{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-qwen-qwen3-coder","slug":"qwen-qwen3-coder","name":"Qwen: Qwen3 Coder 480B A35B","type":"model","url":"https://openrouter.ai/models/qwen~qwen3-coder","page_url":"https://unfragile.ai/qwen-qwen3-coder","categories":["model-training"],"tags":["qwen","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$2.20e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-qwen-qwen3-coder__cap_0","uri":"capability://code.generation.editing.mixture.of.experts.code.generation.with.sparse.activation","name":"mixture-of-experts code generation with sparse activation","description":"Qwen3-Coder uses a Mixture-of-Experts (MoE) architecture with 480B total parameters but only activates 35B parameters per inference token, enabling efficient code generation across multiple programming languages and paradigms. The sparse activation pattern routes different code patterns (e.g., API calls, data transformations, control flow) to specialized expert sub-networks, reducing latency and memory footprint compared to dense models while maintaining reasoning depth for complex coding tasks.","intents":["Generate production-ready code snippets with lower inference latency than dense 480B models","Handle long-context code files (>100K tokens) without proportional memory scaling","Route different coding patterns to specialized experts for domain-specific optimization"],"best_for":["Teams building real-time code generation APIs where latency is critical","Developers working with resource-constrained inference infrastructure","Organizations processing large codebases requiring long-context understanding"],"limitations":["MoE routing overhead adds ~50-100ms per request compared to dense models of equivalent quality","Expert specialization may degrade performance on novel code patterns outside training distribution","Requires inference infrastructure optimized for MoE (not all quantization frameworks support efficient sparse activation)"],"requires":["OpenRouter API key or compatible inference endpoint supporting MoE models","Minimum context window of 4K tokens; optimal performance at 8K-32K token contexts","Inference framework with MoE support (vLLM, TensorRT-LLM, or equivalent)"],"input_types":["code snippets (Python, JavaScript, TypeScript, Java, C++, Go, Rust, etc.)","natural language instructions describing coding tasks","partial code with gaps to fill","code with inline comments or docstrings"],"output_types":["complete code functions or modules","code explanations with reasoning","refactored code with optimization suggestions","structured code with type annotations"],"categories":["code-generation-editing","model-architecture"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-coder__cap_1","uri":"capability://tool.use.integration.agentic.function.calling.with.tool.use.schema.binding","name":"agentic function calling with tool-use schema binding","description":"Qwen3-Coder natively supports structured function calling through a schema-based tool registry that binds natural language instructions to executable functions. The model generates function calls as structured JSON payloads that conform to OpenAPI/JSON Schema specifications, enabling seamless integration with external APIs, code execution environments, and multi-step agentic workflows without requiring prompt engineering or output parsing hacks.","intents":["Build autonomous agents that call external APIs (REST, GraphQL) based on user intent","Chain multiple function calls across different services in a single reasoning step","Generate type-safe function calls with automatic parameter validation against schemas"],"best_for":["Developers building LLM-powered agents that orchestrate multiple tools","Teams implementing autonomous coding assistants that call linters, formatters, and test runners","Organizations deploying multi-step workflows where the model must decide which tools to invoke"],"limitations":["Schema complexity beyond 10-15 parameters may cause hallucinated or invalid function calls","No built-in retry logic for failed function executions — requires external error handling","Function calling accuracy degrades when tool descriptions are ambiguous or when multiple tools have overlapping purposes"],"requires":["Function schemas defined in OpenAPI 3.0 or JSON Schema format","Tool registry or function binding layer (e.g., LangChain, LlamaIndex, or custom implementation)","Execution environment capable of invoking functions and returning results to the model"],"input_types":["natural language task descriptions","JSON Schema definitions of available functions","previous function call results (for multi-turn agentic loops)"],"output_types":["structured JSON function calls with parameters","function call sequences (multiple calls in single response)","reasoning traces explaining which tool to call and why"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-coder__cap_10","uri":"capability://code.generation.editing.api.and.library.integration.assistance","name":"api and library integration assistance","description":"Qwen3-Coder generates code that correctly uses external APIs, libraries, and frameworks by understanding their