{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-z-ai-glm-5","slug":"z-ai-glm-5","name":"Z.ai: GLM 5","type":"model","url":"https://openrouter.ai/models/z-ai~glm-5","page_url":"https://unfragile.ai/z-ai-glm-5","categories":["model-training","deployment-infra"],"tags":["z-ai","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$6.00e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-z-ai-glm-5__cap_0","uri":"capability://code.generation.editing.long.context.code.generation.with.architectural.awareness","name":"long-context code generation with architectural awareness","description":"GLM-5 processes extended code contexts (supporting multi-file projects and large codebases) while maintaining semantic understanding of system architecture through attention mechanisms optimized for code structure. The model uses specialized tokenization for programming languages and maintains coherence across thousands of tokens of code context, enabling generation of complex features that respect existing patterns and dependencies.","intents":["Generate large features that span multiple files while respecting existing codebase architecture","Complete complex refactoring tasks that require understanding of system-wide dependencies","Implement new modules that integrate seamlessly with large legacy codebases","Generate production-grade code for systems with intricate interdependencies"],"best_for":["Expert developers building large-scale systems with complex architectures","Teams maintaining codebases exceeding 100k lines of code","Organizations requiring production-grade code generation without external API calls"],"limitations":["Context window size not explicitly specified — may have practical limits on total codebase size that can be processed in single request","Performance degrades with extremely large context windows due to quadratic attention complexity","Requires careful prompt engineering to maintain architectural consistency across long generations"],"requires":["API access via OpenRouter or direct Z.ai endpoint","Sufficient context budget for multi-file code samples","Integration with IDE or code editor for practical use"],"input_types":["code snippets","full source files","multi-file project structures","architectural documentation","natural language specifications"],"output_types":["code (multiple languages)","refactored code","architectural patterns","implementation plans"],"categories":["code-generation-editing","architecture-aware-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-z-ai-glm-5__cap_1","uri":"capability://planning.reasoning.multi.turn.agent.reasoning.with.tool.integration","name":"multi-turn agent reasoning with tool integration","description":"GLM-5 supports extended reasoning chains for agentic workflows through structured prompt patterns that enable step-by-step decomposition of complex tasks. The model can maintain state across multiple turns, reason about tool outputs, and make decisions about next actions — designed for long-horizon agent loops where the model must plan, execute, observe, and adapt across dozens of steps.","intents":["Build autonomous agents that solve multi-step problems requiring tool use and adaptation","Implement code generation agents that iteratively refine solutions based on test feedback","Create research agents that search, analyze, and synthesize information across multiple sources","Develop debugging agents that hypothesize, test, and refine solutions"],"best_for":["Developers building autonomous agents for code generation or system design","Teams implementing agentic workflows that require extended reasoning","Organizations needing on-premise or API-based agent execution without vendor lock-in"],"limitations":["Agent reasoning quality depends heavily on prompt engineering and tool definitions","No built-in memory persistence — requires external state management for long-running agents","Token consumption grows linearly with reasoning steps, making very long agent chains expensive","Tool calling format not explicitly documented — may require custom integration layer"],"requires":["API access via OpenRouter or Z.ai endpoint","Tool/function definitions in compatible format (likely JSON schema)","External state management system for multi-turn persistence","Monitoring infrastructure for long-running agent loops"],"input_types":["natural language task descriptions","tool/function schemas","previous turn outputs","observation data from tool execution"],"output_types":["reasoning traces","tool calls with parameters","final solutions","decision logs"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-z-ai-glm-5__cap_10","uri":"capability://code.generation.editing.performance.optimization.and.bottleneck.identification","name":"performance optimization and bottleneck identification","description":"GLM-5 analyzes code for performance bottlenecks and suggests optimization strategies through understanding of algorithmic complexity, memory management, and system-level performance patterns. The model can identify inefficient algorithms, suggest data structure improvements, and recommend caching or parallelization strategies — enabling targeted performance improvements with understanding of