{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-qwen-qwen-max","slug":"qwen-qwen-max","name":"Qwen: Qwen-Max ","type":"model","url":"https://openrouter.ai/models/qwen~qwen-max","page_url":"https://unfragile.ai/qwen-qwen-max","categories":["ai-agents"],"tags":["qwen","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$1.04e-6 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-qwen-qwen-max__cap_0","uri":"capability://planning.reasoning.multi.step.reasoning.with.mixture.of.experts.architecture","name":"multi-step reasoning with mixture-of-experts architecture","description":"Qwen-Max implements a large-scale Mixture-of-Experts (MoE) model architecture pretrained on over 20 trillion tokens, enabling it to route complex multi-step reasoning tasks through specialized expert networks. The MoE design allows selective activation of model capacity based on input complexity, improving inference efficiency while maintaining reasoning depth for tasks requiring chain-of-thought decomposition, mathematical problem-solving, and logical inference across multiple reasoning steps.","intents":["I need an LLM that can solve complex multi-step math problems with intermediate reasoning","I want to decompose a complex task into subtasks and have the model reason through each step","I need reliable logical inference for knowledge-intensive questions requiring multiple reasoning hops","I'm building an agent that needs to plan and execute complex workflows with intermediate validation"],"best_for":["teams building reasoning-heavy AI agents and autonomous systems","developers creating educational AI tutors requiring step-by-step explanations","enterprises needing reliable multi-step task decomposition for complex workflows"],"limitations":["MoE routing adds computational overhead during inference — slower than dense models for simple queries","No explicit chain-of-thought token budget control — may produce verbose reasoning for simple tasks","Reasoning quality degrades on highly specialized domains outside pretraining distribution","No built-in mechanism to constrain reasoning depth or token usage per step"],"requires":["API access via OpenRouter or Alibaba Cloud endpoint","Valid authentication credentials (API key)","Network connectivity to inference endpoint","Support for streaming or non-streaming HTTP requests"],"input_types":["text (natural language prompts)","structured prompts with reasoning directives","multi-turn conversation history"],"output_types":["text (natural language responses with reasoning)","structured reasoning traces (when prompted)","code snippets embedded in reasoning"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-max__cap_1","uri":"capability://text.generation.language.long.context.understanding.with.extended.token.window","name":"long-context understanding with extended token window","description":"Qwen-Max supports processing of extended input contexts through optimized attention mechanisms and positional encoding strategies, allowing it to maintain coherence and extract information across documents, conversations, and code repositories spanning tens of thousands of tokens. The model uses efficient attention patterns (likely sparse or hierarchical) to reduce quadratic complexity while preserving long-range dependency modeling for tasks like document summarization, code review across large files, and multi-document question answering.","intents":["I need to analyze a 50-page document and extract key information without losing context","I want to review a large codebase file (10k+ lines) and identify architectural issues","I need to synthesize information across multiple documents to answer a complex question","I'm building a chatbot that maintains coherent conversation history over hundreds of exchanges"],"best_for":["legal and compliance teams processing lengthy contracts and regulatory documents","software engineering teams conducting comprehensive code reviews on large files","research teams synthesizing information from multiple academic papers","customer support systems maintaining long conversation histories"],"limitations":["Exact context window size not publicly specified — may vary by deployment","Attention mechanism efficiency degrades with very long contexts (100k+ tokens) due to memory constraints","Information retrieval accuracy may degrade in middle sections of very long documents (lost-in-the-middle effect)","Longer contexts increase latency and cost proportionally"],"requires":["API access via OpenRouter or Alibaba Cloud","Sufficient request timeout (30+ seconds for very long contexts)","Token counting implementation to stay within context limits"],"input_types":["text documents (plain text, markdown, code)","multi-turn conversation histories","concatenated document collections"],"output_types":["text summaries and analyses","extracted structured information","code review comments with line references"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-max__cap_2","uri":"capability://code.generation.editing.code.generation.and.technical.problem.solving","name":"code generation and technical problem-solving","description":"Qwen-Max generates syntactically correct and logically sound code across multiple programming languages through patterns learned from diverse code repositories in its 20+ trillion token pretraining corpus. The model supports code completion, bug fixing, algorithm implementation, and architectural design discussions by leveraging its reasoning capabilities to understand problem context, consider edge cases, and produce idiomatic solutions. Integration with OpenRouter enables streaming code output for real-time IDE integration.","intents":["I need to generate boilerplate code for a new microservice in Python/Go/Rust","I want the model to debug a failing function and explain the root cause","I need help designing the architecture for a complex system and implementing key components","I'm building an IDE plugin that streams code completions as I type"],"best_for":["individual developers and small teams using AI-assisted coding workflows","DevOps engineers generating infrastructure-as-code templates","educators teaching programming with AI-assisted explanations","teams building AI-powered IDE extensions and code editors"],"limitations":["Code generation quality varies significantly by language — best for Python, JavaScript, Go; weaker