{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-qwen-qwen-plus-2025-07-28","slug":"qwen-qwen-plus-2025-07-28","name":"Qwen: Qwen Plus 0728","type":"model","url":"https://openrouter.ai/models/qwen~qwen-plus-2025-07-28","page_url":"https://unfragile.ai/qwen-qwen-plus-2025-07-28","categories":["chatbots-assistants"],"tags":["qwen","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$2.60e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-qwen-qwen-plus-2025-07-28__cap_0","uri":"capability://memory.knowledge.1.million.token.context.window.reasoning","name":"1-million-token context window reasoning","description":"Processes up to 1 million tokens of input context using a hybrid reasoning architecture that balances computational efficiency with extended context retention. The model uses sparse attention mechanisms and hierarchical token processing to manage the expanded context window without proportional latency increases, enabling analysis of entire codebases, long documents, or multi-turn conversations within a single inference pass.","intents":["Analyze entire source code repositories for refactoring opportunities without splitting into chunks","Process long research papers or documentation with full context preservation","Maintain coherent multi-turn conversations spanning hundreds of exchanges","Extract insights from large datasets or logs without losing contextual relationships"],"best_for":["Enterprise teams processing large documents or codebases in single requests","Researchers analyzing lengthy academic papers or technical specifications","Developers building context-aware coding assistants for large projects"],"limitations":["1M token limit still requires careful prompt engineering for optimal retrieval of relevant information within the window","Latency scales non-linearly with context size; full 1M token inputs may incur 5-10x latency vs 4K token inputs","Hybrid reasoning approach may produce less optimal outputs on tasks requiring deep reasoning over entire context vs focused reasoning on key sections"],"requires":["OpenRouter API key with Qwen Plus 0728 model access","HTTP client capable of handling large request payloads (>5MB)","Token counting utility to stay within 1M limit"],"input_types":["text","code","structured documents (markdown, JSON, XML)"],"output_types":["text","code","structured analysis"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-plus-2025-07-28__cap_1","uri":"capability://text.generation.language.multi.turn.conversational.reasoning.with.state.preservation","name":"multi-turn conversational reasoning with state preservation","description":"Maintains coherent dialogue across multiple exchanges by preserving conversation state and reasoning chains within the 1M token context window. The model tracks user intent evolution, previous conclusions, and contextual constraints across turns without explicit memory management, using attention mechanisms to weight recent vs historical context appropriately for each response.","intents":["Build chatbots that remember and build upon previous conversation context without external session storage","Create interactive debugging assistants that maintain understanding of code issues across multiple clarification rounds","Develop customer support agents that track problem resolution across extended conversations","Implement collaborative writing assistants that preserve document context and user preferences throughout editing sessions"],"best_for":["Teams building conversational AI without dedicated session management infrastructure","Startups prototyping chatbot experiences with minimal backend complexity","Developers creating interactive coding assistants or technical support bots"],"limitations":["Context preservation degrades gracefully as conversation length approaches 1M tokens; oldest turns receive less attention weight","No explicit memory mechanism for facts or preferences — relies on inclusion in active context window","Requires careful prompt engineering to maintain consistent persona and reasoning style across turns"],"requires":["OpenRouter API key","Application logic to accumulate conversation history and pass as context","Token counter to monitor cumulative conversation length"],"input_types":["text"],"output_types":["text"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-plus-2025-07-28__cap_10","uri":"capability://text.generation.language.question.answering.from.context.with.citation.tracking","name":"question answering from context with citation tracking","description":"Answers questions by retrieving relevant information from provided context and generating answers with explicit citations to source material. The model identifies which parts of the context support each claim, enables verification of answers against sources, and handles questions that cannot be answered from available context by explicitly stating information gaps.","intents":["Build customer support systems that answer questions with citations to documentation or knowledge bases","Create research assistants that provide answers with explicit source attribution","Implement fact-checking systems that verify claims against reference materials","Generate question-answer pairs from documents for training or testing purposes"],"best_for":["Organizations building knowledge base systems with verifiable answers","Research teams requiring source attribution and fact verification","Customer support teams automating Q&A with transparency"],"limitations":["Citation accuracy depends on context clarity; ambiguous sources may produce incorrect citations","Model may hallucinate citations to non-existent sources; requires validation that citations actually exist","Questions requiring synthesis across multiple sources may produce incomplete