{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-qwen-qwen3-coder-next","slug":"qwen-qwen3-coder-next","name":"Qwen: Qwen3 Coder Next","type":"model","url":"https://openrouter.ai/models/qwen~qwen3-coder-next","page_url":"https://unfragile.ai/qwen-qwen3-coder-next","categories":["model-training"],"tags":["qwen","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$1.20e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-qwen-qwen3-coder-next__cap_0","uri":"capability://code.generation.editing.sparse.moe.code.generation.with.3b.activation","name":"sparse-moe-code-generation-with-3b-activation","description":"Generates code using a sparse Mixture-of-Experts (MoE) architecture with 80B total parameters but only 3B activated per token, enabling efficient inference on consumer hardware while maintaining reasoning depth. The sparse routing mechanism dynamically selects expert subnetworks based on input context, reducing computational overhead compared to dense models while preserving multi-language code understanding and generation quality.","intents":["Run a capable code generation model locally without enterprise GPU infrastructure","Generate code completions and full functions with sub-second latency on modest hardware","Reduce inference costs by 95% compared to dense 80B models through sparse activation"],"best_for":["Solo developers building local coding agents and IDE plugins","Teams deploying on-premise LLM infrastructure with limited GPU memory","Builders optimizing for inference cost and latency in production systems"],"limitations":["Sparse MoE routing adds ~50-100ms per-token latency overhead vs dense models due to expert selection computation","Expert load balancing can cause uneven GPU utilization if routing distribution becomes skewed","Requires minimum 24GB VRAM for efficient inference; smaller GPUs may trigger CPU offloading","No dynamic expert pruning — all 80B parameters loaded in memory even though only 3B activated"],"requires":["CUDA 12.0+ or compatible GPU with minimum 24GB VRAM","Python 3.9+","Transformers library 4.36+","OpenRouter API key or local inference framework (vLLM, ollama with MoE support)"],"input_types":["text (code context, natural language prompts, file paths)","code (partial functions, class definitions, test cases)"],"output_types":["code (Python, JavaScript, Go, Rust, C++, Java, SQL, etc.)","text (explanations, docstrings, comments)"],"categories":["code-generation-editing","model-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-coder-next__cap_1","uri":"capability://code.generation.editing.multi.language.code.completion.with.context.awareness","name":"multi-language-code-completion-with-context-awareness","description":"Completes code across 40+ programming languages by maintaining language-specific syntax trees and semantic context windows up to 128K tokens. The model uses language-aware tokenization and positional embeddings to understand code structure, enabling completions that respect scope, type hints, and import dependencies rather than purely statistical pattern matching.","intents":["Get intelligent code completions that understand variable scope and type information","Complete code in polyglot projects mixing Python, TypeScript, SQL, and infrastructure-as-code","Generate multi-file edits with cross-file dependency awareness"],"best_for":["Full-stack developers working across multiple languages in single projects","Data engineers building ETL pipelines mixing SQL, Python, and YAML","DevOps teams writing infrastructure code in Terraform, CloudFormation, and Kubernetes manifests"],"limitations":["Context window of 128K tokens limits ability to reference entire large codebases; requires selective context injection","Performance degrades on languages with complex macro systems (C++, Rust) where semantic understanding requires full compilation context","No real-time AST parsing — cannot validate completions against actual project syntax until user saves and lints","Language-specific performance varies: strongest on Python/JavaScript, weaker on niche languages like Elixir or Clojure"],"requires":["OpenRouter API key or local inference setup","Code context (file content, imports, function signatures) provided as prompt","Optional: LSP (Language Server Protocol) integration for IDE plugins to extract semantic context"],"input_types":["code (partial function, class, or file with cursor position)","text (natural language description of desired completion)","structured (AST or symbol table from IDE for semantic context)"],"output_types":["code (completed function, class, or multi-line statement)","text (explanation of completion rationale)"],"categories":["code-generation-editing","developer-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-coder-next__cap_10","uri":"capability://code.generation.editing.code.translation.across.languages","name":"code-translation-across-languages","description":"Translates code between programming languages while preserving