{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-qwen-qwen3-next-80b-a3b-thinking","slug":"qwen-qwen3-next-80b-a3b-thinking","name":"Qwen: Qwen3 Next 80B A3B Thinking","type":"model","url":"https://openrouter.ai/models/qwen~qwen3-next-80b-a3b-thinking","page_url":"https://unfragile.ai/qwen-qwen3-next-80b-a3b-thinking","categories":["chatbots-assistants"],"tags":["qwen","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$9.75e-8 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-qwen-qwen3-next-80b-a3b-thinking__cap_0","uri":"capability://planning.reasoning.structured.reasoning.trace.generation","name":"structured-reasoning-trace-generation","description":"Generates explicit, machine-readable thinking traces before producing final responses using an internal chain-of-thought mechanism that decomposes complex problems into intermediate reasoning steps. The model outputs structured thinking blocks (likely XML or JSON-formatted) that expose its reasoning process, enabling users to audit decision paths and identify where reasoning breaks down. This differs from hidden reasoning by making the cognitive process transparent and parseable.","intents":["I need to understand why the model arrived at a particular answer to debug incorrect reasoning","I want to extract intermediate reasoning steps to use in downstream verification or validation pipelines","I need to trace through multi-step logic to ensure correctness before acting on the model's output","I'm building an agent that needs to inspect and potentially override the model's reasoning at specific steps"],"best_for":["AI researchers and engineers building interpretable reasoning systems","teams building verification-critical applications (math, formal logic, code synthesis)","developers implementing agentic systems that need to validate reasoning before execution","organizations requiring audit trails and explainability for compliance"],"limitations":["Structured thinking output adds latency — expect 2-5x longer generation time vs. direct-answer models","Thinking traces consume significant token budget; a single complex problem may use 50k+ tokens for reasoning alone","Reasoning quality is bounded by the model's training; incorrect intermediate steps are still exposed but not corrected","No guarantee that exposed reasoning is optimal or complete — model may miss valid solution paths"],"requires":["API access to Qwen3-Next-80B-A3B-Thinking via OpenRouter or compatible endpoint","Sufficient token budget and rate limits for extended generation (80B model requires substantial compute)","Parser or schema validator for consuming structured thinking output format","Timeout configuration allowing 30-120+ seconds per request depending on problem complexity"],"input_types":["text (natural language problem statements)","code (debugging, synthesis, analysis tasks)","mathematical notation and formal logic expressions","structured problem specifications (JSON, YAML)"],"output_types":["structured thinking traces (XML or JSON-formatted reasoning blocks)","final answer or solution","intermediate step outputs","confidence scores or uncertainty markers"],"categories":["planning-reasoning","interpretability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-next-80b-a3b-thinking__cap_1","uri":"capability://planning.reasoning.multi.step.mathematical.reasoning","name":"multi-step-mathematical-reasoning","description":"Solves complex mathematical problems including proofs, symbolic manipulation, and multi-equation systems by decomposing them into sequential logical steps with explicit intermediate calculations. The model applies formal reasoning patterns (induction, contradiction, algebraic transformation) and outputs step-by-step derivations that can be validated against known mathematical rules. This capability leverages the 80B parameter scale and reasoning-first architecture to handle problems requiring deep logical chains.","intents":["I need to generate a formal proof for a mathematical theorem with each step justified","I want to solve a system of equations and see the algebraic transformations applied at each stage","I'm verifying mathematical correctness and need the model to show all intermediate calculations","I need to generate educational content that explains mathematical problem-solving step-by-step"],"best_for":["mathematicians and researchers verifying proofs and derivations","educators creating step-by-step math tutorials and problem solutions","AI systems that need to validate mathematical correctness before downstream use","competitive programming and math olympiad preparation platforms"],"limitations":["Model may produce mathematically plausible but incorrect steps; reasoning traces do not guarantee correctness","Complex proofs requiring domain-specific knowledge (abstract algebra, topology) may exceed training coverage","Symbolic manipulation is text-based; no native support for computer algebra system (CAS) integration","Very long proofs (100+ steps) may experience degradation in reasoning quality or coherence"],"requires":["API access to Qwen3-Next-80B-A3B-Thinking","Mathematical notation parser if consuming LaTeX or symbolic output","Verification system (external CAS or symbolic validator) to confirm correctness","Sufficient context window (likely 128k+ tokens) for complex multi-step problems"],"input_types":["mathematical problem statements (natural language or LaTeX)","equations and systems of equations","theorem statements with assumptions","proof sketches or partial solutions to extend"],"output_types":["step-by-step derivations with justifications","formal proofs with logical structure","intermediate calculation results","symbolic expressions