for niche languages","No built-in code execution or validation — generated code must be tested before deployment","May generate plausible-sounding but incorrect solutions for novel algorithmic problems","Security vulnerabilities (SQL injection, hardcoded credentials) not automatically detected in generated code","No awareness of project-specific conventions or internal libraries without explicit context"],"requires":["API key for OpenRouter or Alibaba Cloud","Programming language knowledge to validate generated code","Testing infrastructure to verify correctness before production use"],"input_types":["natural language problem descriptions","code snippets with context","error messages and stack traces","architectural diagrams or specifications"],"output_types":["code in multiple languages (Python, JavaScript, Go, Rust, Java, C++, etc.)","code explanations and documentation","refactoring suggestions","test cases and examples"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-max__cap_3","uri":"capability://text.generation.language.multilingual.understanding.and.generation","name":"multilingual understanding and generation","description":"Qwen-Max processes and generates text across multiple languages (Chinese, English, and others) through a unified transformer architecture with language-agnostic tokenization and cross-lingual embeddings learned during pretraining on 20+ trillion tokens. The model maintains reasoning coherence across language boundaries, enabling translation-adjacent tasks, multilingual document analysis, and code-switching scenarios without explicit language detection or separate model invocation.","intents":["I need to analyze documents in Chinese and English simultaneously without switching models","I want to translate technical documentation while preserving code snippets and formatting","I'm building a customer support system that handles queries in multiple languages","I need to extract information from multilingual sources and synthesize in a target language"],"best_for":["global teams operating across multiple language regions","enterprises with multilingual customer bases and documentation","translation and localization service providers","international research teams analyzing multilingual sources"],"limitations":["Performance varies significantly by language — Chinese and English are strongest; weaker for low-resource languages","No explicit language identification output — requires context to determine input language","Translation quality may not match specialized translation models for literary or highly technical content","Code-switching (mixing languages) may confuse the model in some contexts","Cultural nuances and idioms may not translate accurately without explicit instruction"],"requires":["API access via OpenRouter or Alibaba Cloud","UTF-8 text encoding support","Optional: language identification preprocessing for better results"],"input_types":["text in Chinese, English, or other supported languages","mixed-language documents with code-switching","multilingual conversation histories"],"output_types":["text in target language","translations with preserved formatting","multilingual summaries and analyses"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-max__cap_4","uri":"capability://data.processing.analysis.structured.data.extraction.and.schema.aware.generation","name":"structured data extraction and schema-aware generation","description":"Qwen-Max can extract structured information from unstructured text and generate data conforming to specified schemas through prompt engineering and few-shot examples, leveraging its reasoning capabilities to understand complex extraction rules and validate output against constraints. While not natively schema-aware like some specialized models, it can be guided through detailed instructions to produce JSON, CSV, or domain-specific structured formats with reasonable consistency for semi-structured extraction tasks.","intents":["I need to extract entities (names, dates, amounts) from legal documents and output as JSON","I want to parse product information from e-commerce listings into a structured database format","I'm building a data pipeline that converts unstructured logs into structured events","I need to validate that generated content conforms to a specific schema before storing"],"best_for":["data engineering teams building ETL pipelines with LLM-based extraction","business intelligence teams extracting insights from unstructured documents","startups prototyping data extraction without building custom NLP models","teams migrating from regex-based extraction to LLM-based approaches"],"limitations":["No native schema validation — output may not strictly conform to specified format without careful prompting","Extraction accuracy degrades with complex nested schemas or highly domain-specific formats","Hallucination risk when extracting information not present in source text","Performance on structured extraction is slower than specialized extraction models","Requires careful prompt engineering and validation — not suitable for mission-critical extraction without human review"],"requires":["API access via OpenRouter or Alibaba Cloud","Clear schema definition or examples in prompt","Post-processing validation logic to ensure output format compliance","Optional: few-shot examples to guide extraction behavior"],"input_types":["unstructured text documents","semi-structured data (HTML, markdown)","natural language descriptions of extraction rules"],"output_types":["JSON objects and arrays","CSV/TSV formatted data","domain-specific structured formats","validation error messages"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-max__cap_5","uri":"capability://text.generation.language.conversational.ai.with.multi.turn.context.management","name":"conversational ai with multi-turn context management","description":"Qwen-Max maintains coherent multi-turn conversations by processing full conversation history as context, enabling it to track conversation state, reference previous exchanges, and adapt responses based on established context and user preferences. The model uses attention mechanisms to weight recent messages more heavily while maintaining awareness of earlier context, supporting natural dialogue flows for chatbots, customer support, and interactive applications without explicit state