logic and adapting to target language idioms. The model understands language-specific patterns, standard libraries, and best practices to produce idiomatic code rather than literal translations.","intents":["Migrate code from Python to Go for performance-critical components","Convert JavaScript to TypeScript with proper type annotations","Port algorithms from C++ to Rust with memory safety guarantees"],"best_for":["Teams migrating between tech stacks","Developers learning new languages by translating familiar code","Projects requiring polyglot implementations for performance or deployment"],"limitations":["Translation quality varies by language pair; easier for similar languages (Python→Ruby) than distant ones (C→Haskell)","Idiomatic translation requires understanding target language best practices; may produce suboptimal code","Standard library differences may require manual adjustment (e.g., async patterns in JavaScript vs Python)","No validation that translated code is functionally equivalent without running tests"],"requires":["OpenRouter API key or local inference setup","Source code to translate","Target language specification"],"input_types":["code (source code in any supported language)","text (target language, style preferences)"],"output_types":["code (translated code in target language)","text (translation notes and potential issues)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-coder-next__cap_11","uri":"capability://text.generation.language.context.aware.code.explanation.and.summarization","name":"context-aware-code-explanation-and-summarization","description":"Explains code functionality at multiple levels of abstraction (line-by-line, function-level, module-level) by analyzing code structure, control flow, and data dependencies. The model generates explanations in natural language with examples and diagrams (as text) to help developers understand unfamiliar code.","intents":["Understand what a complex function does without reading every line","Learn how a module integrates with the rest of the codebase","Generate summaries of code changes for pull request descriptions"],"best_for":["Developers onboarding to new codebases","Teams documenting legacy code","Code reviewers understanding large pull requests"],"limitations":["Explanations may be verbose or miss key insights if code is highly complex","Cannot explain business logic or domain-specific intent without comments","Diagram generation is text-based (ASCII art); cannot produce visual diagrams","Explanation quality depends on code clarity and naming conventions"],"requires":["OpenRouter API key or local inference setup","Code to explain"],"input_types":["code (function, class, module, or file)","text (explanation level: line-by-line, function, module)"],"output_types":["text (explanation in natural language, examples, ASCII diagrams)"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-coder-next__cap_2","uri":"capability://tool.use.integration.agent.oriented.function.calling.with.tool.schemas","name":"agent-oriented-function-calling-with-tool-schemas","description":"Supports structured function calling through JSON schema definitions, enabling agents to invoke external tools and APIs by generating valid function calls with typed parameters. The model outputs function names and arguments as structured JSON that can be directly parsed and executed, with built-in validation against provided schemas to ensure parameter types match function signatures.","intents":["Build autonomous coding agents that can call linters, formatters, and test runners","Enable multi-step workflows where the model decides which tools to invoke based on code analysis","Create agents that can fetch documentation, search codebases, and execute shell commands"],"best_for":["Developers building agentic code review and refactoring systems","Teams creating autonomous CI/CD agents that make code decisions","Builders implementing multi-step code generation workflows with tool orchestration"],"limitations":["Function calling accuracy degrades with >10 available tools; router confusion increases exponentially with tool count","No built-in error recovery — if generated function call fails, agent must explicitly handle and retry","Schema validation is client-side; model can still generate invalid JSON if schema is ambiguous or contradictory","No native support for streaming function calls — entire call must be generated before execution"],"requires":["OpenRouter API key or local inference framework with function calling support","JSON schema definitions for each tool (OpenAI function calling format or Anthropic tools format)","Agent framework (LangChain, LlamaIndex, or custom orchestration) to parse and execute function calls"],"input_types":["text (natural language instruction for agent)","code (code to analyze or refactor)","structured (JSON schema definitions for available tools)"],"output_types":["structured (JSON function calls with typed parameters)","text (reasoning or explanation before function invocation)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-coder-next__cap_3","uri":"capability://code.generation.editing.codebase.aware.refactoring.with.cross.file.understanding","name":"codebase-aware-refactoring-with-cross-file-understanding","description":"Refactors code across multiple files by understanding import dependencies, function call graphs, and type relationships across the entire codebase context window. The model tracks variable definitions, function signatures, and class hierarchies to suggest refactorings that maintain correctness across file boundaries, such as renaming functions with all call sites updated or extracting shared logic into utilities.","intents":["Rename a function and automatically update all call sites across the codebase","Extract duplicated logic into a shared utility and update imports in dependent files","Refactor class hierarchies while maintaining type safety across inheritance chains"],"best_for":["Teams maintaining large codebases where manual refactoring is error-prone","Developers migrating between architectural patterns (monolith to microservices, class-based to functional)","Builders automating code modernization and technical debt reduction"],"limitations":["Limited to 128K token context — cannot refactor codebases larger than ~50K lines of code in single pass","No access to actual compilation or type checking — refactorings may introduce type errors that require manual verification","Cannot understand dynamic imports or reflection-based code patterns (common in Python and JavaScript)","Refactoring suggestions are text-based; requires manual application or integration with AST-based refactoring tools"],"requires":["OpenRouter API key or local inference setup","Codebase files provided as context (within 128K token limit)","Optional: IDE plugin or CLI tool to apply suggested refactorings"],"input_types":["code (multiple files with imports and dependencies)","text (natural language description of desired refactoring, e.g., 'rename function X to Y')"],"output_types":["code (refactored files with updated imports and call sites)","text (explanation of changes and potential risks)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-coder-next__cap_4","uri":"capability://code.generation.editing.test.generation.and.coverage.analysis","name":"test-generation-and-coverage-analysis","description":"Generates unit tests and integration tests by analyzing code structure, identifying edge cases, and creating test cases that cover branches and error paths. The model understands testing frameworks (pytest, Jest, JUnit) and generates tests with proper assertions, mocking, and setup/teardown logic based on the code under test.","intents":["Generate comprehensive unit tests for a function with edge case coverage","Create integration tests that mock external dependencies and verify API contracts","Analyze existing code and suggest test cases for uncovered branches"],"best_for":["Teams improving test coverage on legacy codebases","Developers practicing TDD and needing test scaffolding","QA engineers automating test generation for regression testing"],"limitations":["Generated tests may miss domain-specific edge cases that require business logic understanding","Mock generation is framework-aware but may not match actual external service behavior","No execution feedback — cannot verify generated tests actually pass without running them","Performance varies by language: strongest for Python/JavaScript, weaker for statically-typed languages requiring complex setup"],"requires":["OpenRouter API key or local inference setup","Code to test (function, class, or module)","Optional: existing test examples for style matching"],"input_types":["code (function or class to test)","text (testing framework preference, coverage goals)"],"output_types":["code (test file with test cases, assertions, mocks)","text (coverage analysis and suggestions)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-coder-next__cap_5","uri":"capability://text.generation.language.documentation.generation.from.code","name":"documentation-generation-from-code","description":"Generates API documentation, docstrings, and README sections by analyzing code structure, function signatures, and type hints. The model produces documentation in multiple formats (Markdown, reStructuredText, JSDoc) with examples, parameter descriptions, return types, and usage patterns extracted from code context.","intents":["Generate docstrings for all functions in a module automatically","Create API documentation with examples from actual code usage","Generate README sections describing module purpose and key functions"],"best_for":["Teams documenting legacy code without existing docstrings","Open-source maintainers automating documentation generation","Developers maintaining multiple libraries with consistent documentation"],"limitations":["Generated documentation may be generic or miss