{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-moonshotai-kimi-k2-thinking","slug":"moonshotai-kimi-k2-thinking","name":"MoonshotAI: Kimi K2 Thinking","type":"model","url":"https://openrouter.ai/models/moonshotai~kimi-k2-thinking","page_url":"https://unfragile.ai/moonshotai-kimi-k2-thinking","categories":["chatbots-assistants"],"tags":["moonshotai","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$6.00e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-moonshotai-kimi-k2-thinking__cap_0","uri":"capability://planning.reasoning.extended.reasoning.with.long.horizon.planning","name":"extended reasoning with long-horizon planning","description":"Implements a multi-step reasoning framework that decomposes complex problems into intermediate reasoning steps before generating final outputs. Uses a chain-of-thought-like mechanism optimized for agentic tasks that require planning across multiple decision points, leveraging the trillion-parameter MoE architecture to maintain coherence across extended reasoning chains without token collapse.","intents":["I need an AI to break down a multi-step engineering problem and show its work before proposing a solution","I want to build an agent that can plan a sequence of API calls with dependencies and backtracking","I need reasoning transparency for auditable decision-making in high-stakes domains"],"best_for":["AI engineers building agentic systems requiring interpretable reasoning traces","Teams deploying reasoning models in regulated industries needing decision justification","Developers prototyping complex task decomposition without fine-tuning"],"limitations":["Extended reasoning increases latency significantly — expect 5-15x slower inference vs standard models for complex problems","Reasoning tokens consume quota at same rate as output tokens, increasing API costs for reasoning-heavy workloads","No built-in mechanism to constrain reasoning depth — may generate excessive intermediate steps for simple queries","Reasoning output format not standardized — parsing intermediate steps requires custom post-processing logic"],"requires":["OpenRouter API key with Kimi K2 Thinking model access","HTTP/2 capable client for streaming reasoning tokens","Timeout configuration of 60+ seconds for complex reasoning tasks"],"input_types":["text (natural language queries)","code snippets with context","structured problem statements with constraints"],"output_types":["text (reasoning trace + final answer)","structured reasoning steps (if parsed from response)","code solutions with explanation"],"categories":["planning-reasoning","agentic-systems"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-moonshotai-kimi-k2-thinking__cap_1","uri":"capability://planning.reasoning.agentic.task.decomposition.and.execution.planning","name":"agentic task decomposition and execution planning","description":"Generates structured task decomposition plans that break down high-level goals into executable subtasks with dependencies, preconditions, and success criteria. The model uses its reasoning capability to identify task ordering constraints and potential failure modes, producing outputs compatible with agentic frameworks that require explicit task graphs or DAGs for orchestration.","intents":["I need to generate a task breakdown for a complex workflow that an agent can execute step-by-step","I want the model to identify dependencies between subtasks and suggest optimal execution order","I need to detect potential failure points in a multi-step plan before execution"],"best_for":["Developers building LLM-powered workflow orchestration systems","Teams implementing hierarchical task planning for autonomous agents","Product managers prototyping complex automation workflows"],"limitations":["Task decomposition quality depends on problem clarity — ambiguous goals produce over-fragmented or under-specified subtasks","No built-in validation that generated task graphs are actually executable — requires external verification against available tools","Cannot guarantee optimal task ordering for NP-hard scheduling problems — heuristic-based rather than optimal","Task interdependencies are inferred, not formally verified — may miss circular dependencies or hidden constraints"],"requires":["OpenRouter API key","Task description with sufficient context and constraints","Optional: schema definition of available tools/actions for grounding"],"input_types":["text (goal statement)","structured task requirements with constraints","tool/capability inventory (optional)"],"output_types":["text (natural language task breakdown)","structured task lists with dependencies","execution plans with ordering"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-moonshotai-kimi-k2-thinking__cap_10","uri":"capability://planning.reasoning.strategic.decision.making.with.multi.factor.reasoning","name":"strategic decision-making with multi-factor reasoning","description":"Analyzes strategic decisions by reasoning through multiple factors, trade-offs, and long-term consequences. The model considers different stakeholder perspectives, identifies risks and opportunities, and produces decision recommendations with explicit reasoning about why certain options are preferable given the constraints and objectives.","intents":["I need to make a strategic decision and want to understand the trade-offs involved","I want to reason through different options and their long-term consequences","I need to consider multiple stakeholder perspectives on a decision"],"best_for":["Executives and managers making strategic decisions","Product teams evaluating feature trade-offs","Teams conducting scenario planning"],"limitations":["Decision reasoning is based on provided information — missing context leads to incomplete analysis","Model cannot predict future outcomes with certainty — reasoning is probabilistic and based on assumptions","Stakeholder perspectives are inferred from description, not actual stakeholder input","Long-term consequences are speculative — model cannot account for black swan events or market shifts","No integration with decision support tools or data analytics platforms"],"requires":["OpenRouter