{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-qwen-qwen3-max-thinking","slug":"qwen-qwen3-max-thinking","name":"Qwen: Qwen3 Max Thinking","type":"model","url":"https://openrouter.ai/models/qwen~qwen3-max-thinking","page_url":"https://unfragile.ai/qwen-qwen3-max-thinking","categories":["ai-agents"],"tags":["qwen","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$7.80e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-qwen-qwen3-max-thinking__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":"Qwen3-Max-Thinking implements an extended reasoning capability that separates internal deliberation from final responses using dedicated thinking tokens. The model allocates computational budget to multi-step reasoning before generating outputs, enabling it to work through complex logical chains, verify intermediate steps, and backtrack when necessary. This architecture uses reinforcement learning optimization to learn when and how deeply to reason based on task complexity.","intents":["I need the model to show its work and reasoning steps for complex problems","I want to understand why the model arrived at a particular conclusion","I need to solve problems that require multi-stage logical deduction or mathematical proof","I want to debug model reasoning when it produces unexpected outputs"],"best_for":["researchers and engineers building reasoning-dependent systems","teams solving complex technical problems requiring explainability","developers building AI agents that need transparent decision-making","educators and content creators needing step-by-step problem walkthroughs"],"limitations":["Extended thinking increases latency significantly — reasoning phases can add 5-30 seconds per request depending on problem complexity","Thinking tokens consume additional API quota and may incur higher per-token costs than standard inference","Reasoning quality degrades on tasks that don't benefit from deep deliberation (simple factual queries, creative writing)","Thinking process is not always human-interpretable — internal reasoning may use non-obvious logical paths"],"requires":["API access to Qwen3-Max-Thinking via OpenRouter or compatible endpoint","Support for extended token streaming or batch processing to handle longer response sequences","Client-side parsing logic to separate thinking tokens from final response tokens"],"input_types":["text prompts","multi-turn conversation history","structured problem statements with constraints"],"output_types":["text response with embedded thinking tokens","structured reasoning trace (if parsed)","final answer with optional reasoning justification"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-max-thinking__cap_1","uri":"capability://planning.reasoning.high.capacity.multi.domain.knowledge.reasoning","name":"high-capacity multi-domain knowledge reasoning","description":"Qwen3-Max-Thinking leverages significantly scaled model capacity (parameters and training data) to perform reasoning across diverse domains including mathematics, physics, coding, law, medicine, and abstract logic. The model uses a unified transformer architecture trained on curated multi-domain datasets with reinforcement learning to optimize for reasoning accuracy. This enables coherent reasoning across domain boundaries without task-specific fine-tuning.","intents":["I need to solve complex math problems involving multiple domains (e.g., physics + calculus)","I want to reason about code correctness and algorithmic complexity simultaneously","I need to analyze legal or medical scenarios that require cross-domain knowledge synthesis","I want a single model that can handle diverse reasoning tasks without switching models"],"best_for":["research teams working on multi-disciplinary problems","enterprise systems requiring unified reasoning across business domains","educational platforms needing comprehensive problem-solving capabilities","AI agents that must handle heterogeneous task types without model switching"],"limitations":["Larger model capacity increases inference latency and memory requirements compared to smaller models","Cross-domain reasoning can introduce hallucinations when domains interact in unexpected ways","Training data cutoff limits knowledge of recent developments in specialized fields","Performance may be uneven across domains — some domains may have received more training emphasis than others"],"requires":["Sufficient API rate limits to handle potentially longer inference times","Client infrastructure capable of processing longer response sequences","Domain-specific validation logic to catch reasoning errors in specialized contexts"],"input_types":["text prompts with domain-specific terminology","multi-part problems spanning multiple domains","code snippets with mathematical or logical constraints"],"output_types":["text explanations with domain-specific reasoning","code solutions with mathematical justifications","structured analysis combining multiple domains"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-max-thinking__cap_10","uri":"capability://tool.use.integration.api.based.inference.with.streaming.and.batch.processing","name":"api-based inference with streaming and batch processing","description":"Qwen3-Max-Thinking is accessible via OpenRouter's API, supporting both streaming and batch inference