{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-qwen-qwq-32b","slug":"qwen-qwq-32b","name":"Qwen: QwQ 32B","type":"model","url":"https://openrouter.ai/models/qwen~qwq-32b","page_url":"https://unfragile.ai/qwen-qwq-32b","categories":["chatbots-assistants"],"tags":["qwen","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$1.50e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-qwen-qwq-32b__cap_0","uri":"capability://planning.reasoning.extended.chain.of.thought.reasoning.with.explicit.thinking.tokens","name":"extended-chain-of-thought reasoning with explicit thinking tokens","description":"QwQ implements an extended reasoning capability that generates explicit intermediate thinking steps before producing final answers, using a specialized token vocabulary that separates reasoning traces from output. The model allocates computational budget to internal reasoning chains, allowing it to decompose complex problems into substeps and verify intermediate conclusions before committing to a response. This architecture enables the model to catch errors during reasoning rather than post-hoc, improving accuracy on tasks requiring multi-step logical inference.","intents":["I need a model that shows its work and can catch logical errors before finalizing answers","I want to understand how the model arrived at a conclusion for debugging or trust purposes","I'm solving problems that require multi-step reasoning and want higher accuracy than single-pass inference","I need to verify mathematical proofs or logical arguments step-by-step"],"best_for":["AI researchers and engineers building reasoning-heavy applications","teams developing autonomous agents that need interpretable decision-making","educational platforms requiring explainable problem-solving","enterprises deploying high-stakes reasoning tasks (legal analysis, financial modeling, scientific research)"],"limitations":["Extended reasoning increases latency significantly — typical response times 5-15 seconds vs 1-2 seconds for standard models","Thinking tokens consume part of the context window, reducing available space for user input and retrieved context","Reasoning quality degrades on tasks outside the model's training distribution; no guarantee of correct intermediate steps","Verbose reasoning output may be unsuitable for latency-sensitive applications or real-time user interfaces"],"requires":["API access via OpenRouter or compatible endpoint","Support for streaming or full-response buffering to handle extended outputs","Sufficient timeout configuration (30+ seconds recommended)","Token budget planning accounting for 2-4x token consumption vs standard models"],"input_types":["text","natural language questions","mathematical problems","logical reasoning tasks","code analysis requests"],"output_types":["text with embedded reasoning traces","structured reasoning chains","step-by-step solutions","annotated explanations"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwq-32b__cap_1","uri":"capability://planning.reasoning.multi.domain.logical.problem.solving.with.formal.reasoning","name":"multi-domain logical problem-solving with formal reasoning","description":"QwQ demonstrates enhanced capability across mathematical proofs, algorithmic problem-solving, and formal logic tasks by leveraging its reasoning architecture to systematically explore solution spaces. The model can handle symbolic manipulation, constraint satisfaction, and proof verification by decomposing problems into logical subgoals and applying formal reasoning patterns. This capability extends beyond pattern-matching to genuine logical inference, enabling the model to solve novel problem variants that require structural understanding rather than memorized solutions.","intents":["I need to solve competition-level math problems or algorithmic challenges that require deep reasoning","I want to verify the correctness of mathematical proofs or logical arguments","I'm building a system that needs to solve constraint satisfaction problems or formal logic puzzles","I need to generate step-by-step solutions to problems that require exploring multiple solution paths"],"best_for":["competitive programming platforms and coding interview preparation","mathematical research and theorem verification","automated reasoning systems and formal verification tools","educational platforms teaching problem-solving methodology"],"limitations":["Performance on novel problem types not well-represented in training data is unpredictable","Reasoning chains can become circular or get stuck in local reasoning loops without external guidance","No built-in symbolic math engine — relies on natural language reasoning which may lose precision on complex symbolic expressions","Reasoning quality varies significantly by domain; strongest on math/logic, weaker on open-ended creative reasoning"],"requires":["Clear problem statement with sufficient context","Tolerance for variable response times (reasoning-heavy problems may take 20+ seconds)","API key for OpenRouter or compatible service","Understanding that reasoning traces may be verbose and require parsing"],"input_types":["text","mathematical expressions","algorithmic problem descriptions","logical puzzles","code snippets for analysis"],"output_types":["step-by-step solutions","mathematical proofs","algorithm explanations","logical derivations","code solutions with