citations"],"requires":["OpenRouter API key","Context documents or knowledge base content","Question to answer","Prompt engineering to request citations"],"input_types":["text","questions","context documents"],"output_types":["text","answers with citations","source references"],"categories":["text-generation-language","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-plus-2025-07-28__cap_2","uri":"capability://text.generation.language.balanced.performance.speed.cost.optimization","name":"balanced performance-speed-cost optimization","description":"Implements a tuned inference pipeline that optimizes for three competing objectives simultaneously: reasoning quality, response latency, and token cost. Uses quantization, selective attention, and early-exit mechanisms to deliver faster responses than full-capability models while maintaining accuracy above a quality threshold, with transparent per-token pricing enabling cost predictability.","intents":["Deploy production chatbots where response latency must stay under 2-3 seconds while maintaining quality","Build high-volume applications where per-token costs directly impact unit economics","Create interactive tools where users expect near-instant feedback without sacrificing reasoning quality","Scale inference across thousands of concurrent requests without infrastructure costs spiraling"],"best_for":["Startups and scale-ups optimizing for cost-per-inference in high-volume applications","Teams building real-time interactive applications with strict latency SLAs","Enterprises migrating from expensive flagship models to more efficient alternatives"],"limitations":["Optimization for speed and cost may reduce performance on complex reasoning tasks requiring full model capacity","Quantization and early-exit mechanisms introduce non-deterministic behavior; identical inputs may produce slightly different outputs","Performance gains diminish on tasks where reasoning depth is critical; flagship models may still outperform on complex analysis"],"requires":["OpenRouter API key with Qwen Plus 0728 access","Cost monitoring and token counting in application layer","Acceptance of slightly variable output quality vs deterministic flagship models"],"input_types":["text","code"],"output_types":["text","code","structured data"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-plus-2025-07-28__cap_3","uri":"capability://code.generation.editing.code.understanding.and.generation.with.extended.context","name":"code understanding and generation with extended context","description":"Analyzes and generates code by leveraging the 1M token context to understand entire codebases, dependency graphs, and architectural patterns without chunking. Uses syntax-aware tokenization and code-specific attention patterns to identify relevant code sections, maintain consistency with existing patterns, and generate contextually appropriate solutions that integrate seamlessly with surrounding code.","intents":["Generate code that respects existing codebase patterns, naming conventions, and architectural decisions","Refactor large functions or modules while maintaining compatibility with dependent code","Understand cross-file dependencies and generate code that properly imports and integrates with existing modules","Analyze entire test suites to generate new tests that follow established patterns"],"best_for":["Developers working on large codebases where context-aware generation is critical","Teams using AI-assisted refactoring where understanding full codebase structure is essential","Startups building AI-powered code review or generation tools"],"limitations":["Code generation quality depends heavily on code quality and clarity of existing codebase; poorly structured code may confuse the model","Syntax-aware tokenization adds ~50-100ms overhead vs generic tokenization","No real-time compilation or execution feedback; generated code may have runtime errors despite syntactic correctness"],"requires":["OpenRouter API key","Code files formatted as text (source code, not compiled binaries)","Language-specific syntax knowledge in the model (supports Python, JavaScript, Java, C++, Go, Rust, etc.)"],"input_types":["code","text"],"output_types":["code","text"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-plus-2025-07-28__cap_4","uri":"capability://data.processing.analysis.structured.data.extraction.and.transformation","name":"structured data extraction and transformation","description":"Extracts and transforms unstructured text into structured formats (JSON, CSV, XML) by using prompt-based schema specification and validation. The model parses natural language descriptions of desired output structure, applies extraction rules across large documents within the context window, and generates valid structured output with minimal post-processing required.","intents":["Extract entities, relationships, and metadata from long documents or research papers into structured databases","Transform API responses or logs into normalized data formats for downstream processing","Parse semi-structured text (emails, reports, contracts) into consistent JSON schemas","Generate synthetic structured datasets for testing or training purposes"],"best_for":["Data engineering teams building ETL pipelines with unstructured source data","Researchers extracting information from academic papers or technical documentation","Teams automating document processing workflows (invoices, contracts, forms)"],"limitations":["Extraction accuracy depends on schema clarity and document structure; ambiguous schemas produce inconsistent results","No schema validation at generation time; invalid JSON or schema violations require post-processing","Complex nested structures or very large output schemas may exceed token limits or produce incomplete