uEsd49RmMbIya6ZzBYCq87ypeiW2yrGl1KXxK0bZ+AJXf1p3g7zRCA==","signedAt":"2026-06-20T21:40:28.763Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/z-ai-glm-4-32b","artifact":"https://unfragile.ai/z-ai-glm-4-32b","verify":"https://unfragile.ai/api/v1/verify?slug=z-ai-glm-4-32b","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}