trade-offs.","intents":["Identify performance bottlenecks in existing code and suggest optimizations","Suggest algorithmic improvements and data structure changes for better performance","Recommend caching, parallelization, or batching strategies","Generate optimized implementations of performance-critical code"],"best_for":["Teams optimizing performance-critical systems","Developers identifying bottlenecks in existing code","Organizations improving system scalability and efficiency"],"limitations":["Optimization suggestions are theoretical — require profiling and benchmarking to validate","May not account for hardware-specific constraints or platform-specific optimizations","Trade-offs between performance, maintainability, and readability require expert judgment","Parallelization suggestions may introduce concurrency bugs requiring careful implementation"],"requires":["API access via OpenRouter or Z.ai endpoint","Source code to analyze","Optional: performance metrics or profiling data"],"input_types":["source code","performance metrics","profiling data","performance requirements","scalability targets"],"output_types":["optimization suggestions","optimized code","algorithmic improvements","performance analysis","trade-off analysis"],"categories":["code-generation-editing","performance-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-z-ai-glm-5__cap_11","uri":"capability://code.generation.editing.api.design.and.specification.generation","name":"api design and specification generation","description":"GLM-5 generates comprehensive API specifications, including endpoint definitions, request/response schemas, error handling, and usage examples through understanding of API design best practices and REST/GraphQL patterns. The model can produce OpenAPI/Swagger specifications, generate API documentation, and suggest design improvements — enabling rapid API specification and documentation.","intents":["Generate OpenAPI/Swagger specifications from code or requirements","Create comprehensive API documentation with examples and error handling","Design APIs that follow REST or GraphQL best practices","Generate client libraries and SDKs from API specifications"],"best_for":["Teams designing APIs and generating specifications","Organizations standardizing on API design patterns","Developers rapidly prototyping and documenting APIs"],"limitations":["Generated specifications require validation against actual implementation","May not account for organization-specific API design standards","Authentication and authorization patterns may require customization","Performance and rate-limiting specifications require domain expertise"],"requires":["API access via OpenRouter or Z.ai endpoint","API requirements or existing implementation","Optional: existing API design standards or patterns"],"input_types":["API requirements","source code","data models","existing API examples","design patterns"],"output_types":["OpenAPI/Swagger specifications","API documentation","request/response examples","error handling specifications","client library stubs"],"categories":["code-generation-editing","api-design"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-z-ai-glm-5__cap_2","uri":"capability://text.generation.language.expert.level.technical.writing.and.documentation.generation","name":"expert-level technical writing and documentation generation","description":"GLM-5 generates high-quality technical documentation, design documents, and architectural specifications through training on expert-level technical writing patterns. The model understands domain-specific terminology, maintains consistency across long documents, and can generate structured documentation (API specs, RFC-style documents, architecture decision records) with appropriate technical depth and precision.","intents":["Generate comprehensive API documentation from code signatures and docstrings","Create architecture decision records (ADRs) that explain complex design choices","Write detailed technical specifications for complex systems","Produce RFC-style proposals for system design with trade-off analysis"],"best_for":["Technical teams documenting complex systems for internal and external audiences","Organizations standardizing on expert-level documentation practices","Solo developers and small teams lacking dedicated technical writers"],"limitations":["Documentation quality depends on input code quality and clarity of specifications","May require multiple iterations to achieve publication-ready output","Specialized domain knowledge (e.g., cryptography, distributed systems) may require expert review","Consistency across very long documents (100+ pages) not guaranteed without explicit constraints"],"requires":["API access via OpenRouter or Z.ai endpoint","Source code or detailed specifications as input","Domain expertise for review and validation of generated documentation"],"input_types":["source code with comments","API signatures","architectural diagrams (as text descriptions)","design specifications","existing documentation templates"],"output_types":["markdown documentation","API specifications","architecture decision records","technical proposals","structured