and transformations"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-next-80b-a3b-thinking__cap_2","uri":"capability://code.generation.editing.code.synthesis.with.reasoning.traces","name":"code-synthesis-with-reasoning-traces","description":"Generates code solutions for complex programming problems by first reasoning through the algorithmic approach, data structure choices, and edge cases before writing implementation. The model outputs its thinking process (algorithm selection, complexity analysis, potential pitfalls) as structured traces, followed by executable code. This enables developers to understand not just the 'what' (the code) but the 'why' (design decisions and trade-offs).","intents":["I need to generate code for a complex algorithm and understand the design decisions behind it","I want to debug why my code isn't working by having the model reason through the problem first","I'm learning a new programming paradigm and need to see the reasoning behind idiomatic solutions","I need to generate code that handles edge cases and I want to verify the model considered them"],"best_for":["software engineers solving algorithmic problems (LeetCode, system design interviews)","developers debugging complex code by understanding the intended reasoning","educators teaching programming concepts with explicit reasoning traces","teams building code generation pipelines that need to validate design decisions"],"limitations":["Generated code may contain subtle bugs despite correct reasoning; reasoning traces do not guarantee executable correctness","Very large codebases (10k+ lines) exceed context window; model works best on isolated functions or modules","Language support varies; reasoning quality is strongest for Python, Java, C++; weaker for niche languages","No real-time compilation or execution feedback; generated code requires manual testing"],"requires":["API access to Qwen3-Next-80B-A3B-Thinking","Code execution environment to test generated solutions","Parser for consuming structured reasoning output (likely JSON or XML blocks)","Context window of 128k+ tokens for complex multi-file problems"],"input_types":["natural language problem descriptions","pseudocode or algorithm sketches","existing code to refactor or debug","test cases and expected outputs","performance or constraint specifications"],"output_types":["structured reasoning traces (algorithm choice, complexity analysis, edge cases)","executable code in target language","comments and documentation explaining design decisions","test cases or validation logic"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-next-80b-a3b-thinking__cap_3","uri":"capability://planning.reasoning.agentic.task.decomposition.and.planning","name":"agentic-task-decomposition-and-planning","description":"Breaks down complex, multi-step tasks into executable sub-tasks with explicit reasoning about dependencies, resource requirements, and success criteria. The model outputs a structured plan (likely DAG or sequential steps) with reasoning traces explaining why each step is necessary and how it contributes to the overall goal. This enables agents to understand not just the action sequence but the rationale behind it, improving robustness and error recovery.","intents":["I'm building an agent and need it to decompose a user request into a sequence of tool calls with explicit reasoning","I want to understand why the agent chose a particular plan so I can override it if needed","I need the agent to explain its reasoning for task ordering and dependency management","I'm debugging agent failures and need to see where the planning reasoning broke down"],"best_for":["AI engineers building multi-step agents (research, data analysis, automation)","teams implementing agentic workflows that require explainability and auditability","developers building systems where agents must justify decisions to humans","organizations deploying agents in high-stakes domains (finance, healthcare) requiring reasoning transparency"],"limitations":["Planning reasoning does not guarantee optimal task ordering; model may miss parallelization opportunities","Complex interdependencies (circular, conditional) may confuse the planning mechanism","No native integration with external task schedulers or workflow engines; requires custom orchestration","Reasoning traces add significant latency to planning phase (5-30 seconds for complex tasks)"],"requires":["API access to Qwen3-Next-80B-A3B-Thinking","Agent framework or orchestration layer to execute the generated plan","Parser for structured plan output (JSON DAG, YAML task sequences, etc.)","Tool registry or function definitions that the agent can reference"],"input_types":["natural language task descriptions","structured goal specifications (JSON, YAML)","available tools or function definitions","constraints (time, resource, dependency specifications)"],"output_types":["structured task decomposition (DAG or sequential steps)","reasoning traces explaining planning decisions","tool/function call specifications with parameters","success criteria and validation logic for each step"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-next-80b-a3b-thinking__cap_4","uri":"capability://planning.reasoning.logical.reasoning.and.constraint.satisfaction","name":"logical-reasoning-and-constraint-satisfaction","description":"Solves logic puzzles, constraint satisfaction problems, and formal reasoning tasks by explicitly working through logical implications, contradiction detection, and constraint propagation. The model outputs reasoning traces showing how it eliminates possibilities, applies logical rules, and arrives at conclusions. This capability leverages structured