{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-qwen-qwen-plus","slug":"qwen-qwen-plus","name":"Qwen: Qwen-Plus","type":"model","url":"https://openrouter.ai/models/qwen~qwen-plus","page_url":"https://unfragile.ai/qwen-qwen-plus","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__cap_0","uri":"capability://text.generation.language.long.context.conversational.inference.with.131k.token.window","name":"long-context conversational inference with 131k token window","description":"Qwen-Plus processes up to 131,000 tokens in a single context window, enabling multi-turn conversations, document analysis, and code review across large codebases without context truncation. The model uses a rotary position embedding (RoPE) architecture scaled for extended sequences, allowing it to maintain coherence and reference accuracy across lengthy inputs while balancing inference latency against context depth.","intents":["I need to analyze a 50KB codebase in a single conversation without losing context","I want to have extended multi-turn conversations that reference earlier messages without summarization","I need to process entire documents or specifications in one API call for analysis or summarization"],"best_for":["developers building document-aware chatbots or code analysis tools","teams processing large technical specifications or legal documents","builders creating context-rich customer support or knowledge-base systems"],"limitations":["131K context window is fixed; inputs exceeding this are truncated, not automatically summarized","Latency increases non-linearly with context length; full 131K window may add 2-5x inference time vs. 4K context","Cost scales linearly with input tokens; long contexts increase per-request API charges significantly"],"requires":["OpenRouter API key or direct Qwen API access","HTTP client capable of handling multi-second response times","Token counting library to estimate context usage before API calls"],"input_types":["text","code","structured prompts with embedded documents"],"output_types":["text","structured analysis","code snippets"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-plus__cap_1","uri":"capability://text.generation.language.balanced.speed.multilingual.text.generation","name":"balanced-speed multilingual text generation","description":"Qwen-Plus generates text across 29+ languages with optimized inference speed through a 32B parameter architecture that balances model capacity against latency. The model uses grouped-query attention (GQA) to reduce memory bandwidth during decoding, enabling faster token generation while maintaining multilingual coherence through shared embedding spaces trained on diverse language corpora.","intents":["I need to generate responses in multiple languages without switching models","I want faster inference than larger models for real-time chat applications","I need to build a global customer support system that handles mixed-language conversations"],"best_for":["startups building multilingual chatbots with cost and latency constraints","teams needing sub-second response times for customer-facing applications","developers creating international content generation pipelines"],"limitations":["32B parameter size trades off reasoning depth vs. larger models (70B+); complex multi-step reasoning may be less reliable","Multilingual performance varies by language; low-resource languages may have lower quality than English","No fine-tuning API exposed via OpenRouter; customization requires direct model access or prompt engineering"],"requires":["OpenRouter API key or Qwen API credentials","HTTP client with connection pooling for sustained request rates","Language detection or explicit language specification in prompts for optimal multilingual routing"],"input_types":["text","prompts with language tags"],"output_types":["text","multilingual responses"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-plus__cap_2","uri":"capability://text.generation.language.cost.optimized.api.inference.with.per.token.billing","name":"cost-optimized api inference with per-token billing","description":"Qwen-Plus is accessed via OpenRouter's per-token billing model, where costs scale directly with input and output token consumption. The model is deployed on shared infrastructure with dynamic routing, meaning inference latency and availability depend on OpenRouter's load balancing and regional availability rather than dedicated capacity, making it suitable for variable-load applications.","intents":["I want to minimize API costs for a high-volume text generation application","I need predictable per-token pricing without subscription commitments","I want to compare costs across multiple models using a unified API interface"],"best_for":["bootstrapped teams and solo developers with limited budgets","applications with variable or unpredictable request volumes","builders evaluating multiple models before committing to a single provider"],"limitations":["Per-token pricing means long contexts and verbose outputs directly increase costs; no flat-rate option for predictable budgeting","Shared infrastructure means no SLA guarantees; latency and availability depend on OpenRouter's current load","No direct access to model weights or fine-tuning; customization limited to prompt engineering and few-shot examples"],"requires":["OpenRouter API key (free tier available with rate limits)","Payment method for production