{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-qwen-qwen3-coder-flash","slug":"qwen-qwen3-coder-flash","name":"Qwen: Qwen3 Coder Flash","type":"model","url":"https://openrouter.ai/models/qwen~qwen3-coder-flash","page_url":"https://unfragile.ai/qwen-qwen3-coder-flash","categories":["ai-agents"],"tags":["qwen","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$1.95e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-qwen-qwen3-coder-flash__cap_0","uri":"capability://tool.use.integration.autonomous.code.generation.via.tool.calling","name":"autonomous-code-generation-via-tool-calling","description":"Generates code by autonomously invoking external tools and APIs through a schema-based function-calling interface. The model receives tool definitions, decides which tools to invoke based on code context, executes them, and iteratively refines code based on tool outputs. This enables multi-step programming workflows where the model can fetch APIs, run tests, or query documentation without human intervention between steps.","intents":["I want the model to write code that calls external APIs by automatically discovering and invoking the right endpoints","I need the model to generate code, run it, see errors, and fix them in a loop without my manual intervention","I want to build an autonomous agent that can scaffold a full project by calling build tools, package managers, and linters"],"best_for":["teams building autonomous coding agents that need to execute multi-step workflows","developers creating self-healing code generation pipelines with feedback loops","builders prototyping LLM-powered development environments with tool integration"],"limitations":["Tool-calling overhead adds latency per invocation cycle — each tool call requires model inference, tool execution, and response parsing","No built-in persistence of tool state across sessions — requires external state management for long-running workflows","Tool schema complexity can degrade performance if >20 tools are registered simultaneously","Requires explicit tool definitions in OpenAI/Anthropic function-calling format — no automatic schema inference from arbitrary APIs"],"requires":["API key for OpenAI, Anthropic, or compatible LLM provider supporting function calling","Tool definitions in JSON Schema format (OpenAI function calling spec or Anthropic tool_use format)","HTTP/REST endpoints or local executables for tools to be invoked","Network connectivity for remote tool execution"],"input_types":["text (natural language task description)","code (existing codebase context)","structured tool definitions (JSON Schema)"],"output_types":["code (generated or modified source files)","structured tool calls (function name + arguments)","execution logs (tool invocation history and results)"],"categories":["tool-use-integration","code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-coder-flash__cap_1","uri":"capability://code.generation.editing.multi.language.code.generation.with.syntax.awareness","name":"multi-language-code-generation-with-syntax-awareness","description":"Generates syntactically correct code across 40+ programming languages by leveraging language-specific training data and syntax-aware token prediction. The model understands language-specific idioms, standard library patterns, and framework conventions, producing code that compiles/runs without syntax errors. It handles language-specific features like type systems, async patterns, and module imports with contextual awareness rather than template-based generation.","intents":["I need to generate production-ready code in Python, JavaScript, Go, Rust, and Java from a single prompt without manual syntax fixes","I want the model to understand language-specific best practices and generate idiomatic code, not just syntactically valid code","I need to scaffold multi-language microservices where each service is generated in its optimal language"],"best_for":["polyglot development teams working across multiple languages","platform teams building code generation tools that must support diverse tech stacks","developers migrating or refactoring code across languages"],"limitations":["Accuracy varies by language — well-represented languages (Python, JavaScript, Java) have higher correctness than niche languages (Elixir, Clojure)","No real-time syntax validation — generated code may have subtle semantic errors that require testing to catch","Context window limits multi-file generation — cannot generate large monorepos in a single invocation","Language-specific libraries and frameworks not in training data may be hallucinated or incorrectly used"],"requires":["API key for Qwen3 Coder Flash via OpenRouter or direct Alibaba endpoint","Language specification in prompt (e.g., 'Generate Python 3.11 code')","Target language compiler/runtime for validation (optional but recommended)"],"input_types":["text (natural language description of code to generate)","code (existing code snippets for context or refactoring)","structured specifications (API schemas, type definitions)"],"output_types":["code (source files in specified language)","structured code metadata (function signatures, type