management.","intents":["I'm building a customer support chatbot that remembers customer context across multiple exchanges","I need a conversational AI that can reference earlier parts of the conversation naturally","I want to create an interactive tutoring system that adapts explanations based on student responses","I'm developing a personal assistant that learns user preferences and communication style over time"],"best_for":["customer support and help desk automation teams","educational technology companies building interactive tutors","conversational AI startups and chatbot platforms","enterprises building internal knowledge assistants"],"limitations":["No persistent memory across sessions — conversation history must be provided in each request","Token usage grows linearly with conversation length — long conversations become expensive","Context window limits conversation history to ~20k-30k tokens (exact limit not specified)","No built-in mechanism to summarize or compress old conversation history","May lose important context from very early in long conversations due to attention distribution"],"requires":["API access via OpenRouter or Alibaba Cloud","Application-level conversation history management","Token counting to track context window usage","Optional: conversation summarization logic for very long conversations"],"input_types":["user messages (text)","full conversation history (array of messages with roles)","system prompts defining assistant behavior"],"output_types":["assistant responses (text)","structured conversation metadata","conversation summaries (when requested)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-max__cap_6","uri":"capability://text.generation.language.instruction.following.and.task.specific.adaptation","name":"instruction-following and task-specific adaptation","description":"Qwen-Max follows detailed instructions and adapts its behavior to task-specific requirements through instruction tuning applied during model training, enabling it to handle diverse tasks (summarization, translation, question-answering, creative writing) within a single model without task-specific fine-tuning. The model interprets natural language instructions, respects output format constraints, and adjusts tone and style based on explicit guidance, making it suitable for building flexible AI systems that handle multiple use cases.","intents":["I need one model to handle summarization, translation, and Q&A without switching between models","I want to specify output format (bullet points, JSON, markdown) and have the model comply","I'm building a system that adapts AI responses based on user-provided instructions","I need the model to adjust tone (formal, casual, technical) based on context"],"best_for":["teams building multi-purpose AI assistants and platforms","enterprises consolidating multiple specialized models into a single deployment","developers prototyping diverse AI applications quickly","organizations seeking flexibility without maintaining multiple model variants"],"limitations":["Instruction-following quality degrades with ambiguous or contradictory instructions","Complex format constraints (nested structures, specific delimiters) may not be followed perfectly","No guarantee of instruction compliance — may require validation and retry logic","Instruction injection vulnerabilities possible if user instructions are not sanitized","Performance on niche tasks may be lower than specialized models trained specifically for those tasks"],"requires":["API access via OpenRouter or Alibaba Cloud","Clear, well-structured instructions in prompts","Output validation logic to verify instruction compliance","Optional: few-shot examples to clarify expected behavior"],"input_types":["natural language instructions","task descriptions","content to process (text, code, data)","format specifications"],"output_types":["task-specific outputs (summaries, translations, answers, etc.)","formatted responses (JSON, markdown, plain text, etc.)","style-adapted content (formal, casual, technical, etc.)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-max__cap_7","uri":"capability://text.generation.language.knowledge.grounded.question.answering.with.reasoning","name":"knowledge-grounded question-answering with reasoning","description":"Qwen-Max answers questions by combining knowledge from its pretraining (20+ trillion tokens) with reasoning capabilities to synthesize information, handle multi-hop questions, and acknowledge knowledge limitations. The model can answer factual questions, explain concepts, and reason through complex scenarios, though without real-time information access or explicit knowledge base integration. It uses chain-of-thought reasoning to break down complex questions and provide transparent reasoning traces.","intents":["I need an AI that can answer technical questions about programming, architecture, and best practices","I want to build a knowledge assistant that explains complex concepts with examples","I need the model to answer multi-hop questions requiring synthesis of multiple facts","I'm building a system that should acknowledge when it doesn't know something rather than hallucinate"],"best_for":["technical support and documentation teams","educational platforms and online learning systems","knowledge management and internal wiki systems","research and analysis teams needing synthesis of known information"],"limitations":["Knowledge cutoff date not specified — may lack recent information (post-2024)","Hallucination risk on obscure or specialized topics outside pretraining distribution","No real-time information access — cannot answer questions about current events","No explicit knowledge base integration — cannot cite specific sources or documents","Reasoning traces may be verbose and include incorrect intermediate steps that are later corrected"],"requires":["API access via OpenRouter or Alibaba Cloud","Optional: external knowledge base or retrieval system for grounding answers","Fact-checking infrastructure for mission-critical applications"],"input_types":["natural language questions","multi-part questions requiring synthesis","requests for explanations and examples"],"output_types":["natural language answers","reasoning traces and explanations","confidence indicators (implicit in language)","citations (when