reasoning"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwq-32b__cap_2","uri":"capability://text.generation.language.instruction.following.with.reasoning.aware.interpretation","name":"instruction-following with reasoning-aware interpretation","description":"QwQ implements instruction-following by first reasoning about the intent and constraints of a user request before generating a response, enabling it to handle ambiguous, multi-part, or complex instructions more accurately than models that directly generate output. The model uses its reasoning capability to parse instruction semantics, identify potential edge cases, and plan a response strategy before execution. This approach reduces hallucination and instruction-misinterpretation by forcing explicit reasoning about what the user is asking before committing to an answer.","intents":["I need a model that correctly interprets complex or ambiguous instructions without requiring excessive clarification","I want to give detailed, multi-part instructions and have the model reason about dependencies and constraints","I'm building a system where instruction-following accuracy is critical and I need to minimize misinterpretation","I need the model to flag potential ambiguities or edge cases in instructions before proceeding"],"best_for":["autonomous agent systems requiring reliable instruction interpretation","enterprise applications where instruction misinterpretation has high cost","complex workflow automation where multi-step instructions must be executed correctly","systems requiring audit trails of reasoning about instruction interpretation"],"limitations":["Reasoning overhead adds latency even for simple, unambiguous instructions","Model may over-reason on straightforward tasks, consuming tokens unnecessarily","Reasoning traces may expose uncertainty or alternative interpretations that confuse users expecting confident responses","No mechanism to request clarification — model must reason through ambiguity independently"],"requires":["API access to QwQ via OpenRouter","Acceptance of variable latency based on instruction complexity","Ability to parse and utilize reasoning traces if needed for debugging","Token budget accounting for reasoning overhead"],"input_types":["text","natural language instructions","structured task descriptions","multi-part requests"],"output_types":["text responses","structured outputs","reasoning traces with final answer","clarification requests"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwq-32b__cap_3","uri":"capability://code.generation.editing.code.generation.and.algorithm.implementation.with.verification","name":"code generation and algorithm implementation with verification","description":"QwQ generates code by first reasoning about algorithm correctness, edge cases, and implementation strategy before producing the final code. The model can generate solutions in multiple programming languages and uses its reasoning capability to verify that generated code handles boundary conditions and matches the problem specification. This approach reduces the likelihood of off-by-one errors, infinite loops, and logic bugs that are common in single-pass code generation.","intents":["I need to generate correct code for algorithmic problems with confidence in correctness","I want code generation that considers edge cases and boundary conditions explicitly","I'm building a coding assistant that needs to explain why a particular algorithm or implementation approach was chosen","I need to generate code that passes test cases and handles corner cases correctly"],"best_for":["competitive programming and coding interview preparation platforms","educational coding assistants that need to teach problem-solving methodology","automated code generation for algorithmic problems","systems requiring high-confidence code generation with explicit reasoning"],"limitations":["Code generation latency is significantly higher than standard models (5-15 seconds typical)","Reasoning traces may be verbose and require filtering for production use","No built-in testing or execution — generated code must be validated externally","Performance on non-algorithmic code (web frameworks, configuration, boilerplate) is not differentiated from standard models"],"requires":["API access to QwQ via OpenRouter","External test harness or execution environment to validate generated code","Tolerance for extended response times","Support for parsing reasoning traces if audit trail is needed"],"input_types":["text","problem descriptions","algorithm specifications","code snippets for completion or refactoring"],"output_types":["code in multiple languages (Python, C++, Java, JavaScript, etc.)","algorithm explanations","reasoning traces about implementation choices","edge case analysis"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwq-32b__cap_4","uri":"capability://tool.use.integration.api.based.inference.with.streaming.and.context.management","name":"api-based inference with streaming and context management","description":"QwQ is accessed via OpenRouter's API, providing a standardized interface for model inference with support for streaming responses, token counting, and context window management. The API handles model routing, load balancing, and provides consistent request/response formatting across different underlying model implementations. Developers can stream reasoning traces and final outputs separately, enabling real-time