annotations)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-coder-flash__cap_10","uri":"capability://code.generation.editing.natural.language.to.code.translation.with.intent.preservation","name":"natural-language-to-code-translation-with-intent-preservation","description":"Translates natural language descriptions into executable code by understanding intent and generating implementations that match the described behavior. The model parses natural language to extract requirements, identifies appropriate algorithms and data structures, and generates code that implements the described functionality. It handles ambiguity by asking clarifying questions or generating multiple implementations for the user to choose from.","intents":["I want to describe what I want in plain English and have the model generate working code","I need to convert pseudocode or algorithm descriptions into actual implementations","I want to generate code from requirements without specifying implementation details"],"best_for":["non-technical stakeholders who want to generate code from descriptions","developers rapidly prototyping ideas without detailed implementation planning","teams using natural language as a specification format"],"limitations":["Ambiguous requirements may result in incorrect implementations — requires clarification","Generated code may not match intended behavior if natural language description is unclear","No validation that generated code matches requirements — requires testing","Performance characteristics may not match expectations (e.g., O(n²) algorithm when O(n) is needed)","Complex requirements may be misinterpreted or partially implemented"],"requires":["API key for Qwen3 Coder Flash","Natural language description of desired functionality","Optional: target language specification"],"input_types":["text (natural language description of desired code behavior)","code (pseudocode or algorithm description)"],"output_types":["code (generated implementation)","text (explanation of implementation approach)"],"categories":["code-generation-editing","text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-coder-flash__cap_11","uri":"capability://code.generation.editing.debugging.assistance.with.root.cause.analysis","name":"debugging-assistance-with-root-cause-analysis","description":"Assists with debugging by analyzing error messages, stack traces, and code to identify root causes and suggest fixes. The model understands common bug patterns, runtime errors, and exception types, generating hypotheses about what caused the error and suggesting debugging steps or code fixes. It can analyze logs, error messages, and code context to pinpoint issues that might not be obvious from the error message alone.","intents":["I have an error message and stack trace — help me understand what went wrong and how to fix it","I want to understand why my code is behaving unexpectedly and get suggestions for debugging","I need to analyze logs and error messages to identify the root cause of production issues"],"best_for":["developers debugging complex issues with unclear error messages","teams analyzing production logs to identify root causes","developers learning to debug by understanding error patterns"],"limitations":["Cannot debug issues that require runtime inspection (e.g., memory leaks, race conditions)","Suggestions may be incorrect if error message is misleading or incomplete","No access to actual runtime state — debugging is based on static analysis and error messages","Complex multi-threaded or distributed system bugs may be difficult to diagnose","Domain-specific errors may be misinterpreted without context"],"requires":["API key for Qwen3 Coder Flash","Error message or stack trace","Source code context (optional but recommended)"],"input_types":["text (error message, stack trace, or log output)","code (source code context)","structured specifications (runtime environment, dependencies)"],"output_types":["text (root cause analysis and explanation)","code (suggested fixes or debugging steps)","structured debugging steps (step-by-step debugging guide)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-coder-flash__cap_12","uri":"capability://code.generation.editing.performance.optimization.with.profiling.insights","name":"performance-optimization-with-profiling-insights","description":"Optimizes code performance by analyzing profiling data and identifying bottlenecks, then suggesting algorithmic improvements, data structure changes, or implementation optimizations. The model understands performance characteristics of algorithms and data structures, can identify inefficient patterns (N+1 queries, unnecessary allocations, inefficient loops), and generates optimized code with explanations of performance improvements.","intents":["I have profiling data showing my code is slow — help me identify bottlenecks and optimize","I want to improve the performance of my algorithm without changing its behavior","I need to identify inefficient patterns in my code (N+1 queries, unnecessary allocations, etc.)"],"best_for":["developers optimizing performance-critical code","teams analyzing profiling data to identify bottlenecks","developers learning performance optimization techniques"],"limitations":["Optimization suggestions may not be optimal for specific hardware or