thinking to handle problems requiring careful logical tracking (e.g., Sudoku, graph coloring, satisfiability).","intents":["I need to solve a logic puzzle and see the reasoning steps that lead to the solution","I want to verify that a constraint satisfaction problem has been solved correctly by checking the reasoning","I'm building a system that needs to reason about logical constraints and I need transparent decision-making","I need to generate explanations for why certain logical conclusions follow from given premises"],"best_for":["puzzle enthusiasts and competitive logic puzzle solvers","AI systems that need to solve constraint satisfaction problems with explainability","formal verification and theorem proving applications","educational platforms teaching logic and reasoning"],"limitations":["Model may miss valid logical deductions or apply incorrect inference rules despite reasoning traces","Very large constraint spaces (100+ variables, 1000+ constraints) may exceed reasoning capacity","No native integration with SAT solvers or constraint programming libraries; reasoning is heuristic-based","Reasoning quality degrades on problems requiring deep backtracking or complex constraint interactions"],"requires":["API access to Qwen3-Next-80B-A3B-Thinking","Problem specification in natural language or structured format (CSP notation, logic notation)","Validator to check solutions against constraints","Sufficient token budget for reasoning-heavy problems"],"input_types":["logic puzzle descriptions (natural language)","constraint specifications (CSP format, first-order logic)","premise-conclusion pairs for logical reasoning","partially solved problems to complete"],"output_types":["step-by-step logical deductions","constraint satisfaction solutions","reasoning traces showing contradiction detection and elimination","formal proofs of logical conclusions"],"categories":["planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-next-80b-a3b-thinking__cap_5","uri":"capability://code.generation.editing.code.debugging.with.root.cause.analysis","name":"code-debugging-with-root-cause-analysis","description":"Analyzes buggy code by reasoning through execution flow, identifying where assumptions break, and tracing the root cause of failures. The model outputs reasoning traces showing how it simulates code execution, identifies incorrect logic, and explains why the bug occurs before proposing fixes. This differs from simple code review by explicitly exposing the debugging thought process.","intents":["I have a bug and I need the model to explain why it occurs, not just fix it","I want to understand the execution flow that leads to the bug so I can prevent similar issues","I'm learning debugging techniques and need to see the reasoning behind root cause analysis","I need to verify that a proposed fix actually addresses the root cause, not just the symptom"],"best_for":["software developers debugging complex issues in unfamiliar codebases","teams conducting code reviews that require understanding bug root causes","educators teaching debugging methodology and problem-solving","QA engineers analyzing test failures and understanding failure modes"],"limitations":["Model may misunderstand runtime behavior or external dependencies; reasoning is based on static code analysis","Concurrency bugs, race conditions, and timing-dependent issues are difficult to reason about from code alone","Large codebases (10k+ lines) exceed context window; debugging works best on isolated functions or modules","No access to runtime state, logs, or execution traces; reasoning is speculative"],"requires":["API access to Qwen3-Next-80B-A3B-Thinking","Source code of the buggy program","Error messages, stack traces, or reproduction steps","Context window of 128k+ tokens for complex multi-file debugging"],"input_types":["source code with bugs","error messages and stack traces","test cases that fail","expected vs. actual behavior descriptions"],"output_types":["reasoning traces showing execution flow simulation","root cause analysis and explanation","proposed fixes with justification","prevention strategies for similar bugs"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-next-80b-a3b-thinking__cap_6","uri":"capability://planning.reasoning.complex.problem.verification.and.validation","name":"complex-problem-verification-and-validation","description":"Validates solutions to complex problems by reasoning through correctness criteria, checking edge cases, and identifying potential flaws before the solution is deployed. The model outputs reasoning traces showing how it verifies each aspect of a solution (correctness, efficiency, robustness) and flags potential issues. This enables developers to catch problems early in the development cycle.","intents":["I have a solution and I need the model to verify it's correct before I deploy it","I want to understand what edge cases might break my solution so I can test them","I need to validate that my algorithm meets performance requirements","I'm reviewing code and need to identify potential failure modes or security issues"],"best_for":["developers building critical systems (finance, healthcare, security) requiring high confidence","teams conducting pre-deployment verification and validation","competitive programmers verifying solutions before submission","security engineers identifying potential vulnerabilities in code"],"limitations":["Verification is heuristic-based; model may miss subtle bugs or edge cases","No formal proof of correctness; reasoning traces do not guarantee the solution is bug-free","Performance analysis is approximate; actual runtime behavior may differ from model's reasoning","Security analysis