documentation, signatures, and usage patterns. The model generates correct API calls with proper parameter handling, error handling, and idiomatic usage patterns specific to each library or framework, reducing integration errors and accelerating development.","intents":["Generate correct API calls for REST, GraphQL, or SDK-based services","Integrate third-party libraries with proper error handling and best practices","Generate framework-specific boilerplate (Django models, React components, etc.)"],"best_for":["Developers integrating new libraries or APIs","Teams building applications with multiple third-party dependencies","Organizations standardizing on specific frameworks or libraries"],"limitations":["API knowledge is limited to training data; recent API changes or new libraries may not be supported","Generated code may use deprecated API versions or methods","No built-in validation that generated code works with actual API endpoints"],"requires":["API documentation or library reference (optional, for context)","Target library or API specification","API keys or credentials (for testing generated code)"],"input_types":["natural language task descriptions","API documentation or library references","existing code using the same library (for style reference)"],"output_types":["correct API calls with parameters","error handling and retry logic","framework-specific boilerplate","usage examples and documentation"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-coder__cap_11","uri":"capability://code.generation.editing.instruction.following.code.generation.with.reasoning.chains","name":"instruction-following code generation with reasoning chains","description":"Qwen3-Coder generates code from natural language instructions by decomposing complex tasks into intermediate reasoning steps, then generating code that implements each step. The model uses chain-of-thought reasoning to break down requirements, plan implementation approaches, and generate code that satisfies all specified constraints, with explicit reasoning traces explaining the generation process.","intents":["Generate complex code from detailed natural language specifications","Implement multi-step algorithms or workflows from descriptions","Generate code that satisfies multiple constraints or requirements"],"best_for":["Developers translating specifications into code","Teams building code generation tools that must handle complex requirements","Organizations automating code generation from design documents"],"limitations":["Reasoning chains add latency (2-5x slower than direct generation)","Complex specifications may require multiple refinement iterations","Generated code may not satisfy all constraints if specification is ambiguous"],"requires":["Clear natural language specification of requirements","Optional: examples or reference implementations","Optional: constraints or performance requirements"],"input_types":["natural language task descriptions","detailed specifications or requirements","examples or reference implementations"],"output_types":["reasoning traces explaining generation process","generated code implementing all requirements","explanations of design decisions"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-coder__cap_2","uri":"capability://code.generation.editing.long.context.code.understanding.with.128k.token.window","name":"long-context code understanding with 128k+ token window","description":"Qwen3-Coder supports extended context windows (up to 128K tokens or higher depending on deployment) enabling analysis and generation of code across entire repositories, large documentation sets, and multi-file codebases without chunking or summarization. The model uses efficient attention mechanisms (likely rotary position embeddings and sparse attention patterns) to maintain coherence over long sequences while the MoE architecture keeps memory footprint manageable.","intents":["Analyze entire codebase structure to generate contextually-aware code that respects existing patterns","Refactor large functions or modules while maintaining consistency with surrounding code","Generate documentation or tests for complex multi-file systems without losing context"],"best_for":["Teams working with large monorepos or microservice architectures","Developers needing codebase-aware code generation that respects existing conventions","Organizations building code review or refactoring tools that must understand full context"],"limitations":["Latency increases non-linearly with context length; 128K token requests may take 10-30 seconds depending on infrastructure","Attention computation scales quadratically with sequence length, requiring inference hardware with sufficient memory bandwidth","Model may lose focus on specific details when context exceeds 64K tokens due to attention dilution"],"requires":["Inference infrastructure optimized for long-context (vLLM with paged attention, or equivalent)","Sufficient GPU memory (minimum 40GB for 128K context with batch size 1)","Code tokenization compatible with Qwen's tokenizer (handles multi-byte UTF-8 efficiently)"],"input_types":["entire source files or modules (up to 128K tokens)","repository structure descriptions or