runtime characteristics","Premature optimization may degrade code readability — requires balancing performance and maintainability","Profiling data may be incomplete or misleading — optimization based on incomplete data may not improve actual performance","Algorithmic optimizations may require significant refactoring with risk of introducing bugs","Cache behavior and memory access patterns are difficult to optimize without detailed profiling"],"requires":["API key for Qwen3 Coder Flash","Source code to optimize","Optional: profiling data (CPU profile, memory profile, execution traces)"],"input_types":["code (source code to optimize)","text (performance requirements or bottleneck description)","structured specifications (profiling data, performance metrics)"],"output_types":["code (optimized implementation)","text (explanation of optimizations and expected performance improvements)","structured metrics (estimated performance improvement, complexity analysis)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-coder-flash__cap_2","uri":"capability://code.generation.editing.context.aware.code.completion.with.codebase.indexing","name":"context-aware-code-completion-with-codebase-indexing","description":"Completes code by analyzing the full codebase context, including imported modules, function signatures, type definitions, and architectural patterns. The model receives indexed codebase metadata (AST summaries, symbol tables, dependency graphs) and uses this to generate completions that respect existing code structure and conventions. This enables completions that are not just syntactically valid but semantically aligned with the project's architecture.","intents":["I want code completion that understands my project's architecture and suggests completions consistent with my codebase patterns","I need the model to complete code that correctly imports and uses functions defined elsewhere in my project","I want to generate code that follows my team's naming conventions and architectural patterns without manual specification"],"best_for":["development teams with large, complex codebases (>100K LOC) where architectural consistency matters","teams using code generation in IDEs where codebase context is readily available","builders creating AI-powered refactoring tools that must preserve architectural intent"],"limitations":["Requires codebase indexing overhead — initial indexing can take 30-60 seconds for large projects","Index staleness — if codebase changes frequently, cached index may become stale and produce incorrect completions","Memory overhead — maintaining full codebase context in model context window limits scalability to projects >5MB of source code","No cross-repository context — cannot leverage patterns from external dependencies unless explicitly provided"],"requires":["Codebase indexing tool (e.g., tree-sitter, Roslyn, or custom AST parser)","Symbol table extraction from codebase","API key for Qwen3 Coder Flash","IDE integration or code editor plugin for context injection"],"input_types":["text (partial code to complete)","structured codebase metadata (symbol tables, AST summaries, dependency graphs)","code (surrounding context from current file)"],"output_types":["code (completed code snippet)","structured suggestions (ranked completion options with confidence scores)"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-coder-flash__cap_3","uri":"capability://code.generation.editing.code.refactoring.with.semantic.preservation","name":"code-refactoring-with-semantic-preservation","description":"Refactors code by understanding semantic intent and preserving behavior while improving structure, readability, or performance. The model analyzes code to identify refactoring opportunities (extract functions, rename variables, simplify logic, modernize syntax) and generates refactored code with explanations of changes. It validates refactoring by comparing input/output semantics rather than just syntax, ensuring behavior is preserved.","intents":["I want to modernize legacy Python 2 code to Python 3 idioms while preserving all behavior","I need to extract common patterns from duplicated code into reusable functions","I want to improve code readability by renaming variables and functions to match project conventions"],"best_for":["teams maintaining legacy codebases that need modernization without behavior changes","developers preparing code for code review by improving structure before submission","technical leads automating code quality improvements across large teams"],"limitations":["Cannot guarantee semantic equivalence for complex logic — refactoring may introduce subtle behavioral changes in edge cases","No automatic testing integration — refactored code requires test validation to confirm behavior preservation","Limited to single-file refactoring — cross-file refactoring (e.g., moving functions between modules) requires manual coordination","Performance optimizations may be incorrect for specific runtime characteristics (e.g., memory constraints, concurrency patterns)"],"requires":["API key for Qwen3 Coder Flash","Source code in supported language","Test suite for validation (recommended but not required)","Refactoring specification or intent description"],"input_types":["code (source code to refactor)","text (refactoring intent, e.g., 'modernize to Python 3.11')","structured specifications (target language version, style guide)"],"output_types":["code (refactored source code)","text (explanation of refactoring changes)","structured diff (before/after comparison)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-coder-flash__cap_4","uri":"capability://code.generation.editing.code.review.and.bug.detection.with.pattern.matching","name":"code-review-and-bug-detection-with-pattern-matching","description":"Reviews code by identifying bugs, security vulnerabilities, performance issues, and style violations through pattern matching and semantic analysis. The model analyzes code against known anti-patterns, security risks (SQL injection, XSS, buffer overflows), and performance pitfalls, generating detailed feedback with explanations and suggested fixes. It learns from training data containing real bug reports and security advisories to identify issues that static analysis tools might miss.","intents":["I want automated code review that catches security vulnerabilities and performance issues before human review","I need to identify common bug patterns in my codebase (null pointer dereferences, off-by-one errors, race conditions)","I want to enforce coding standards and style guidelines across my team without manual review overhead"],"best_for":["teams implementing automated code review pipelines in CI/CD","security-focused teams needing AI-assisted vulnerability detection","development teams with high code volume where manual review is a bottleneck"],"limitations":["False positive rate ~15-25% — requires human validation of flagged issues","Cannot detect logic errors that require domain knowledge (e.g., incorrect business logic)","Limited to code patterns in training data — novel vulnerabilities or anti-patterns may be missed","No runtime context — cannot detect issues that only manifest under specific execution conditions","Language-specific rules may be incomplete for niche languages or frameworks"],"requires":["API key for Qwen3 Coder Flash","Source code in supported language","Optional: coding standards or style guide for context","Optional: security policy or vulnerability database for custom rules"],"input_types":["code (source code to review)","text (review criteria or focus areas)","structured specifications (security policies, style guides)"],"output_types":["text (detailed code review feedback)","structured issues (bug/vulnerability list with severity, location, and suggested fixes)","code (suggested fixes or improvements)"],"categories":["code-generation-editing","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-coder-flash__cap_5","uri":"capability://code.generation.editing.test.generation.with.coverage.optimization","name":"test-generation-with-coverage-optimization","description":"Generates unit tests, integration tests, and edge-case tests by analyzing code structure and identifying untested code paths. The model understands test frameworks (pytest, Jest, JUnit), generates tests with proper setup/teardown, mocking, and assertions, and optimizes for code coverage. It identifies edge cases, boundary conditions, and error scenarios that human developers might miss, generating tests that catch real bugs.","intents":["I want to automatically generate unit tests for my functions with good coverage without writing boilerplate","I need to identify and test edge cases and boundary conditions that my manual tests might miss","I want to generate integration tests that validate interactions between multiple components"],"best_for":["teams with low test coverage that need rapid test generation to improve coverage metrics","developers writing new code who want to generate tests alongside implementation","teams adopting TDD practices and needing test scaffolding"],"limitations":["Generated tests may not reflect actual business requirements — tests validate code behavior, not correctness","Mock setup can be incorrect for complex dependencies — requires manual validation of mocks","Cannot generate tests for code with external dependencies (databases, APIs) without explicit mocking setup","Test quality varies — generated tests may have low semantic value (e.g., testing trivial getters)","No understanding of test performance — generated tests may be slow or flaky"],"requires":["API key for Qwen3 Coder Flash","Source code in supported language","Test framework specification (pytest, Jest, JUnit, etc.)","Optional: existing test examples for style matching"],"input_types":["code (source code to test)","text (test requirements or focus areas)","structured specifications (test framework, coverage targets)"],"output_types":["code (generated test files)","structured test metadata (test names, coverage estimates, edge cases identified)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-coder-flash__cap_6","uri":"capability://text.generation.language.documentation.generation.from.code","name":"documentation-generation-from-code","description":"Generates API documentation, docstrings, and README files by analyzing code structure and inferring intent. The model examines function signatures, type annotations, and code logic to generate