display of thinking process or buffering for latency-sensitive applications.","intents":["I need to integrate a reasoning model into my application via a standard REST API","I want to stream reasoning traces and final answers separately for different UI/UX patterns","I need to manage token budgets and context windows across multiple requests","I want to avoid vendor lock-in by using a model aggregation platform"],"best_for":["developers building applications that need reasoning capabilities without managing infrastructure","teams using multiple LLM providers and wanting a unified API","applications requiring streaming responses for real-time user feedback","systems with variable load that benefit from OpenRouter's load balancing"],"limitations":["API latency adds 100-500ms overhead vs direct model access","Streaming responses require handling partial tokens and potential connection interruptions","Token pricing may be higher than direct model access due to aggregation overhead","API rate limits and quota management required for production deployments"],"requires":["OpenRouter API key","HTTP client library (curl, requests, axios, etc.)","Understanding of OpenAI-compatible API format","Handling for streaming responses if using streaming mode","Token budget planning and monitoring"],"input_types":["text","JSON-formatted API requests","system prompts","conversation history"],"output_types":["text responses","streaming token sequences","token usage metadata","error responses with diagnostic information"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwq-32b__cap_5","uri":"capability://text.generation.language.context.aware.response.generation.with.reasoning.informed.content.selection","name":"context-aware response generation with reasoning-informed content selection","description":"QwQ generates contextually appropriate responses by reasoning about the user's intent, background knowledge, and the relevance of different information sources before selecting what to include in the response. The model uses its reasoning capability to evaluate whether information is directly relevant, whether additional context is needed, and how to structure the response for clarity. This enables more targeted, less verbose responses compared to models that generate all potentially relevant information.","intents":["I need responses that are tailored to my specific context and background knowledge","I want the model to reason about what information is most relevant before responding","I'm building a system where response length and relevance are critical","I need the model to identify and flag when it lacks sufficient context to answer properly"],"best_for":["conversational AI systems where context matters significantly","knowledge-intensive applications requiring targeted information retrieval","systems where response length and relevance directly impact user satisfaction","applications requiring reasoning about information necessity and relevance"],"limitations":["Reasoning about context relevance adds latency even for simple queries","Model may over-reason about context, producing verbose reasoning traces without proportional benefit","No built-in mechanism to retrieve external context — relies on context provided in prompt","Context window limitations mean reasoning about relevance becomes less effective with very large contexts"],"requires":["API access to QwQ","Sufficient context provided in prompts for reasoning to be effective","Tolerance for variable response times based on context complexity","Understanding that reasoning traces may reveal uncertainty about context relevance"],"input_types":["text","user queries with context","conversation history","background information"],"output_types":["contextually tailored text responses","reasoning about context relevance","structured answers with reasoning","clarification requests when context is insufficient"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwq-32b__cap_6","uri":"capability://planning.reasoning.error.detection.and.self.correction.through.reasoning.verification","name":"error detection and self-correction through reasoning verification","description":"QwQ implements error detection by reasoning through solutions and explicitly verifying intermediate steps before finalizing responses. The model can identify logical inconsistencies, mathematical errors, and reasoning gaps during the thinking phase and correct them before output, reducing the need for external validation or post-hoc correction. This capability is particularly effective for tasks where errors are detectable through logical verification rather than requiring external ground truth.","intents":["I need a model that catches and corrects its own errors before responding","I want to reduce the need for external validation or fact-checking of model outputs","I'm building a system where error rates directly impact reliability and I need self-correction","I need the model to flag uncertainty or potential errors in its reasoning"],"best_for":["high-stakes applications where error rates must be minimized","systems where external validation is expensive or unavailable","mathematical and logical reasoning tasks where correctness is verifiable","autonomous systems requiring self-monitoring and error detection"],"limitations":["Self-correction is limited to errors detectable through logical reasoning — cannot correct factual errors without