file listings","code with extensive comments or docstrings providing context"],"output_types":["code generation respecting full codebase context","cross-file refactoring suggestions","architecture analysis and recommendations","comprehensive code documentation"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-coder__cap_3","uri":"capability://code.generation.editing.multi.language.code.generation.with.language.specific.expert.routing","name":"multi-language code generation with language-specific expert routing","description":"Qwen3-Coder generates syntactically correct code across 30+ programming languages (Python, JavaScript, TypeScript, Java, C++, Go, Rust, C#, PHP, Swift, Kotlin, etc.) by routing language-specific patterns to dedicated expert sub-networks within the MoE architecture. The model learns language-specific syntax rules, idioms, and standard library patterns during training, enabling generation of idiomatic code that follows language conventions rather than generic pseudo-code.","intents":["Generate production-ready code in any target language from natural language specifications","Translate code between languages while preserving logic and adapting to language idioms","Generate language-specific boilerplate, configuration files, and build scripts"],"best_for":["Polyglot development teams working across multiple languages","Organizations building code generation tools that must support diverse tech stacks","Developers needing language-specific code generation (e.g., async patterns in Rust vs Python)"],"limitations":["Code quality varies by language; performance is highest for Python/JavaScript/TypeScript, lower for niche languages like Haskell or Clojure","Language-specific libraries and frameworks may not be well-represented in training data, leading to outdated or incorrect API usage","No built-in validation that generated code compiles or runs; requires external linting/testing"],"requires":["Target language specification in prompt (e.g., 'generate Python 3.9+ code')","Language-specific context or examples to guide generation toward correct idioms","Compiler or linter for the target language to validate generated code"],"input_types":["natural language task descriptions","code snippets in any supported language","language-specific requirements (e.g., 'async/await pattern', 'type-safe generics')"],"output_types":["syntactically correct code in target language","language-specific idioms and best practices","build configuration files (Cargo.toml, package.json, pom.xml, etc.)","language-specific documentation or comments"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-coder__cap_4","uri":"capability://code.generation.editing.code.completion.with.context.aware.token.prediction","name":"code completion with context-aware token prediction","description":"Qwen3-Coder predicts the next tokens in a code sequence given a partial code context, supporting both single-line and multi-line completions. The model uses causal attention masking to ensure predictions only depend on preceding tokens, and the MoE architecture routes completion patterns (e.g., API method chains, control flow continuations) to specialized experts, enabling fast, accurate completions that respect code structure and semantics.","intents":["Provide IDE-integrated code completion suggestions as developers type","Auto-complete function calls, method chains, and API usage patterns","Suggest next lines of code based on preceding context and coding patterns"],"best_for":["IDE/editor plugin developers building real-time code completion features","Teams implementing GitHub Copilot-like experiences with custom models","Developers needing fast, context-aware completions for specific codebases"],"limitations":["Single-token prediction latency must be <100ms for real-time IDE integration; MoE routing overhead may exceed this on resource-constrained hardware","Completion quality degrades when context is ambiguous or when multiple valid continuations exist","No built-in ranking of completion candidates; requires external scoring or filtering"],"requires":["Low-latency inference infrastructure (GPU with <100ms per-token latency)","IDE integration layer or language server protocol (LSP) implementation","Tokenizer compatible with Qwen's vocabulary"],"input_types":["partial code up to cursor position","file context (imports, class definitions, etc.)","language hint (Python, JavaScript, etc.)"],"output_types":["next token predictions with probabilities","multi-token completion suggestions","ranked list of completion candidates"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-coder__cap_5","uri":"capability://text.generation.language.code.explanation.and.documentation.generation","name":"code explanation and documentation generation","description":"Qwen3-Coder generates natural language explanations of code functionality, generates docstrings and comments, and produces comprehensive documentation from source code. The model uses its code understanding capabilities to parse syntax and semantics, then generates human-readable explanations at multiple levels of abstraction (function-level, module-level, system-level) with optional formatting for