accurate documentation that explains what code does, how to use it, and what parameters/return values mean. It supports multiple documentation formats (Markdown, reStructuredText, Javadoc) and can generate documentation at different levels of detail.","intents":["I want to automatically generate docstrings for all functions in my codebase without manual effort","I need to create API documentation from my code that stays in sync with implementation","I want to generate README files with usage examples and API reference"],"best_for":["teams with undocumented legacy code that needs documentation","open-source projects needing to maintain documentation alongside code","developers who want to generate documentation as part of CI/CD pipeline"],"limitations":["Generated documentation may be inaccurate if code intent is not obvious from structure","Cannot infer business logic or domain-specific context from code alone","Documentation quality depends on code clarity — poorly written code generates poor documentation","No automatic validation that documentation matches actual behavior","May generate verbose or redundant documentation for simple code"],"requires":["API key for Qwen3 Coder Flash","Source code in supported language","Optional: documentation style guide or template","Optional: existing documentation examples for style matching"],"input_types":["code (source code to document)","text (documentation style or format preference)","structured specifications (documentation template, detail level)"],"output_types":["text (generated documentation in Markdown, reStructuredText, or other format)","code (generated docstrings in language-specific format)"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-coder-flash__cap_7","uri":"capability://code.generation.editing.sql.query.generation.and.optimization","name":"sql-query-generation-and-optimization","description":"Generates SQL queries from natural language descriptions and optimizes existing queries for performance. The model understands database schemas, relationships, and query patterns, generating queries that are syntactically correct and semantically appropriate for the schema. It can optimize queries by suggesting indexes, rewriting subqueries, and identifying N+1 query problems in application code.","intents":["I want to generate SQL queries from natural language descriptions without writing SQL manually","I need to optimize slow queries by identifying missing indexes and rewriting inefficient patterns","I want to validate that my application code is generating efficient queries"],"best_for":["developers building data-heavy applications who want to generate queries quickly","database administrators optimizing query performance","teams implementing query generation in ORMs or query builders"],"limitations":["Requires explicit schema definition — cannot generate queries without knowing table structure","Query optimization suggestions may not be optimal for specific database engines or data distributions","Cannot understand complex business logic that determines query structure","No runtime performance validation — suggested optimizations may not improve actual query time","Limited to standard SQL — database-specific syntax (PostgreSQL, MySQL, SQL Server) may be incorrect"],"requires":["API key for Qwen3 Coder Flash","Database schema definition (DDL or schema metadata)","Optional: sample data or statistics for optimization"],"input_types":["text (natural language query description)","code (existing SQL query to optimize)","structured specifications (database schema, table definitions)"],"output_types":["code (generated or optimized SQL query)","text (explanation of query logic and optimization rationale)","structured metadata (estimated performance impact, index suggestions)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-coder-flash__cap_8","uri":"capability://code.generation.editing.api.endpoint.generation.from.specifications","name":"api-endpoint-generation-from-specifications","description":"Generates REST API endpoints, request/response handlers, and validation logic from API specifications (OpenAPI/Swagger, AsyncAPI). The model understands API design patterns, generates endpoints that conform to specifications, and includes proper error handling, input validation, and response formatting. It can generate complete API implementations in frameworks like FastAPI, Express, Django, or Spring Boot.","intents":["I want to generate API endpoint implementations from OpenAPI specifications without manual coding","I need to scaffold a REST API with proper validation, error handling, and documentation","I want to generate API client code that matches my server implementation"],"best_for":["teams using API-first development with OpenAPI specifications","developers rapidly prototyping APIs and need quick scaffolding","teams maintaining multiple API implementations that must stay in sync with specifications"],"limitations":["Generated code may lack business logic — requires manual implementation of core functionality","Error handling may be generic — domain-specific error handling requires customization","No database integration — generated endpoints require manual database layer implementation","Authentication/authorization