documentation"],"categories":["text-generation-language","technical-writing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-z-ai-glm-5__cap_3","uri":"capability://planning.reasoning.complex.problem.decomposition.and.planning","name":"complex problem decomposition and planning","description":"GLM-5 breaks down complex, ambiguous problems into structured task hierarchies and implementation plans through chain-of-thought reasoning patterns. The model can identify dependencies, suggest phased approaches, and generate detailed step-by-step plans for tackling large engineering challenges — useful for translating high-level requirements into actionable development roadmaps.","intents":["Decompose vague product requirements into concrete technical tasks and milestones","Generate implementation plans for complex system redesigns with dependency analysis","Create phased rollout strategies for large-scale infrastructure changes","Break down ambiguous feature requests into specific, implementable subtasks"],"best_for":["Technical leads planning complex projects and system redesigns","Teams transitioning from waterfall to iterative development","Organizations needing structured planning for large engineering initiatives"],"limitations":["Plan quality depends on clarity and completeness of input requirements","May miss domain-specific constraints or organizational constraints not explicitly stated","Estimates and timelines are not provided — requires expert judgment to add","Plans may not account for team capacity, skill levels, or organizational constraints"],"requires":["API access via OpenRouter or Z.ai endpoint","Clear problem statement or requirements document","Domain expertise to validate and refine generated plans"],"input_types":["problem statements","requirements documents","system specifications","existing architecture descriptions","constraints and limitations"],"output_types":["task hierarchies","implementation plans","dependency graphs","phased approaches","risk analyses"],"categories":["planning-reasoning","task-decomposition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-z-ai-glm-5__cap_4","uri":"capability://code.generation.editing.code.review.and.quality.analysis.with.architectural.feedback","name":"code review and quality analysis with architectural feedback","description":"GLM-5 analyzes code for quality issues, architectural violations, and design improvements through patterns learned from expert code review practices. The model can identify performance bottlenecks, suggest refactoring opportunities, flag architectural inconsistencies, and provide detailed feedback on code quality — going beyond simple linting to understand design intent and system-wide implications.","intents":["Perform automated code reviews that identify architectural violations and design issues","Suggest refactoring opportunities that improve code maintainability and performance","Identify performance bottlenecks and suggest optimization strategies","Validate that new code adheres to established architectural patterns"],"best_for":["Teams implementing code review automation and quality gates","Organizations standardizing on architectural patterns and design principles","Solo developers seeking expert-level code review feedback"],"limitations":["Review quality depends on code clarity and presence of architectural documentation","May miss context-specific optimizations or business constraints","Cannot validate runtime behavior or performance without execution data","Suggestions may conflict with team preferences or organizational standards"],"requires":["API access via OpenRouter or Z.ai endpoint","Source code to review","Optional: architectural documentation or design patterns guide"],"input_types":["source code","code diffs","architectural specifications","design pattern documentation","performance requirements"],"output_types":["code review comments","refactoring suggestions","performance analysis","architectural feedback","quality metrics"],"categories":["code-generation-editing","quality-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-z-ai-glm-5__cap_5","uri":"capability://code.generation.editing.cross.language.code.translation.with.semantic.preservation","name":"cross-language code translation with semantic preservation","description":"GLM-5 translates code between programming languages while preserving semantic meaning and adapting to language-specific idioms. The model understands language-specific patterns, libraries, and best practices, enabling translation that produces idiomatic code rather than mechanical line-by-line conversions — useful for migrating systems across language ecosystems or supporting polyglot architectures.","intents":["Migrate codebases from one language to another while maintaining functionality","Translate algorithms or utilities to different languages for integration","Generate language-specific implementations from language-agnostic specifications","Support polyglot systems by translating between supported languages"],"best_for":["Teams migrating systems between language ecosystems","Organizations supporting polyglot architectures","Developers integrating code across multiple programming languages"],"limitations":["Translation quality varies significantly by