prompted)"],"categories":["text-generation-language","planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-max__cap_8","uri":"capability://text.generation.language.creative.writing.and.content.generation","name":"creative writing and content generation","description":"Qwen-Max generates creative content including stories, poetry, marketing copy, and other narrative forms by leveraging language patterns and stylistic knowledge from its pretraining corpus. The model can adapt tone, style, and genre based on instructions, generate content in multiple languages, and maintain narrative coherence across longer pieces. It supports iterative refinement through multi-turn conversations, enabling users to request revisions and variations.","intents":["I need to generate marketing copy and product descriptions at scale","I want to create story outlines and expand them into full narratives","I'm building a content creation platform that generates blog posts and articles","I need to generate creative variations of existing content for A/B testing"],"best_for":["marketing and content creation teams","publishing and media companies","creative agencies and freelancers","e-commerce platforms generating product descriptions"],"limitations":["Generated content may lack originality and reuse patterns from training data","Creative quality is subjective and may not meet professional standards without revision","Longer creative pieces may lose narrative coherence or consistency","No built-in plagiarism detection — generated content should be checked for originality","Style adaptation may be inconsistent across longer pieces"],"requires":["API access via OpenRouter or Alibaba Cloud","Human editorial review for quality assurance","Optional: plagiarism detection tools for compliance"],"input_types":["creative briefs and prompts","style and tone specifications","existing content for variation or expansion","narrative outlines and story beats"],"output_types":["creative text (stories, poetry, marketing copy)","content variations and alternatives","narrative outlines and summaries","styled content (formal, casual, brand voice, etc.)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-max__cap_9","uri":"capability://tool.use.integration.api.based.inference.with.streaming.and.batch.processing","name":"api-based inference with streaming and batch processing","description":"Qwen-Max is accessed exclusively through OpenRouter's API or Alibaba Cloud endpoints, supporting both streaming (real-time token-by-token output) and non-streaming (complete response) inference modes. The API abstracts away model deployment complexity, handling load balancing, rate limiting, and infrastructure management. Streaming enables real-time UI updates and lower perceived latency, while batch processing supports asynchronous workflows and cost optimization.","intents":["I want to integrate Qwen-Max into my application without managing infrastructure","I need streaming responses for real-time chatbot UI updates","I'm building a batch processing pipeline for analyzing thousands of documents","I want to use Qwen-Max without worrying about GPU provisioning or model deployment"],"best_for":["startups and small teams without ML infrastructure expertise","companies seeking managed inference without operational overhead","applications requiring real-time streaming responses","teams building multi-model systems using OpenRouter's unified API"],"limitations":["Vendor lock-in to OpenRouter or Alibaba Cloud — no local deployment option","API latency depends on external service availability and network conditions","Streaming adds per-token overhead compared to batch processing","Rate limiting and quota constraints may apply based on pricing tier","No fine-tuning or model customization available through API"],"requires":["API key for OpenRouter or Alibaba Cloud","HTTP client library (REST API)","Network connectivity to inference endpoint","Optional: OpenRouter SDK for language-specific integration"],"input_types":["HTTP POST requests with JSON payload","messages array (conversation history)","system prompts and parameters"],"output_types":["streaming: server-sent events (SSE) with token-by-token output","non-streaming: complete JSON response with full output","usage statistics (tokens, cost)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["API access via OpenRouter or Alibaba Cloud endpoint","Valid authentication credentials (API key)","Network connectivity to inference endpoint","Support for streaming or non-streaming HTTP requests","API access via OpenRouter or Alibaba Cloud","Sufficient request timeout (30+ seconds for very long contexts)","Token counting implementation to stay within context limits","API key for OpenRouter or Alibaba Cloud","Programming language knowledge to validate generated code","Testing infrastructure to verify correctness before production use"],"failure_modes":["MoE routing adds computational overhead during inference — slower than dense models for simple queries","No explicit chain-of-thought token budget control — may produce verbose reasoning for simple tasks","Reasoning quality degrades on highly specialized domains outside pretraining distribution","No built-in mechanism to constrain reasoning depth or token usage per step","Exact context window size not publicly specified — may vary by deployment","Attention mechanism efficiency degrades with very long contexts (100k+ tokens) due to memory constraints","Information retrieval accuracy may degrade in middle sections of very long documents (lost-in-the-middle effect)","Longer contexts increase latency and cost proportionally","Code generation quality varies significantly by language — best for Python, JavaScript, Go; weaker for niche languages","No built-in code execution or validation — generated code must be tested before deployment","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.45,"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-max","compare_url":"https://unfragile.ai/compare?artifact=qwen-qwen-max"}},"signature":"9x2rQ1yyB8pjxYufHqrNNx9jNc8K+YQWW5QdIiDb0ZMpebz/E7KCo5MdHH/QK88WeB2csCOwYbwaeHziLMJJDQ==","signedAt":"2026-06-21T13:01:57.079Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/qwen-qwen-max","artifact":"https://unfragile.ai/qwen-qwen-max","verify":"https://unfragile.ai/api/v1/verify?slug=qwen-qwen-max","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"}}