domain-specific context","Cannot infer documentation from comments or commit history — relies solely on code structure","Example generation may not match actual usage patterns in the codebase","No validation that generated documentation is accurate or complete"],"requires":["OpenRouter API key or local inference setup","Code to document (functions, classes, modules)"],"input_types":["code (functions, classes, modules)","text (documentation style preference, target audience)"],"output_types":["text (docstrings, API documentation, README sections in Markdown or reStructuredText)"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-coder-next__cap_6","uri":"capability://code.generation.editing.code.review.and.quality.analysis","name":"code-review-and-quality-analysis","description":"Analyzes code for bugs, performance issues, security vulnerabilities, and style violations by examining code patterns, data flow, and common anti-patterns. The model identifies issues like null pointer dereferences, SQL injection risks, inefficient algorithms, and style inconsistencies, providing specific line numbers and remediation suggestions.","intents":["Automatically review pull requests and flag potential bugs before merge","Identify security vulnerabilities in code (SQL injection, XSS, insecure deserialization)","Suggest performance optimizations for hot code paths"],"best_for":["Teams implementing automated code review in CI/CD pipelines","Security teams scanning codebases for vulnerability patterns","Developers learning best practices through AI-powered feedback"],"limitations":["Cannot detect runtime errors that require execution context (e.g., null pointer exceptions in specific code paths)","Security analysis is pattern-based; may miss novel vulnerability classes or context-specific risks","Performance analysis is heuristic-based without profiling data; may suggest optimizations that don't improve actual runtime","False positive rate increases with code complexity and unfamiliar patterns"],"requires":["OpenRouter API key or local inference setup","Code to review (file or diff)"],"input_types":["code (full file or diff)","text (review focus: security, performance, style, bugs)"],"output_types":["text (list of issues with line numbers, severity, and remediation suggestions)","structured (JSON with issue categories and confidence scores)"],"categories":["code-generation-editing","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-coder-next__cap_7","uri":"capability://code.generation.editing.sql.and.database.query.generation","name":"sql-and-database-query-generation","description":"Generates SQL queries, database migrations, and schema definitions by understanding natural language descriptions and existing database context. The model produces database-specific SQL (PostgreSQL, MySQL, SQLite) with proper indexing, constraints, and optimization hints, and can generate migration scripts that preserve data integrity.","intents":["Generate SQL queries from natural language descriptions (e.g., 'find all users who signed up in the last month')","Create database migrations that alter schemas while preserving existing data","Optimize slow queries by suggesting indexes and query rewrites"],"best_for":["Data engineers and backend developers building database-driven applications","Teams automating database schema management and migrations","Developers learning SQL through AI-assisted query generation"],"limitations":["Generated queries may not be optimal for large datasets without profiling and index analysis","Migration generation assumes simple schema changes; complex refactorings may require manual intervention","No access to actual database statistics — cannot generate queries optimized for specific data distributions","Database-specific syntax varies; generated queries may require adjustment for target database"],"requires":["OpenRouter API key or local inference setup","Database schema context (table definitions, relationships)","Optional: existing queries for style matching"],"input_types":["text (natural language query description)","structured (database schema, table definitions)","code (existing queries for context)"],"output_types":["code (SQL queries, migration scripts)","text (explanation of query logic and optimization notes)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-coder-next__cap_8","uri":"capability://code.generation.editing.infrastructure.as.code.generation.and.validation","name":"infrastructure-as-code-generation-and-validation","description":"Generates infrastructure code (Terraform, CloudFormation, Kubernetes manifests) from natural language descriptions and validates configurations for security, cost, and best practices. The model understands cloud provider APIs, resource dependencies, and infrastructure patterns to produce production-ready IaC with proper error handling and monitoring.","intents":["Generate Terraform modules for common infrastructure patterns (VPC, RDS, load balancer)","Create