API key","Clear decision context and options","Objectives, constraints, and stakeholder information"],"input_types":["text (decision context)","option descriptions","constraints and objectives","stakeholder information"],"output_types":["text (decision analysis)","trade-off reasoning","recommendation with justification"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-moonshotai-kimi-k2-thinking__cap_2","uri":"capability://text.generation.language.multi.turn.conversational.reasoning.with.context.retention","name":"multi-turn conversational reasoning with context retention","description":"Maintains conversational state across multiple turns while preserving reasoning context, allowing follow-up questions to build on previous reasoning steps without re-computation. Implements a context window management strategy that keeps reasoning traces accessible for refinement, correction, or extension in subsequent turns without losing intermediate conclusions.","intents":["I want to ask follow-up questions that reference the model's previous reasoning without repeating context","I need to iteratively refine a solution by asking the model to reconsider specific reasoning steps","I want to build a debugging session where the model can trace back through its own reasoning"],"best_for":["Developers building interactive debugging or analysis tools","Teams implementing collaborative problem-solving interfaces","Researchers studying reasoning transparency and model interpretability"],"limitations":["Context window is finite — extended conversations will eventually require summarization or context pruning","Reasoning traces accumulate in context, increasing token consumption for each subsequent turn","No explicit mechanism to 'branch' reasoning — cannot easily explore alternative reasoning paths from a previous step","Model may conflate reasoning from different turns if context becomes ambiguous"],"requires":["OpenRouter API key","Client-side conversation state management (message history)","Sufficient context window (likely 100k+ tokens for extended reasoning sessions)"],"input_types":["text (user messages)","previous reasoning traces (implicit in context)"],"output_types":["text (reasoning + response)","refined solutions based on feedback"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-moonshotai-kimi-k2-thinking__cap_3","uri":"capability://code.generation.editing.code.generation.with.reasoning.driven.correctness.verification","name":"code generation with reasoning-driven correctness verification","description":"Generates code solutions by first reasoning through algorithmic correctness, edge cases, and implementation tradeoffs before producing the final code. The reasoning phase identifies potential bugs, performance issues, and test cases that should be considered, resulting in more robust code generation than direct synthesis. Output includes both the code and the reasoning justification for design choices.","intents":["I need to generate code for a complex algorithm and understand why the solution is correct","I want the model to identify edge cases and potential bugs before I run the code","I need to generate code with reasoning about performance tradeoffs and design decisions"],"best_for":["Solo developers building LLM-assisted coding tools","Teams using AI for code review and correctness verification","Educators teaching algorithm design with AI assistance"],"limitations":["Reasoning-first approach adds 3-5x latency compared to direct code generation models like Copilot","Reasoning may identify issues that are actually non-issues, leading to over-engineered solutions","Code generation quality still depends on problem clarity — ambiguous requirements produce ambiguous code","Reasoning traces may not catch all edge cases, especially for novel or domain-specific algorithms","No built-in code execution or testing — reasoning about correctness is theoretical, not empirical"],"requires":["OpenRouter API key","Problem statement with sufficient algorithmic detail","Optional: test cases or constraints for grounding"],"input_types":["text (problem description)","code snippets (for context or partial solutions)","pseudocode or algorithm descriptions"],"output_types":["code (multiple languages supported)","reasoning explanation","test case suggestions"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-moonshotai-kimi-k2-thinking__cap_4","uri":"capability://planning.reasoning.complex.problem.analysis.with.constraint.satisfaction.reasoning","name":"complex problem analysis with constraint satisfaction reasoning","description":"Analyzes multi-constraint problems by reasoning through constraint interactions, identifying conflicts, and finding solutions that satisfy all constraints simultaneously. Uses the extended reasoning capability to explore the constraint satisfaction problem space, backtrack when conflicts are detected, and propose solutions with explicit justification of how each constraint is satisfied.","intents":["I need to solve a scheduling or resource allocation problem with multiple conflicting constraints","I want the model to identify which constraints are in conflict and suggest trade-offs","I need to verify that a proposed solution actually satisfies all stated constraints"],"best_for":["Operations teams optimizing complex scheduling or allocation problems","Consultants analyzing multi-stakeholder requirements with conflicting goals","Researchers prototyping constraint-based reasoning without building custom solvers"],"limitations":["Reasoning about constraint satisfaction is NP-hard — model may struggle with highly constrained problems or timeout","No guarantee of optimal solutions — model finds satisficing solutions, not necessarily optimal ones","Constraint formalization must be explicit in the prompt — implicit or ambiguous constraints are missed","Cannot handle real-time constraint changes — each new constraint requires re-reasoning from scratch","No integration with formal constraint solvers — purely reasoning-based rather than hybrid symbolic-neural"],"requires":["OpenRouter API key","Explicit list of all constraints with clear definitions","Problem domain