language pair and code complexity","Language-specific libraries and frameworks may not have direct equivalents","Performance characteristics may differ significantly across languages","Requires expert review to validate semantic equivalence and idiomatic correctness"],"requires":["API access via OpenRouter or Z.ai endpoint","Source code in supported language","Expertise in both source and target languages for validation"],"input_types":["source code","code snippets","algorithms","library usage patterns"],"output_types":["translated code","idiomatic implementations","library mappings","migration guides"],"categories":["code-generation-editing","translation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-z-ai-glm-5__cap_6","uri":"capability://planning.reasoning.system.design.and.architecture.specification.generation","name":"system design and architecture specification generation","description":"GLM-5 generates detailed system architecture specifications, design documents, and technical specifications for complex systems through understanding of distributed systems patterns, scalability principles, and architectural trade-offs. The model can produce specifications that include component diagrams, data flow descriptions, scalability analysis, and failure mode discussions — enabling teams to move from high-level requirements to detailed architectural blueprints.","intents":["Generate detailed system architecture specifications from high-level requirements","Create scalability and reliability analysis for proposed system designs","Produce component interaction diagrams and data flow specifications","Generate failure mode and recovery strategy documentation"],"best_for":["Technical architects designing complex distributed systems","Teams planning large-scale infrastructure projects","Organizations standardizing on architectural patterns and design principles"],"limitations":["Generated specifications require expert review and validation","May not account for organization-specific constraints or legacy system integration","Scalability estimates are theoretical and require load testing validation","Assumes standard cloud infrastructure patterns — may not apply to specialized environments"],"requires":["API access via OpenRouter or Z.ai endpoint","High-level requirements and constraints","Domain expertise for validation and refinement"],"input_types":["requirements documents","functional specifications","non-functional requirements","constraints and limitations","existing system descriptions"],"output_types":["architecture specifications","component diagrams (as text descriptions)","data flow specifications","scalability analysis","reliability and failure mode analysis"],"categories":["planning-reasoning","architecture-design"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-z-ai-glm-5__cap_7","uri":"capability://code.generation.editing.natural.language.to.code.synthesis.with.specification.fidelity","name":"natural language to code synthesis with specification fidelity","description":"GLM-5 converts detailed natural language specifications into executable code through understanding of both natural language semantics and programming language syntax. The model maintains fidelity to specifications while generating idiomatic, production-grade code — useful for rapid prototyping, specification-driven development, and automating routine implementation tasks.","intents":["Generate complete implementations from detailed natural language specifications","Rapidly prototype features from product requirements","Automate routine implementation tasks from specifications","Generate boilerplate and scaffolding code from architectural specifications"],"best_for":["Teams practicing specification-driven development","Rapid prototyping and MVP development","Organizations automating routine implementation tasks"],"limitations":["Specification clarity directly impacts code quality — vague specs produce poor code","Generated code requires testing and validation before production use","Complex business logic may require multiple iterations to achieve correct implementation","Performance optimization typically requires manual tuning after generation"],"requires":["API access via OpenRouter or Z.ai endpoint","Detailed natural language specifications","Testing infrastructure for validation"],"input_types":["natural language specifications","functional requirements","API specifications","algorithm descriptions","data structure specifications"],"output_types":["executable code","function implementations","class definitions","API implementations","test scaffolding"],"categories":["code-generation-editing","specification-driven"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-z-ai-glm-5__cap_8","uri":"capability://code.generation.editing.debugging.and.error.diagnosis.with.root.cause.analysis","name":"debugging and error diagnosis with root cause analysis","description":"GLM-5 analyzes error messages, stack traces, and failing code to identify root causes and suggest fixes through understanding of common bug patterns and debugging methodologies. The model can trace through code execution paths, identify logic errors, and suggest targeted fixes — going beyond simple