results"],"requires":["OpenRouter API key","Clear JSON schema or format specification in prompt","JSON validation library for post-processing (optional but recommended)"],"input_types":["text","code","structured documents"],"output_types":["JSON","CSV","XML","structured text"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-plus-2025-07-28__cap_5","uri":"capability://text.generation.language.multi.language.text.generation.and.translation","name":"multi-language text generation and translation","description":"Generates and translates text across multiple languages by using language-specific tokenization and cross-lingual attention patterns. The model maintains semantic consistency across language boundaries, preserves tone and style during translation, and generates culturally appropriate content for target languages without explicit language-specific fine-tuning.","intents":["Translate technical documentation or user-facing content into multiple languages while preserving technical accuracy","Generate marketing copy or creative content in multiple languages with culturally appropriate messaging","Build multilingual chatbots that respond naturally in user's preferred language","Localize software interfaces or help documentation for international audiences"],"best_for":["Global companies localizing products or content for multiple markets","Startups building multilingual applications without dedicated translation teams","Content creators producing material for international audiences"],"limitations":["Translation quality varies by language pair; less common language combinations may produce lower quality","Cultural nuances and idioms may not translate perfectly; human review recommended for customer-facing content","Tone and style preservation depends on explicit prompting; implicit style transfer is unreliable"],"requires":["OpenRouter API key","Source text in supported language","Target language specification in prompt"],"input_types":["text"],"output_types":["text"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-plus-2025-07-28__cap_6","uri":"capability://planning.reasoning.reasoning.chain.decomposition.and.step.by.step.problem.solving","name":"reasoning chain decomposition and step-by-step problem solving","description":"Breaks down complex problems into intermediate reasoning steps using chain-of-thought patterns, generating explicit step-by-step solutions that improve accuracy on multi-step reasoning tasks. The model generates intermediate conclusions, validates assumptions, and backtracks when necessary, producing transparent reasoning traces that enable verification and debugging of solution logic.","intents":["Solve complex math problems or logic puzzles by showing work and intermediate steps","Debug code by tracing execution flow and identifying where assumptions break down","Analyze complex scenarios by breaking them into manageable sub-problems","Generate explainable AI outputs where reasoning transparency is required for compliance or trust"],"best_for":["Educational applications where showing work is as important as final answers","Enterprise systems requiring explainable AI for regulatory compliance","Debugging and analysis tools where understanding reasoning process is critical"],"limitations":["Step-by-step reasoning increases token usage by 2-4x vs direct answers; cost and latency increase proportionally","Intermediate steps may contain errors that compound into incorrect final answers; reasoning transparency doesn't guarantee correctness","Reasoning chains are model-generated approximations of human reasoning; may not match human problem-solving approaches"],"requires":["OpenRouter API key","Prompt engineering to explicitly request step-by-step reasoning (e.g., 'Let's think step by step')","Token budget for 2-4x increased output length"],"input_types":["text","code","math problems"],"output_types":["text","reasoning traces","step-by-step solutions"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-plus-2025-07-28__cap_7","uri":"capability://tool.use.integration.api.integration.and.function.calling.with.schema.based.dispatch","name":"api integration and function calling with schema-based dispatch","description":"Calls external APIs and functions by parsing natural language requests into structured function calls with validated parameters. The model generates function names, arguments, and execution order based on task requirements, with support for sequential chaining of multiple function calls and error handling for failed invocations.","intents":["Build agents that autonomously call APIs to fetch data, update systems, or trigger workflows","Create assistants that integrate with external tools (calculators, databases, web services) to enhance capabilities","Automate multi-step workflows that require coordinating calls across multiple APIs","Enable natural language interfaces to complex systems by translating user requests into API calls"],"best_for":["Teams building AI agents that need to interact with external systems","Developers creating natural language interfaces to APIs or databases","Enterprises automating workflows that span multiple systems"],"limitations":["Function calling accuracy depends on schema clarity; ambiguous or poorly documented schemas produce incorrect calls","No built-in error recovery; failed API calls require explicit retry logic in application layer","Sequential function calling may be inefficient for independent operations; no native parallelization"],"requires":["OpenRouter API key","Function schema definitions (JSON Schema format)","Application layer to execute function calls and return results to model","Error handling and retry logic for failed API calls"],"input_types":["text","natural language requests"],"output_types":["function calls","API