context and any known feasible solutions (for grounding)"],"input_types":["text (constraint descriptions)","structured constraint lists","problem parameters and ranges"],"output_types":["text (reasoning about constraints)","proposed solutions","constraint satisfaction verification"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-moonshotai-kimi-k2-thinking__cap_5","uri":"capability://tool.use.integration.api.integration.planning.and.tool.use.orchestration","name":"api integration planning and tool-use orchestration","description":"Reasons through multi-step API orchestration sequences, identifying which APIs to call, in what order, how to handle dependencies between calls, and how to transform data between API boundaries. The reasoning phase considers error handling, rate limiting, and fallback strategies before generating the orchestration plan, producing executable sequences compatible with agentic frameworks.","intents":["I need to generate a sequence of API calls to accomplish a complex task across multiple services","I want the model to reason about data transformations and dependencies between API calls","I need to plan error handling and fallback strategies for API orchestration"],"best_for":["Backend engineers building API orchestration layers","Teams implementing multi-service workflows without dedicated orchestration platforms","Developers prototyping complex integrations before building custom code"],"limitations":["API schemas must be provided explicitly — model cannot discover or infer API capabilities","Reasoning about rate limits and quotas is theoretical — no real-time feedback on actual API state","Generated orchestration plans are not automatically executable — require translation to actual API calls","Error handling reasoning is generic — domain-specific error recovery strategies must be explicitly taught","No built-in retry logic or circuit breaker patterns — these must be implemented separately"],"requires":["OpenRouter API key","API schemas or documentation for all services involved","Authentication credentials or tokens (not passed to model, used by executor)"],"input_types":["text (high-level task description)","API schemas (OpenAPI, GraphQL, or natural language descriptions)","data transformation requirements"],"output_types":["text (reasoning about orchestration)","structured API call sequences","data transformation specifications"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-moonshotai-kimi-k2-thinking__cap_6","uri":"capability://text.generation.language.natural.language.problem.solving.with.explanation.generation","name":"natural language problem-solving with explanation generation","description":"Solves open-ended problems expressed in natural language by reasoning through the problem space, considering multiple solution approaches, and generating detailed explanations of the reasoning process. The model produces not just answers but also the justification for why that answer is correct, making it suitable for educational contexts and situations requiring transparency.","intents":["I need to solve a complex problem and understand the reasoning behind the solution","I want to generate educational content that explains problem-solving approaches","I need to verify that a solution is correct by examining the reasoning process"],"best_for":["Educators building AI-assisted tutoring systems","Content creators generating educational materials","Teams requiring explainable AI for decision support"],"limitations":["Explanation quality depends on problem clarity — vague problems produce verbose but unclear explanations","Reasoning may be correct but explanation may not match human intuition or teaching style","Extended explanations increase token consumption significantly","No guarantee that reasoning is actually correct — model can produce confident but wrong explanations","Explanations are generated, not retrieved from a knowledge base — may miss domain-specific terminology"],"requires":["OpenRouter API key","Clear problem statement","Optional: domain context or background information"],"input_types":["text (problem statement)","natural language questions","context or background information"],"output_types":["text (solution + explanation)","step-by-step reasoning","alternative approaches"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-moonshotai-kimi-k2-thinking__cap_7","uri":"capability://code.generation.editing.debugging.and.error.analysis.with.root.cause.reasoning","name":"debugging and error analysis with root cause reasoning","description":"Analyzes code errors, system failures, or unexpected behaviors by reasoning through potential root causes, examining error traces, and identifying the most likely source of the problem. The reasoning phase considers multiple hypotheses, eliminates unlikely causes, and produces a prioritized list of debugging steps with explanations for why each step is necessary.","intents":["I have a bug and need the model to help me understand what's causing it","I want to generate a debugging plan that prioritizes the most likely root causes","I need to understand why a system is behaving unexpectedly and what to check first"],"best_for":["Developers using AI-assisted debugging tools","DevOps teams analyzing production failures","QA engineers investigating complex test failures"],"limitations":["Debugging effectiveness depends on error information quality — incomplete traces lead to speculative reasoning","Model cannot execute code or inspect live system state — all reasoning is based on provided information","Root cause analysis is probabilistic, not deterministic — model may miss the actual cause if it's unusual","Debugging suggestions are generic — domain-specific knowledge about the system must be provided explicitly","No integration with debugging tools or profilers — suggestions must be manually verified"],"requires":["OpenRouter API key","Error messages, stack traces, or system logs","Code context or system architecture description"],"input_types":["text (error messages)","code snippets","system logs or traces","reproduction steps"],"output_types":["text (root cause analysis)","debugging