logic is template-based — requires customization for specific auth schemes","Performance optimizations (caching, pagination) may be missing"],"requires":["API key for Qwen3 Coder Flash","OpenAPI/Swagger specification or AsyncAPI specification","Target framework specification (FastAPI, Express, Django, Spring Boot, etc.)"],"input_types":["structured specifications (OpenAPI/Swagger/AsyncAPI JSON or YAML)","text (API design requirements or patterns)"],"output_types":["code (generated API endpoint implementations)","code (generated request/response models and validation)","code (generated API client code)"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-coder-flash__cap_9","uri":"capability://code.generation.editing.infrastructure.as.code.generation.from.requirements","name":"infrastructure-as-code-generation-from-requirements","description":"Generates infrastructure-as-code (Terraform, CloudFormation, Kubernetes manifests) from natural language requirements or architecture diagrams. The model understands cloud infrastructure patterns, generates IaC that provisions resources correctly, and includes best practices for security, scalability, and cost optimization. It can generate multi-cloud configurations and validate IaC for syntax and logical errors.","intents":["I want to generate Terraform configurations for my cloud infrastructure from a description","I need to scaffold Kubernetes manifests for containerized applications","I want to generate CloudFormation templates that follow AWS best practices"],"best_for":["DevOps teams automating infrastructure provisioning","developers who want to generate infrastructure alongside application code","teams migrating infrastructure between cloud providers"],"limitations":["Generated IaC may not reflect actual infrastructure requirements — requires validation against real needs","Security configurations may be generic — requires customization for specific security policies","Cost optimization suggestions may not be optimal for specific workloads","No validation against actual cloud provider APIs — generated IaC may fail during deployment","Limited to standard resources — custom or niche resources may not be supported"],"requires":["API key for Qwen3 Coder Flash","Infrastructure requirements description or architecture diagram","Optional: existing IaC examples for style matching"],"input_types":["text (natural language infrastructure requirements)","structured specifications (architecture diagrams, resource lists)","code (existing IaC for reference)"],"output_types":["code (generated Terraform, CloudFormation, or Kubernetes manifests)","text (explanation of infrastructure design and best practices)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["API key for OpenAI, Anthropic, or compatible LLM provider supporting function calling","Tool definitions in JSON Schema format (OpenAI function calling spec or Anthropic tool_use format)","HTTP/REST endpoints or local executables for tools to be invoked","Network connectivity for remote tool execution","API key for Qwen3 Coder Flash via OpenRouter or direct Alibaba endpoint","Language specification in prompt (e.g., 'Generate Python 3.11 code')","Target language compiler/runtime for validation (optional but recommended)","API key for Qwen3 Coder Flash","Natural language description of desired functionality","Optional: target language specification"],"failure_modes":["Tool-calling overhead adds latency per invocation cycle — each tool call requires model inference, tool execution, and response parsing","No built-in persistence of tool state across sessions — requires external state management for long-running workflows","Tool schema complexity can degrade performance if >20 tools are registered simultaneously","Requires explicit tool definitions in OpenAI/Anthropic function-calling format — no automatic schema inference from arbitrary APIs","Accuracy varies by language — well-represented languages (Python, JavaScript, Java) have higher correctness than niche languages (Elixir, Clojure)","No real-time syntax validation — generated code may have subtle semantic errors that require testing to catch","Context window limits multi-file generation — cannot generate large monorepos in a single invocation","Language-specific libraries and frameworks not in training data may be hallucinated or incorrectly used","Ambiguous requirements may result in incorrect implementations — requires clarification","Generated code may not match intended behavior if natural language description is unclear","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.5,"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":"2026-05-24T12:16:24.485Z","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=qwen-qwen3-coder-flash","compare_url":"https://unfragile.ai/compare?artifact=qwen-qwen3-coder-flash"}},"signature":"PObt9TAa45JMOoU9f/7VCkd09Nb2eM53+4y+syqRrLI7BQsM6+n6VXPSjyVFQmqLK3JLQpmvfS5dWKXwacdPAg==","signedAt":"2026-06-22T18:00:21.044Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/qwen-qwen3-coder-flash","artifact":"https://unfragile.ai/qwen-qwen3-coder-flash","verify":"https://unfragile.ai/api/v1/verify?slug=qwen-qwen3-coder-flash","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"}}