Kubernetes manifests with proper resource limits, health checks, and security policies","Validate infrastructure code for security vulnerabilities and cost optimization"],"best_for":["DevOps engineers automating infrastructure provisioning","Teams implementing infrastructure-as-code practices","Cloud architects validating infrastructure designs"],"limitations":["Generated IaC may not match organization-specific naming conventions or tagging strategies","Cost estimation is approximate without actual cloud pricing data","Security validation is pattern-based; may miss organization-specific compliance requirements","No integration with actual cloud provider APIs — cannot validate that resources will actually provision"],"requires":["OpenRouter API key or local inference setup","Cloud provider context (account, region, existing resources)","Optional: organization-specific IaC templates for style matching"],"input_types":["text (natural language infrastructure description)","structured (cloud provider configuration, resource requirements)","code (existing IaC for context)"],"output_types":["code (Terraform, CloudFormation, Kubernetes manifests)","text (validation results, security findings, cost estimates)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-coder-next__cap_9","uri":"capability://code.generation.editing.debugging.assistance.with.error.analysis","name":"debugging-assistance-with-error-analysis","description":"Analyzes error messages, stack traces, and code context to identify root causes and suggest fixes. The model understands common error patterns, exception hierarchies, and debugging techniques to provide targeted remediation steps rather than generic suggestions.","intents":["Understand cryptic error messages and identify root cause","Suggest fixes for common exceptions (NullPointerException, TypeError, ImportError)","Analyze stack traces to identify which code path led to the error"],"best_for":["Developers debugging complex issues in unfamiliar codebases","Teams reducing time-to-resolution for production incidents","Junior developers learning debugging techniques"],"limitations":["Cannot reproduce runtime errors without execution context; suggestions are based on error patterns","Debugging effectiveness depends on error message quality and stack trace completeness","May suggest incorrect fixes if error message is misleading or context is incomplete","No access to actual logs or monitoring data — cannot correlate errors with system state"],"requires":["OpenRouter API key or local inference setup","Error message and stack trace","Optional: relevant code context"],"input_types":["text (error message, stack trace)","code (relevant code context)"],"output_types":["text (root cause analysis, suggested fixes, debugging steps)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["CUDA 12.0+ or compatible GPU with minimum 24GB VRAM","Python 3.9+","Transformers library 4.36+","OpenRouter API key or local inference framework (vLLM, ollama with MoE support)","OpenRouter API key or local inference setup","Code context (file content, imports, function signatures) provided as prompt","Optional: LSP (Language Server Protocol) integration for IDE plugins to extract semantic context","Source code to translate","Target language specification","Code to explain"],"failure_modes":["Sparse MoE routing adds ~50-100ms per-token latency overhead vs dense models due to expert selection computation","Expert load balancing can cause uneven GPU utilization if routing distribution becomes skewed","Requires minimum 24GB VRAM for efficient inference; smaller GPUs may trigger CPU offloading","No dynamic expert pruning — all 80B parameters loaded in memory even though only 3B activated","Context window of 128K tokens limits ability to reference entire large codebases; requires selective context injection","Performance degrades on languages with complex macro systems (C++, Rust) where semantic understanding requires full compilation context","No real-time AST parsing — cannot validate completions against actual project syntax until user saves and lints","Language-specific performance varies: strongest on Python/JavaScript, weaker on niche languages like Elixir or Clojure","Translation quality varies by language pair; easier for similar languages (Python→Ruby) than distant ones (C→Haskell)","Idiomatic translation requires understanding target language best practices; may produce suboptimal code","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-next","compare_url":"https://unfragile.ai/compare?artifact=qwen-qwen3-coder-next"}},"signature":"r5axD8Qp/yt0G6S0ZUpXxhTOw0MeoCaOj0ntEEbL5o4Fcmebs7q7g0wK6QvFE9SKBizCOx2nsG1q42VYrefyDA==","signedAt":"2026-06-20T01:07:25.448Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/qwen-qwen3-coder-next","artifact":"https://unfragile.ai/qwen-qwen3-coder-next","verify":"https://unfragile.ai/api/v1/verify?slug=qwen-qwen3-coder-next","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"}}