steps","potential fixes"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-moonshotai-kimi-k2-thinking__cap_8","uri":"capability://text.generation.language.research.synthesis.and.literature.analysis.with.reasoning","name":"research synthesis and literature analysis with reasoning","description":"Synthesizes information from multiple sources or research papers by reasoning through connections, identifying patterns, and generating coherent summaries that integrate findings across sources. The reasoning phase considers contradictions between sources, evaluates evidence quality, and produces synthesis that acknowledges uncertainty and limitations.","intents":["I need to synthesize findings from multiple research papers into a coherent summary","I want to identify patterns and connections across different sources of information","I need to understand contradictions between sources and evaluate which is more credible"],"best_for":["Researchers conducting literature reviews","Analysts synthesizing information from multiple sources","Teams building knowledge bases from diverse sources"],"limitations":["Synthesis quality depends on source quality — garbage in, garbage out applies to reasoning too","Model cannot verify claims or check citations — reasoning is based on provided text, not fact-checking","Contradictions between sources are noted but not resolved — model cannot determine ground truth","Reasoning may conflate similar concepts from different sources — requires domain expertise to validate","No integration with citation management or knowledge graphs — synthesis is text-based only"],"requires":["OpenRouter API key","Source texts or summaries to synthesize","Optional: domain context or research questions"],"input_types":["text (research papers, articles, or summaries)","multiple sources on the same topic","research questions or synthesis prompts"],"output_types":["text (synthesis summary)","pattern identification","contradiction analysis","research gaps"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-moonshotai-kimi-k2-thinking__cap_9","uri":"capability://planning.reasoning.hypothesis.generation.and.testing.with.reasoning","name":"hypothesis generation and testing with reasoning","description":"Generates multiple hypotheses to explain observations or data, reasons through the plausibility of each hypothesis, and suggests experiments or tests to validate or refute them. The reasoning phase considers alternative explanations, identifies confounding factors, and produces a prioritized list of hypotheses with testing strategies.","intents":["I have observations and need to generate hypotheses that could explain them","I want to reason about which hypothesis is most likely and what evidence would support it","I need to design experiments to test competing hypotheses"],"best_for":["Researchers designing experiments","Data scientists investigating anomalies","Teams conducting root cause analysis for complex problems"],"limitations":["Hypothesis generation is creative but not exhaustive — model may miss plausible explanations","Reasoning about hypothesis plausibility is based on prior knowledge, not empirical data","Suggested experiments may not be feasible or practical in the actual domain","Confounding factors are identified theoretically but not empirically controlled","No integration with statistical testing or experimental design tools"],"requires":["OpenRouter API key","Clear description of observations or data","Domain context and constraints"],"input_types":["text (observation descriptions)","data summaries or statistics","domain context"],"output_types":["text (hypothesis generation)","plausibility reasoning","experiment design suggestions"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"low","permissions":["OpenRouter API key with Kimi K2 Thinking model access","HTTP/2 capable client for streaming reasoning tokens","Timeout configuration of 60+ seconds for complex reasoning tasks","OpenRouter API key","Task description with sufficient context and constraints","Optional: schema definition of available tools/actions for grounding","Clear decision context and options","Objectives, constraints, and stakeholder information","Client-side conversation state management (message history)","Sufficient context window (likely 100k+ tokens for extended reasoning sessions)"],"failure_modes":["Extended reasoning increases latency significantly — expect 5-15x slower inference vs standard models for complex problems","Reasoning tokens consume quota at same rate as output tokens, increasing API costs for reasoning-heavy workloads","No built-in mechanism to constrain reasoning depth — may generate excessive intermediate steps for simple queries","Reasoning output format not standardized — parsing intermediate steps requires custom post-processing logic","Task decomposition quality depends on problem clarity — ambiguous goals produce over-fragmented or under-specified subtasks","No built-in validation that generated task graphs are actually executable — requires external verification against available tools","Cannot guarantee optimal task ordering for NP-hard scheduling problems — heuristic-based rather than optimal","Task interdependencies are inferred, not formally verified — may miss circular dependencies or hidden constraints","Decision reasoning is based on provided information — missing context leads to incomplete analysis","Model cannot predict future outcomes with certainty — reasoning is probabilistic and based on assumptions","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":"2026-05-24T12:16:24.484Z","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=moonshotai-kimi-k2-thinking","compare_url":"https://unfragile.ai/compare?artifact=moonshotai-kimi-k2-thinking"}},"signature":"Nd+bdQL7YIc5DIgekxGuCF26UOf4/8X74BPGeg8izSMv+FX6VzBIrYqVWKLyV0KAT3F7W4zR/sT/9EVuZLSoAw==","signedAt":"2026-06-20T22:47:23.514Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/moonshotai-kimi-k2-thinking","artifact":"https://unfragile.ai/moonshotai-kimi-k2-thinking","verify":"https://unfragile.ai/api/v1/verify?slug=moonshotai-kimi-k2-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"}}