requests","structured data"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-plus-2025-07-28__cap_8","uri":"capability://text.generation.language.summarization.and.content.condensation","name":"summarization and content condensation","description":"Condenses long documents, conversations, or code into concise summaries while preserving key information and context. The model identifies salient points, removes redundancy, and generates summaries at configurable abstraction levels (bullet points, paragraphs, single sentence) without losing critical details.","intents":["Generate executive summaries of long reports or research papers for quick review","Create concise meeting notes from full conversation transcripts","Produce code documentation summaries from implementation details","Condense customer feedback or support tickets into actionable insights"],"best_for":["Knowledge workers processing large volumes of information","Teams automating documentation and note-taking workflows","Researchers reviewing large bodies of literature"],"limitations":["Summarization quality depends on source document clarity; poorly written sources produce poor summaries","Configurable abstraction levels may lose important nuances; aggressive condensation risks losing critical details","Summaries are model-generated approximations; may omit information the model considers non-salient but users find important"],"requires":["OpenRouter API key","Source document or text to summarize","Summary length or abstraction level specification in prompt"],"input_types":["text","code","documents"],"output_types":["text","bullet points","structured summaries"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-plus-2025-07-28__cap_9","uri":"capability://safety.moderation.content.moderation.and.safety.filtering","name":"content moderation and safety filtering","description":"Evaluates text content for policy violations, harmful content, or safety concerns by applying learned patterns for detecting abuse, misinformation, and inappropriate material. The model classifies content against multiple safety dimensions (violence, hate speech, sexual content, etc.) and provides confidence scores and explanations for flagged content.","intents":["Filter user-generated content in platforms to prevent harmful material from being published","Audit existing content libraries for policy violations or problematic material","Provide safety feedback to content creators about potential issues with their submissions","Implement content policies consistently across multiple languages and cultural contexts"],"best_for":["Platform operators managing user-generated content at scale","Content moderation teams augmenting human review with AI assistance","Compliance teams auditing content for regulatory requirements"],"limitations":["Moderation accuracy varies by content type and cultural context; edge cases require human review","False positive rates may be high for borderline content; requires tuning confidence thresholds","Model may have biases in detecting harmful content across different demographic groups or cultural contexts"],"requires":["OpenRouter API key","Content to moderate (text format)","Policy definitions or safety guidelines in prompt","Human review process for edge cases and appeals"],"input_types":["text"],"output_types":["safety classifications","confidence scores","explanations"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"low","permissions":["OpenRouter API key with Qwen Plus 0728 model access","HTTP client capable of handling large request payloads (>5MB)","Token counting utility to stay within 1M limit","OpenRouter API key","Application logic to accumulate conversation history and pass as context","Token counter to monitor cumulative conversation length","Context documents or knowledge base content","Question to answer","Prompt engineering to request citations","OpenRouter API key with Qwen Plus 0728 access"],"failure_modes":["1M token limit still requires careful prompt engineering for optimal retrieval of relevant information within the window","Latency scales non-linearly with context size; full 1M token inputs may incur 5-10x latency vs 4K token inputs","Hybrid reasoning approach may produce less optimal outputs on tasks requiring deep reasoning over entire context vs focused reasoning on key sections","Context preservation degrades gracefully as conversation length approaches 1M tokens; oldest turns receive less attention weight","No explicit memory mechanism for facts or preferences — relies on inclusion in active context window","Requires careful prompt engineering to maintain consistent persona and reasoning style across turns","Citation accuracy depends on context clarity; ambiguous sources may produce incorrect citations","Model may hallucinate citations to non-existent sources; requires validation that citations actually exist","Questions requiring synthesis across multiple sources may produce incomplete citations","Optimization for speed and cost may reduce performance on complex reasoning tasks requiring full model capacity","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.47,"ecosystem":0.24,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:24.485Z","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-qwen-plus-2025-07-28","compare_url":"https://unfragile.ai/compare?artifact=qwen-qwen-plus-2025-07-28"}},"signature":"3tIUe+P9sm3JAUu15qprFfyIgNgl0QwkVOZ7HMBqbbSHV5o4PpxpitnW3HukyyOdq84DQ5RSBzjPk4JlRc2kCw==","signedAt":"2026-06-20T07:28:32.447Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/qwen-qwen-plus-2025-07-28","artifact":"https://unfragile.ai/qwen-qwen-plus-2025-07-28","verify":"https://unfragile.ai/api/v1/verify?slug=qwen-qwen-plus-2025-07-28","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"}}