modes. The API handles authentication, rate limiting, and request routing to Qwen3 infrastructure. Streaming mode returns tokens progressively (including thinking tokens), while batch mode optimizes throughput for multiple requests. The API abstracts away model deployment complexity.","intents":["I want to integrate Qwen3-Max-Thinking into my application without managing model infrastructure","I need to process multiple reasoning requests efficiently with batch processing","I want to stream responses progressively to users for better UX","I need to access the model via standard API protocols without custom deployment"],"best_for":["startups and small teams without ML infrastructure expertise","applications requiring flexible model access without long-term commitments","teams building multi-model systems that need unified API access","developers prototyping AI features quickly"],"limitations":["API latency adds overhead compared to local inference — network round-trip time is non-negligible","Rate limits and quota restrictions may constrain throughput for high-volume applications","Streaming mode may have higher per-token costs than batch mode","Dependency on external API availability — service outages affect application availability"],"requires":["OpenRouter API key with appropriate permissions","Network connectivity to OpenRouter endpoints","Client library or HTTP client capable of handling streaming responses","Billing account with sufficient credits for inference costs"],"input_types":["text prompts","conversation histories","structured API requests with parameters"],"output_types":["streamed text tokens","complete responses (batch mode)","usage statistics and token counts","error messages and status codes"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-max-thinking__cap_2","uri":"capability://planning.reasoning.reinforcement.learning.optimized.response.generation","name":"reinforcement-learning-optimized response generation","description":"Qwen3-Max-Thinking uses reinforcement learning (RL) training to optimize response quality beyond supervised fine-tuning. The model learns reward signals based on correctness, reasoning quality, and user satisfaction, allowing it to generate responses that maximize these learned objectives. This RL layer operates on top of the base transformer, refining both reasoning paths and final outputs through iterative policy optimization.","intents":["I need responses that are not just plausible but demonstrably correct","I want the model to optimize for reasoning quality, not just fluency","I need to improve model performance on specific evaluation metrics relevant to my domain","I want the model to learn from feedback about what constitutes a good response"],"best_for":["teams building high-stakes applications where correctness is critical","researchers studying RL-based LLM alignment and optimization","enterprises with domain-specific quality metrics they want to optimize for","developers building systems where response quality directly impacts user outcomes"],"limitations":["RL optimization can overfit to training reward signals, potentially degrading performance on out-of-distribution tasks","Reward function design is non-trivial — poorly designed rewards can lead to gaming or unintended behaviors","RL training increases model complexity and may reduce interpretability of decision-making","Performance gains from RL are task-specific and may not generalize across all use cases"],"requires":["Understanding of RL training objectives and potential failure modes","Evaluation framework to measure whether RL optimization is improving desired metrics","Monitoring systems to detect reward hacking or unintended optimization side effects"],"input_types":["text prompts","problem statements with correctness criteria","feedback signals (implicit or explicit) about response quality"],"output_types":["optimized text responses","reasoning traces with RL-guided deliberation","responses ranked by learned quality metrics"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-max-thinking__cap_3","uri":"capability://planning.reasoning.complex.problem.decomposition.and.multi.step.solution.synthesis","name":"complex problem decomposition and multi-step solution synthesis","description":"Qwen3-Max-Thinking can break down complex, multi-faceted problems into constituent sub-problems, reason about each independently, and synthesize solutions that account for interactions between components. The model uses its extended reasoning capability to explicitly track problem structure, identify dependencies, and verify that sub-solutions compose correctly into a coherent whole.","intents":["I have a complex problem with multiple interrelated constraints and need a structured solution","I want the model to identify and articulate the sub-problems within a larger challenge","I need to verify that a proposed solution actually addresses all aspects of the problem","I want to understand the dependency structure of a complex system or problem"],"best_for":["systems engineers designing complex architectures","product managers breaking down feature requirements","researchers tackling multi-faceted research questions","consultants solving business problems with multiple stakeholder constraints"],"limitations":["Decomposition quality depends on problem