external knowledge","Reasoning verification adds significant latency (2-3x vs single-pass models)","Model may fail to detect errors that require domain expertise or external knowledge","Over-verification can lead to excessive reasoning without proportional error reduction"],"requires":["API access to QwQ","Acceptance of extended response times for error-checking","Tasks where correctness is logically verifiable","Understanding that self-correction is probabilistic, not guaranteed"],"input_types":["text","mathematical problems","logical reasoning tasks","code for verification"],"output_types":["corrected responses","reasoning traces showing error detection and correction","confidence assessments","uncertainty flags"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwq-32b__cap_7","uri":"capability://text.generation.language.multi.turn.conversation.with.reasoning.continuity","name":"multi-turn conversation with reasoning continuity","description":"QwQ maintains reasoning continuity across multi-turn conversations by building on previous reasoning traces and conclusions in subsequent responses. The model can reference earlier reasoning steps, correct previous conclusions based on new information, and develop increasingly sophisticated reasoning as the conversation progresses. This enables more coherent long-form interactions where the model's reasoning evolves with the conversation rather than treating each turn as independent.","intents":["I need to have extended conversations where the model builds on previous reasoning","I want the model to correct or refine earlier conclusions based on new information","I'm building a conversational system where reasoning should evolve and improve over time","I need the model to maintain consistency with earlier reasoning while incorporating new context"],"best_for":["conversational AI systems requiring deep, multi-turn interactions","tutoring and educational systems where reasoning should build progressively","collaborative problem-solving systems where reasoning evolves with discussion","debugging and analysis workflows requiring iterative refinement"],"limitations":["Context window fills quickly with reasoning traces from previous turns, limiting conversation length","Model may become anchored to earlier reasoning and resist correcting previous conclusions","Reasoning continuity requires careful prompt engineering to maintain coherence","Token consumption grows quadratically with conversation length due to reasoning overhead"],"requires":["API access to QwQ","Conversation history management and context window tracking","Prompt engineering to maintain reasoning continuity across turns","Token budget planning for extended conversations"],"input_types":["text","conversation history","follow-up questions","corrections and new information"],"output_types":["multi-turn responses","reasoning traces showing evolution","corrections to previous conclusions","refined analyses"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["API access via OpenRouter or compatible endpoint","Support for streaming or full-response buffering to handle extended outputs","Sufficient timeout configuration (30+ seconds recommended)","Token budget planning accounting for 2-4x token consumption vs standard models","Clear problem statement with sufficient context","Tolerance for variable response times (reasoning-heavy problems may take 20+ seconds)","API key for OpenRouter or compatible service","Understanding that reasoning traces may be verbose and require parsing","API access to QwQ via OpenRouter","Acceptance of variable latency based on instruction complexity"],"failure_modes":["Extended reasoning increases latency significantly — typical response times 5-15 seconds vs 1-2 seconds for standard models","Thinking tokens consume part of the context window, reducing available space for user input and retrieved context","Reasoning quality degrades on tasks outside the model's training distribution; no guarantee of correct intermediate steps","Verbose reasoning output may be unsuitable for latency-sensitive applications or real-time user interfaces","Performance on novel problem types not well-represented in training data is unpredictable","Reasoning chains can become circular or get stuck in local reasoning loops without external guidance","No built-in symbolic math engine — relies on natural language reasoning which may lose precision on complex symbolic expressions","Reasoning quality varies significantly by domain; strongest on math/logic, weaker on open-ended creative reasoning","Reasoning overhead adds latency even for simple, unambiguous instructions","Model may over-reason on straightforward tasks, consuming tokens unnecessarily","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.41,"ecosystem":0.24,"match_graph":0.25,"freshness":0.9,"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":"2026-04-22T08:12:12.969Z","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-qwq-32b","compare_url":"https://unfragile.ai/compare?artifact=qwen-qwq-32b"}},"signature":"J6KGx4iHFZh6qotPpaPuTkb5FORNPphURSwfj7TOVo4vFwbFla5uddReN7Wzks98MlMuAEETwd93+xyc2dH7DQ==","signedAt":"2026-06-15T14:50:47.607Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/qwen-qwq-32b","artifact":"https://unfragile.ai/qwen-qwq-32b","verify":"https://unfragile.ai/api/v1/verify?slug=qwen-qwq-32b","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"}}