usage (credit card or prepaid credits)","Token counting before requests to estimate costs"],"input_types":["text"],"output_types":["text"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-plus__cap_3","uri":"capability://text.generation.language.instruction.following.and.task.specific.prompt.optimization","name":"instruction-following and task-specific prompt optimization","description":"Qwen-Plus is trained on instruction-following datasets and responds to structured prompts with high fidelity, enabling zero-shot task execution across code generation, summarization, translation, and analysis without fine-tuning. The model uses a decoder-only transformer architecture with instruction-tuning applied post-training, allowing it to interpret complex multi-step prompts and follow formatting constraints specified in natural language.","intents":["I want to generate code, summaries, or translations with a single well-crafted prompt","I need the model to follow specific output formatting (JSON, XML, markdown) without training","I want to build a task-specific API wrapper that routes different user requests to optimized prompts"],"best_for":["developers building prompt-driven applications without fine-tuning infrastructure","teams creating task-specific wrappers around a general-purpose model","builders prototyping multi-task systems that need flexible instruction handling"],"limitations":["Instruction-following quality degrades with extremely complex or ambiguous prompts; edge cases may require prompt refinement","No explicit task-specific training; performance on specialized domains (medical, legal) is lower than domain-specific models","Output formatting is best-effort; JSON or XML generation may occasionally produce malformed output requiring post-processing"],"requires":["Well-structured prompts with clear instructions and examples","Output validation logic to catch formatting errors","Understanding of prompt engineering best practices (few-shot examples, role-playing, chain-of-thought)"],"input_types":["text","structured prompts with examples"],"output_types":["text","code","structured data (JSON, XML, markdown)"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-plus__cap_4","uri":"capability://code.generation.editing.code.generation.and.technical.problem.solving","name":"code generation and technical problem-solving","description":"Qwen-Plus generates code across multiple programming languages (Python, JavaScript, Java, C++, Go, Rust, etc.) and can solve technical problems through step-by-step reasoning. The model is trained on code-heavy datasets and uses instruction-tuning to follow coding conventions, generate syntactically correct snippets, and explain logic, though it lacks real-time compilation or execution feedback and may produce subtle bugs in complex algorithms.","intents":["I need to generate boilerplate code or function implementations from natural language descriptions","I want to debug code or get explanations of how existing code works","I need to generate SQL queries, regex patterns, or configuration files from specifications"],"best_for":["developers using AI as a coding assistant for routine implementation tasks","teams building code generation tools or IDE plugins","builders creating technical documentation or tutorial systems"],"limitations":["Generated code is not guaranteed to be correct; complex algorithms, edge cases, and security-sensitive code require manual review","No access to compiler feedback or runtime errors; syntax errors may not be caught until execution","Context window of 131K tokens limits ability to analyze very large codebases; file-by-file analysis may be needed","No built-in knowledge of proprietary libraries or internal APIs; requires explicit documentation in prompts"],"requires":["Code review process or testing framework to validate generated code","Explicit language specification in prompts for optimal code generation","Context about dependencies, frameworks, and coding standards relevant to the project"],"input_types":["text","code snippets","natural language descriptions"],"output_types":["code","code explanations","technical documentation"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-plus__cap_5","uri":"capability://text.generation.language.multi.turn.conversation.state.management.with.context.preservation","name":"multi-turn conversation state management with context preservation","description":"Qwen-Plus maintains conversation state across multiple turns by accepting full message history in each API request, allowing the model to reference previous exchanges and build on prior context. The model uses standard transformer attention mechanisms to weight recent and relevant messages, but requires the client to manage conversation history explicitly (no server-side session storage), meaning all prior messages must be re-sent with each request.","intents":["I want to build a chatbot that remembers previous messages and references them naturally","I need to create a multi-turn dialogue system where context accumulates across exchanges","I want to implement conversation branching or alternative response paths based on history"],"best_for":["developers building conversational AI applications with stateless API backends","teams creating chatbot systems where conversation history is stored externally (database, vector store)","builders implementing multi-turn dialogue systems with