clarity — ambiguous or poorly-specified problems may be decomposed incorrectly","Synthesis of sub-solutions can fail if dependencies between components are not correctly identified","Extended reasoning for complex decomposition significantly increases latency","Model may over-decompose simple problems, adding unnecessary complexity to the reasoning process"],"requires":["Clear problem statements with sufficient context for the model to identify structure","Ability to validate that proposed decompositions and solutions are correct","Tolerance for longer inference times due to extended reasoning phases"],"input_types":["complex problem statements","systems with multiple constraints","multi-part questions with interdependencies"],"output_types":["structured problem decomposition","sub-problem solutions with dependency annotations","integrated solution addressing all components"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-max-thinking__cap_4","uri":"capability://planning.reasoning.mathematical.reasoning.and.symbolic.computation","name":"mathematical reasoning and symbolic computation","description":"Qwen3-Max-Thinking demonstrates strong mathematical reasoning capabilities including algebraic manipulation, calculus, discrete mathematics, and proof verification. The model uses extended reasoning to work through mathematical steps explicitly, verify intermediate results, and backtrack when errors are detected. It can handle both symbolic reasoning (proving theorems) and numerical problem-solving.","intents":["I need to solve complex math problems with step-by-step verification","I want the model to verify mathematical proofs or identify errors in reasoning","I need to work with symbolic expressions and algebraic manipulation","I want to understand the mathematical reasoning behind a solution"],"best_for":["educators and students working on advanced mathematics","researchers in mathematics, physics, and engineering","developers building math-heavy applications (optimization, simulation)","tutoring systems that need to explain mathematical reasoning"],"limitations":["Performance degrades on novel mathematical domains not well-represented in training data","Symbolic computation is limited compared to dedicated computer algebra systems (CAS)","Large mathematical problems may exceed context windows or reasoning budgets","Numerical precision is limited by floating-point representation — exact symbolic computation requires external tools"],"requires":["Mathematical notation understanding (LaTeX, standard mathematical symbols)","Ability to validate mathematical correctness independently or with external tools","Tolerance for extended reasoning latency on complex proofs"],"input_types":["mathematical problems in text or LaTeX notation","proofs or mathematical statements to verify","symbolic expressions and equations"],"output_types":["step-by-step mathematical solutions","proof verification with error identification","symbolic manipulations and simplifications"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-max-thinking__cap_5","uri":"capability://code.generation.editing.code.generation.with.reasoning.based.correctness.verification","name":"code generation with reasoning-based correctness verification","description":"Qwen3-Max-Thinking generates code solutions while using extended reasoning to verify correctness, identify edge cases, and explain algorithmic choices. The model can reason about code complexity, correctness properties, and potential bugs before finalizing solutions. It supports multiple programming languages and can reason about code interactions across language boundaries.","intents":["I need code solutions with explanations of why they work","I want the model to identify potential bugs or edge cases in generated code","I need to understand the algorithmic complexity and correctness properties of a solution","I want to generate code for complex algorithms that require careful reasoning"],"best_for":["developers learning new algorithms or languages","teams building safety-critical systems where code correctness is essential","educators teaching programming and algorithms","developers working on complex algorithmic problems"],"limitations":["Generated code may not follow language-specific best practices or idioms","Extended reasoning for code verification increases latency significantly","Code generation quality varies by language — better for popular languages (Python, JavaScript, Java) than niche languages","Model cannot execute code to verify correctness — relies on reasoning alone"],"requires":["Ability to test and validate generated code independently","Understanding of the target programming language and its idioms","Tolerance for longer inference times due to reasoning-based verification"],"input_types":["natural language problem descriptions","algorithm specifications","code snippets to refactor or debug","pseudocode to implement"],"output_types":["executable code in target language","code with inline comments explaining reasoning","complexity analysis and correctness proofs","identified edge cases and potential bugs"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-max-thinking__cap_6","uri":"capability://planning.reasoning.logical.reasoning.and.constraint.satisfaction","name":"logical reasoning and constraint