Markdown, Sphinx, or JSDoc standards.","intents":["Generate docstrings and comments for undocumented legacy code","Produce API documentation from source code with examples","Explain complex algorithms or architectural patterns in natural language"],"best_for":["Teams documenting legacy codebases or onboarding new developers","Organizations automating API documentation generation","Developers needing to understand unfamiliar code quickly"],"limitations":["Generated explanations may be verbose or miss subtle implementation details","Documentation quality depends on code clarity; poorly-written code produces poor explanations","No built-in validation that explanations are accurate; requires human review"],"requires":["Source code in supported language","Optional: documentation format specification (Markdown, Sphinx, JSDoc, etc.)"],"input_types":["source code files or snippets","function or class definitions","code with existing comments or docstrings (for style reference)"],"output_types":["natural language explanations","formatted docstrings (Python, JavaScript, Java, etc.)","Markdown documentation","architecture diagrams or flowchart descriptions"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-coder__cap_6","uri":"capability://code.generation.editing.code.refactoring.and.optimization.suggestions","name":"code refactoring and optimization suggestions","description":"Qwen3-Coder analyzes code for refactoring opportunities (e.g., extracting functions, simplifying logic, removing duplication) and generates optimized versions with explanations of improvements. The model evaluates code against best practices, design patterns, and performance considerations, then produces refactored code with detailed reasoning about why changes improve maintainability, readability, or performance.","intents":["Suggest refactoring improvements to reduce code complexity or duplication","Optimize code for performance (e.g., algorithmic improvements, caching strategies)","Modernize legacy code to use current language features or frameworks"],"best_for":["Teams conducting code reviews or quality improvements","Developers working on performance optimization","Organizations modernizing legacy codebases"],"limitations":["Refactoring suggestions may not account for domain-specific constraints or business logic","Performance optimizations are heuristic-based; actual speedup depends on runtime characteristics and data distribution","No built-in testing to verify refactored code maintains original behavior"],"requires":["Source code in supported language","Optional: performance metrics or constraints (e.g., 'reduce memory usage by 50%')","Test suite to validate refactored code (recommended)"],"input_types":["source code files or functions","performance metrics or profiling data","refactoring goals or constraints"],"output_types":["refactored code with improvements","detailed explanations of changes","before/after comparisons","performance impact estimates"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-coder__cap_7","uri":"capability://code.generation.editing.test.generation.and.test.case.synthesis","name":"test generation and test case synthesis","description":"Qwen3-Coder generates unit tests, integration tests, and test cases from source code or specifications. The model analyzes function signatures, docstrings, and implementation logic to synthesize test cases covering normal cases, edge cases, and error conditions. Generated tests are formatted in standard frameworks (pytest, Jest, JUnit, etc.) and include assertions, setup/teardown logic, and mocking where appropriate.","intents":["Generate comprehensive unit tests for untested functions or modules","Create test cases covering edge cases and error conditions","Generate integration tests for multi-component systems"],"best_for":["Teams improving test coverage on legacy codebases","Developers practicing test-driven development (TDD)","Organizations automating test generation for quality assurance"],"limitations":["Generated tests may not cover all edge cases or domain-specific error conditions","Test quality depends on code clarity and function specifications","No built-in validation that tests actually pass; requires execution and debugging"],"requires":["Source code with clear function signatures and docstrings","Test framework specification (pytest, Jest, JUnit, etc.)","Optional: existing test examples for style reference"],"input_types":["function or class definitions","API specifications or docstrings","existing test examples (for style reference)"],"output_types":["test code in specified framework","test cases with assertions","mocking and fixture setup","test documentation or comments"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-coder__cap_8","uri":"capability://safety.moderation.bug.detection.and.vulnerability.analysis","name":"bug detection and vulnerability analysis","description":"Qwen3-Coder identifies potential bugs, security vulnerabilities, and code quality issues through static analysis of source code. The model detects common vulnerability patterns (SQL injection, XSS, buffer overflows, etc.), logic errors, null pointer dereferences, and performance anti-patterns, then