explicit context management"],"limitations":["Client-side history management required; no server-side session storage means developers must implement conversation persistence","Re-sending full history with each request increases token consumption and API costs linearly with conversation length","Attention mechanism may dilute context quality in very long conversations (100+ turns); older messages receive less weight","No built-in conversation summarization; long conversations must be manually summarized or truncated to manage costs"],"requires":["Client-side conversation history storage (in-memory, database, or vector store)","Message formatting following OpenAI-compatible chat API schema (role, content)","Token counting to track cumulative history size and manage costs"],"input_types":["text","message history in chat format"],"output_types":["text","conversational responses"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-plus__cap_6","uri":"capability://text.generation.language.semantic.understanding.and.reasoning.for.complex.queries","name":"semantic understanding and reasoning for complex queries","description":"Qwen-Plus uses transformer-based attention mechanisms to understand semantic relationships between concepts and can perform multi-step reasoning on complex queries, such as answering questions that require combining information from multiple parts of a document or inferring implicit relationships. The model's 32B parameter capacity provides reasonable reasoning ability for most common tasks, though it may struggle with very abstract reasoning or problems requiring deep mathematical proofs.","intents":["I need to answer complex questions that require understanding relationships between multiple concepts","I want to extract insights from documents that require inference, not just keyword matching","I need to perform multi-step reasoning to solve problems or answer 'why' questions"],"best_for":["developers building question-answering systems over documents or knowledge bases","teams creating analytical tools that require semantic understanding","builders implementing search systems that go beyond keyword matching"],"limitations":["Reasoning depth is limited by 32B parameter size; very abstract or multi-step logical problems may fail","No explicit reasoning traces; model outputs conclusions without showing intermediate steps (though chain-of-thought prompting can help)","Semantic understanding is probabilistic; edge cases and ambiguous queries may produce incorrect inferences","No access to external knowledge beyond training data cutoff; cannot reason about recent events or proprietary information without explicit context"],"requires":["Well-structured queries or prompts that guide reasoning","Context or documents containing information needed for inference","Chain-of-thought prompting or explicit reasoning instructions for complex problems"],"input_types":["text","documents","questions"],"output_types":["text","answers with reasoning","inferences"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"high","permissions":["OpenRouter API key or direct Qwen API access","HTTP client capable of handling multi-second response times","Token counting library to estimate context usage before API calls","OpenRouter API key or Qwen API credentials","HTTP client with connection pooling for sustained request rates","Language detection or explicit language specification in prompts for optimal multilingual routing","OpenRouter API key (free tier available with rate limits)","Payment method for production usage (credit card or prepaid credits)","Token counting before requests to estimate costs","Well-structured prompts with clear instructions and examples"],"failure_modes":["131K context window is fixed; inputs exceeding this are truncated, not automatically summarized","Latency increases non-linearly with context length; full 131K window may add 2-5x inference time vs. 4K context","Cost scales linearly with input tokens; long contexts increase per-request API charges significantly","32B parameter size trades off reasoning depth vs. larger models (70B+); complex multi-step reasoning may be less reliable","Multilingual performance varies by language; low-resource languages may have lower quality than English","No fine-tuning API exposed via OpenRouter; customization requires direct model access or prompt engineering","Per-token pricing means long contexts and verbose outputs directly increase costs; no flat-rate option for predictable budgeting","Shared infrastructure means no SLA guarantees; latency and availability depend on OpenRouter's current load","No direct access to model weights or fine-tuning; customization limited to prompt engineering and few-shot examples","Instruction-following quality degrades with extremely complex or ambiguous prompts; edge cases may require prompt refinement","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.39,"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","compare_url":"https://unfragile.ai/compare?artifact=qwen-qwen-plus"}},"signature":"qcjgKELrnlaPMBTNlW6q+G2nZ4JzCoTKADuO14rmCtRmTcityI61+uwxxi44VscgHI+p26e8obpAI3oIUWqZBQ==","signedAt":"2026-06-23T03:33:28.076Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/qwen-qwen-plus","artifact":"https://unfragile.ai/qwen-qwen-plus","verify":"https://unfragile.ai/api/v1/verify?slug=qwen-qwen-plus","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"}}