satisfaction","description":"Qwen3-Max-Thinking can reason about logical constraints, identify contradictions, and find solutions that satisfy multiple constraints simultaneously. The model uses extended reasoning to work through logical implications, track constraint satisfaction, and verify that proposed solutions are consistent with all stated constraints.","intents":["I need to solve constraint satisfaction problems with multiple interdependent constraints","I want to identify logical contradictions or inconsistencies in a problem statement","I need to reason about logical implications and their consequences","I want to verify that a proposed solution satisfies all stated constraints"],"best_for":["operations research and optimization teams","business analysts solving scheduling and resource allocation problems","logic puzzle enthusiasts and competitive programmers","systems requiring formal verification of constraint satisfaction"],"limitations":["Performance degrades on problems with very large constraint sets or complex constraint interactions","Model cannot guarantee optimal solutions — provides valid solutions but may not find the best one","Reasoning about constraints is limited by context window size","Model may miss subtle constraint interactions that require exhaustive search"],"requires":["Clear specification of all constraints and their relationships","Ability to validate proposed solutions against constraints","Understanding of constraint satisfaction problem structure"],"input_types":["constraint specifications in natural language or formal notation","problem statements with implicit constraints","logical statements and implications to reason about"],"output_types":["solutions satisfying all constraints","identification of contradictions or unsatisfiable constraints","constraint satisfaction verification","explanation of logical reasoning steps"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-max-thinking__cap_7","uri":"capability://planning.reasoning.multi.turn.conversational.reasoning.with.context.retention","name":"multi-turn conversational reasoning with context retention","description":"Qwen3-Max-Thinking maintains reasoning context across multiple conversation turns, allowing it to build on previous reasoning steps, reference earlier conclusions, and refine solutions iteratively. The model can track assumptions made in earlier turns and verify their consistency with new information introduced later in the conversation.","intents":["I want to have a back-and-forth conversation where the model builds on previous reasoning","I need to iteratively refine a solution based on feedback and new constraints","I want the model to remember and reference earlier reasoning steps in the conversation","I need to explore multiple solution paths and compare them within a single conversation"],"best_for":["interactive problem-solving sessions with human collaboration","iterative design and refinement workflows","educational tutoring systems requiring multi-turn interaction","debugging and troubleshooting conversations"],"limitations":["Context window limits the number of turns that can be retained — very long conversations may lose earlier context","Model may lose track of earlier reasoning if conversation becomes too complex or tangential","Reasoning budget is consumed across all turns — long conversations may exhaust reasoning capacity","Inconsistencies between earlier and later reasoning may not be automatically detected"],"requires":["Client infrastructure to maintain conversation history","Ability to manage context windows and truncate old turns if necessary","Monitoring to detect when context is lost or reasoning becomes inconsistent"],"input_types":["multi-turn conversation history","follow-up questions and refinements","new constraints or information introduced mid-conversation"],"output_types":["responses building on previous reasoning","refined solutions incorporating new information","explicit references to earlier reasoning steps","consistency verification across turns"],"categories":["planning-reasoning","text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-max-thinking__cap_8","uri":"capability://text.generation.language.natural.language.explanation.generation.for.complex.reasoning","name":"natural language explanation generation for complex reasoning","description":"Qwen3-Max-Thinking can generate clear, detailed natural language explanations of its reasoning process, making complex logical chains accessible to non-experts. The model uses its extended reasoning capability to identify the key steps in its reasoning and explain them in language appropriate to the audience's expertise level.","intents":["I need to explain complex reasoning to stakeholders who aren't domain experts","I want to generate educational content that teaches reasoning skills","I need to document the reasoning behind a decision for compliance or audit purposes","I want to make AI reasoning transparent and understandable to non-technical users"],"best_for":["educators creating explanatory content","compliance and audit teams documenting decision-making","product teams building explainable AI features","communicators translating technical reasoning for general audiences"],"limitations":["Explanations may oversimplify complex reasoning, losing important