generates detailed explanations and remediation suggestions with corrected code examples.","intents":["Identify security vulnerabilities in code before deployment","Detect logic errors and potential runtime exceptions","Find performance anti-patterns and optimization opportunities"],"best_for":["Security teams conducting code reviews or vulnerability assessments","Developers building secure applications","Organizations automating security scanning in CI/CD pipelines"],"limitations":["Detection accuracy varies by vulnerability type; common patterns detected reliably, novel vulnerabilities may be missed","False positives possible; requires human review to validate findings","No runtime context; cannot detect vulnerabilities that depend on specific data flows or execution paths"],"requires":["Source code in supported language","Optional: security standards or compliance requirements (OWASP, CWE, etc.)"],"input_types":["source code files or snippets","security requirements or threat models","code with known vulnerabilities (for training context)"],"output_types":["list of detected vulnerabilities with severity levels","detailed explanations of vulnerability impact","corrected code examples","remediation recommendations"],"categories":["safety-moderation","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-coder__cap_9","uri":"capability://code.generation.editing.code.review.and.quality.assessment","name":"code review and quality assessment","description":"Qwen3-Coder performs automated code reviews, evaluating code against style guides, design patterns, best practices, and quality metrics. The model provides detailed feedback on readability, maintainability, performance, security, and adherence to project conventions, with specific suggestions for improvement and examples of better approaches.","intents":["Provide automated code review feedback on pull requests","Assess code quality against project standards and best practices","Identify code style violations and suggest corrections"],"best_for":["Teams automating code review processes","Organizations enforcing consistent code quality standards","Developers seeking feedback on code quality before human review"],"limitations":["Review feedback is heuristic-based and may not account for domain-specific requirements","Cannot evaluate code against project-specific conventions without explicit specification","Subjective aspects of code quality (naming, organization) may not align with team preferences"],"requires":["Source code in supported language","Optional: style guide or coding standards specification","Optional: project context or conventions"],"input_types":["source code files or diffs","style guide or coding standards","project context or conventions"],"output_types":["code review feedback with severity levels","specific suggestions for improvement","examples of better approaches","quality metrics or scores"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"low","permissions":["OpenRouter API key or compatible inference endpoint supporting MoE models","Minimum context window of 4K tokens; optimal performance at 8K-32K token contexts","Inference framework with MoE support (vLLM, TensorRT-LLM, or equivalent)","Function schemas defined in OpenAPI 3.0 or JSON Schema format","Tool registry or function binding layer (e.g., LangChain, LlamaIndex, or custom implementation)","Execution environment capable of invoking functions and returning results to the model","API documentation or library reference (optional, for context)","Target library or API specification","API keys or credentials (for testing generated code)","Clear natural language specification of requirements"],"failure_modes":["MoE routing overhead adds ~50-100ms per request compared to dense models of equivalent quality","Expert specialization may degrade performance on novel code patterns outside training distribution","Requires inference infrastructure optimized for MoE (not all quantization frameworks support efficient sparse activation)","Schema complexity beyond 10-15 parameters may cause hallucinated or invalid function calls","No built-in retry logic for failed function executions — requires external error handling","Function calling accuracy degrades when tool descriptions are ambiguous or when multiple tools have overlapping purposes","API knowledge is limited to training data; recent API changes or new libraries may not be supported","Generated code may use deprecated API versions or methods","No built-in validation that generated code works with actual API endpoints","Reasoning chains add latency (2-5x slower than direct generation)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.49,"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","compare_url":"https://unfragile.ai/compare?artifact=qwen-qwen3-coder"}},"signature":"KoFn9mIbl7JTz1y9wiNUJBvp5OW5IGRICSuastPhT+lCWIDDQELgdSojI/yJRZF59z0oTNFXFCOmoAMAlhqFCw==","signedAt":"2026-06-20T22:42:20.394Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/qwen-qwen3-coder","artifact":"https://unfragile.ai/qwen-qwen3-coder","verify":"https://unfragile.ai/api/v1/verify?slug=qwen-qwen3-coder","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"}}