is limited to code-level issues; does not account for infrastructure or deployment vulnerabilities"],"requires":["API access to Qwen3-Next-80B-A3B-Thinking","Solution code or algorithm description","Problem specification and requirements","Test cases or validation criteria"],"input_types":["solution code or algorithm descriptions","problem specifications and requirements","test cases and expected outputs","performance or constraint specifications"],"output_types":["verification reasoning traces","list of identified issues or edge cases","confidence assessment of solution correctness","recommendations for additional testing or validation"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-next-80b-a3b-thinking__cap_7","uri":"capability://planning.reasoning.multi.turn.conversational.reasoning","name":"multi-turn-conversational-reasoning","description":"Maintains reasoning context across multiple conversation turns, building on previous reasoning traces and conclusions to handle follow-up questions and refinements. The model tracks assumptions, intermediate results, and logical dependencies across turns, enabling coherent multi-step conversations where later responses reference and build on earlier reasoning. This requires maintaining state and context across API calls.","intents":["I'm working through a complex problem iteratively and need the model to remember my previous reasoning","I want to ask follow-up questions that build on earlier conclusions without re-explaining the context","I'm refining a solution incrementally and need the model to track changes and their implications","I need to explore alternative approaches while maintaining awareness of previous reasoning paths"],"best_for":["developers iteratively solving complex problems through conversation","researchers exploring ideas and building on previous reasoning","teams collaborating on problem-solving where context must be preserved","educators using the model as an interactive tutoring system"],"limitations":["Context window limits how much previous reasoning can be retained; very long conversations may lose early context","Model may inconsistently apply reasoning from earlier turns or forget key assumptions","No persistent memory across sessions; context is lost when conversation ends","Reasoning traces from previous turns must be explicitly included in subsequent requests, consuming token budget"],"requires":["API access to Qwen3-Next-80B-A3B-Thinking","Conversation management system to track and include previous reasoning traces","Sufficient context window (128k+ tokens) to maintain multi-turn conversation history","Token budget accounting for repeated context inclusion"],"input_types":["natural language follow-up questions","refinements or modifications to previous solutions","requests to explore alternatives","clarifications or additional constraints"],"output_types":["reasoning traces that reference previous conclusions","updated solutions incorporating previous context","explanations of how new reasoning builds on earlier work","summaries of reasoning evolution across turns"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"low","permissions":["API access to Qwen3-Next-80B-A3B-Thinking via OpenRouter or compatible endpoint","Sufficient token budget and rate limits for extended generation (80B model requires substantial compute)","Parser or schema validator for consuming structured thinking output format","Timeout configuration allowing 30-120+ seconds per request depending on problem complexity","API access to Qwen3-Next-80B-A3B-Thinking","Mathematical notation parser if consuming LaTeX or symbolic output","Verification system (external CAS or symbolic validator) to confirm correctness","Sufficient context window (likely 128k+ tokens) for complex multi-step problems","Code execution environment to test generated solutions","Parser for consuming structured reasoning output (likely JSON or XML blocks)"],"failure_modes":["Structured thinking output adds latency — expect 2-5x longer generation time vs. direct-answer models","Thinking traces consume significant token budget; a single complex problem may use 50k+ tokens for reasoning alone","Reasoning quality is bounded by the model's training; incorrect intermediate steps are still exposed but not corrected","No guarantee that exposed reasoning is optimal or complete — model may miss valid solution paths","Model may produce mathematically plausible but incorrect steps; reasoning traces do not guarantee correctness","Complex proofs requiring domain-specific knowledge (abstract algebra, topology) may exceed training coverage","Symbolic manipulation is text-based; no native support for computer algebra system (CAS) integration","Very long proofs (100+ steps) may experience degradation in reasoning quality or coherence","Generated code may contain subtle bugs despite correct reasoning; reasoning traces do not guarantee executable correctness","Very large codebases (10k+ lines) exceed context window; model works best on isolated functions or modules","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":0.75,"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-next-80b-a3b-thinking","compare_url":"https://unfragile.ai/compare?artifact=qwen-qwen3-next-80b-a3b-thinking"}},"signature":"8FCHElTtsHE9q9bImSAIcDsVQjAZnLPvojUt5rvT7HwNP92JIvZmEh10ITqZJM/9lHzmjpi9PBYr/zYvW19wCQ==","signedAt":"2026-06-19T19:31:36.983Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/qwen-qwen3-next-80b-a3b-thinking","artifact":"https://unfragile.ai/qwen-qwen3-next-80b-a3b-thinking","verify":"https://unfragile.ai/api/v1/verify?slug=qwen-qwen3-next-80b-a3b-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"}}