nuances","Model may struggle to explain reasoning that relies on implicit domain knowledge","Explanation quality depends on the model's ability to identify key reasoning steps","Very complex reasoning may require multiple explanation attempts to fully convey"],"requires":["Ability to specify the target audience and their expertise level","Validation that explanations are accurate and complete","Feedback mechanism to improve explanation quality"],"input_types":["complex reasoning problems","audience expertise level specifications","requests for explanation at different levels of detail"],"output_types":["natural language explanations","step-by-step reasoning walkthroughs","simplified summaries for non-experts","detailed technical explanations for experts"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-max-thinking__cap_9","uri":"capability://planning.reasoning.error.detection.and.self.correction.in.reasoning.chains","name":"error detection and self-correction in reasoning chains","description":"Qwen3-Max-Thinking can identify errors in its own reasoning, backtrack to the point of error, and pursue alternative reasoning paths. The model uses extended reasoning to verify intermediate steps, detect logical inconsistencies, and correct mistakes before finalizing responses. This self-correction capability reduces the likelihood of propagating errors through multi-step reasoning.","intents":["I want the model to catch and correct its own mistakes before providing a final answer","I need to understand where the model made an error and how it corrected it","I want to reduce the likelihood of incorrect solutions due to reasoning errors","I need to debug model reasoning when it produces unexpected outputs"],"best_for":["high-stakes applications where errors have significant consequences","research teams studying model reliability and robustness","systems requiring auditable error detection and correction","developers building safety-critical AI applications"],"limitations":["Self-correction is not guaranteed — model may fail to detect errors or correct them incorrectly","Error detection adds additional reasoning overhead, increasing latency","Model may correct correct reasoning if it misidentifies it as erroneous","Some errors (e.g., factual hallucinations) may not be detectable through reasoning alone"],"requires":["Independent verification mechanism to validate that corrections are accurate","Monitoring to detect when self-correction fails or introduces new errors","Tolerance for increased latency due to error detection and correction overhead"],"input_types":["complex reasoning problems","problems with multiple potential error points","reasoning chains to verify"],"output_types":["corrected solutions with error identification","explanation of errors and corrections","reasoning traces showing backtracking and alternative paths","confidence levels for final answers"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"low","permissions":["API access to Qwen3-Max-Thinking via OpenRouter or compatible endpoint","Support for extended token streaming or batch processing to handle longer response sequences","Client-side parsing logic to separate thinking tokens from final response tokens","Sufficient API rate limits to handle potentially longer inference times","Client infrastructure capable of processing longer response sequences","Domain-specific validation logic to catch reasoning errors in specialized contexts","OpenRouter API key with appropriate permissions","Network connectivity to OpenRouter endpoints","Client library or HTTP client capable of handling streaming responses","Billing account with sufficient credits for inference costs"],"failure_modes":["Extended thinking increases latency significantly — reasoning phases can add 5-30 seconds per request depending on problem complexity","Thinking tokens consume additional API quota and may incur higher per-token costs than standard inference","Reasoning quality degrades on tasks that don't benefit from deep deliberation (simple factual queries, creative writing)","Thinking process is not always human-interpretable — internal reasoning may use non-obvious logical paths","Larger model capacity increases inference latency and memory requirements compared to smaller models","Cross-domain reasoning can introduce hallucinations when domains interact in unexpected ways","Training data cutoff limits knowledge of recent developments in specialized fields","Performance may be uneven across domains — some domains may have received more training emphasis than others","API latency adds overhead compared to local inference — network round-trip time is non-negligible","Rate limits and quota restrictions may constrain throughput for high-volume applications","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-qwen3-max-thinking","compare_url":"https://unfragile.ai/compare?artifact=qwen-qwen3-max-thinking"}},"signature":"qnnA6+ua0KLPYOn6mgJzK+pN+u2uVHnHrCZa8EAFXqxV0XYMxIvsG5346WsRV5VOE61CsJDGmEyala972DkWBA==","signedAt":"2026-06-23T04:21:06.271Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/qwen-qwen3-max-thinking","artifact":"https://unfragile.ai/qwen-qwen3-max-thinking","verify":"https://unfragile.ai/api/v1/verify?slug=qwen-qwen3-max-thinking","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"}}