error matching to understand the underlying problem and context.","intents":["Diagnose root causes of errors from stack traces and error messages","Suggest targeted fixes for failing code with explanation of root cause","Identify logic errors and edge cases in implementation","Generate test cases that expose and validate fixes for identified bugs"],"best_for":["Developers debugging complex systems and hard-to-reproduce issues","Teams implementing automated debugging and error diagnosis","Organizations reducing time-to-resolution for production issues"],"limitations":["Diagnosis accuracy depends on quality and completeness of error information","May miss context-specific issues or race conditions in concurrent code","Cannot diagnose issues without sufficient error context or stack traces","Suggested fixes require validation and testing before deployment"],"requires":["API access via OpenRouter or Z.ai endpoint","Error messages, stack traces, or failing code","Optional: logs, metrics, or reproduction steps"],"input_types":["error messages","stack traces","failing code snippets","logs and metrics","reproduction steps"],"output_types":["root cause analysis","suggested fixes","explanation of issues","test cases","debugging strategies"],"categories":["code-generation-editing","debugging"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-z-ai-glm-5__cap_9","uri":"capability://code.generation.editing.test.generation.and.test.case.synthesis","name":"test generation and test case synthesis","description":"GLM-5 generates comprehensive test cases from code, specifications, or requirements through understanding of testing methodologies and edge case patterns. The model can produce unit tests, integration tests, and edge case tests that achieve high coverage and validate both happy paths and error conditions — automating routine test writing while maintaining test quality.","intents":["Generate comprehensive unit tests from function signatures and implementations","Create integration tests from system specifications and component interactions","Identify and generate tests for edge cases and error conditions","Generate test fixtures and mock data for complex test scenarios"],"best_for":["Teams improving test coverage and automating test writing","Organizations implementing test-driven development practices","Developers rapidly prototyping with comprehensive test coverage"],"limitations":["Generated tests require review to ensure they test meaningful behavior","May miss domain-specific edge cases or business logic constraints","Performance and load testing typically requires manual implementation","Test quality depends on code clarity and specification completeness"],"requires":["API access via OpenRouter or Z.ai endpoint","Source code or specifications to test","Testing framework and dependencies"],"input_types":["source code","function signatures","specifications","requirements documents","existing test examples"],"output_types":["unit tests","integration tests","test fixtures","mock data","test coverage analysis"],"categories":["code-generation-editing","testing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["API access via OpenRouter or direct Z.ai endpoint","Sufficient context budget for multi-file code samples","Integration with IDE or code editor for practical use","API access via OpenRouter or Z.ai endpoint","Tool/function definitions in compatible format (likely JSON schema)","External state management system for multi-turn persistence","Monitoring infrastructure for long-running agent loops","Source code to analyze","Optional: performance metrics or profiling data","API requirements or existing implementation"],"failure_modes":["Context window size not explicitly specified — may have practical limits on total codebase size that can be processed in single request","Performance degrades with extremely large context windows due to quadratic attention complexity","Requires careful prompt engineering to maintain architectural consistency across long generations","Agent reasoning quality depends heavily on prompt engineering and tool definitions","No built-in memory persistence — requires external state management for long-running agents","Token consumption grows linearly with reasoning steps, making very long agent chains expensive","Tool calling format not explicitly documented — may require custom integration layer","Optimization suggestions are theoretical — require profiling and benchmarking to validate","May not account for hardware-specific constraints or platform-specific optimizations","Trade-offs between performance, maintainability, and readability require expert judgment","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.49,"ecosystem":0.34,"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-5","compare_url":"https://unfragile.ai/compare?artifact=z-ai-glm-5"}},"signature":"f16JVjeSo+yRTULlOYln7dxHpTibEjl82dlrboyVJBN1tplbNglmJNGhWfOhdNZ3Dn1oFbogRtMagP6I7ADDDg==","signedAt":"2026-06-20T02:44:49.470Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/z-ai-glm-5","artifact":"https://unfragile.ai/z-ai-glm-5","verify":"https://unfragile.ai/api/v1/verify?slug=z-ai-glm-5","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"}}