{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-moonshotai-kimi-k2-0905","slug":"moonshotai-kimi-k2-0905","name":"MoonshotAI: Kimi K2 0905","type":"model","url":"https://openrouter.ai/models/moonshotai~kimi-k2-0905","page_url":"https://unfragile.ai/moonshotai-kimi-k2-0905","categories":["chatbots-assistants"],"tags":["moonshotai","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$4.00e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-moonshotai-kimi-k2-0905__cap_0","uri":"capability://text.generation.language.long.context.multilingual.text.generation.with.moe.routing","name":"long-context multilingual text generation with moe routing","description":"Generates coherent text across 200K token context windows using a Mixture-of-Experts architecture with 1 trillion total parameters and 32 expert routing. The MoE design activates only task-relevant expert subsets per token, reducing computational overhead while maintaining semantic consistency across extended conversations, documents, and code. Supports 40+ languages with unified tokenization and cross-lingual reasoning.","intents":["Generate long-form content (articles, reports, code) without losing context coherence across 200K tokens","Conduct multilingual conversations and translate between 40+ languages while preserving nuance","Process and summarize entire documents or codebases in a single request without chunking","Maintain conversation history and context awareness across extended multi-turn interactions"],"best_for":["Teams building multilingual AI assistants requiring extended context windows","Developers processing large codebases or documents in single inference passes","Content creators and researchers needing long-form generation without context loss","Organizations requiring non-English language support at scale"],"limitations":["200K context window is fixed — cannot exceed this limit per request","MoE routing adds ~50-100ms latency overhead compared to dense models for short contexts","Expert load balancing may cause uneven token distribution across sparse experts","Requires API key and rate-limited by Moonshot AI's infrastructure","No local deployment option — cloud-only access via OpenRouter"],"requires":["OpenRouter API key or direct Moonshot AI API credentials","HTTP/REST client capable of streaming responses","Support for 200K token batching in application layer","Network connectivity to Moonshot AI inference servers"],"input_types":["text (UTF-8, any language)","code (any programming language)","structured prompts with system instructions"],"output_types":["text (streaming or batch)","structured JSON (with schema constraints)","code (any language)"],"categories":["text-generation-language","multilingual-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-moonshotai-kimi-k2-0905__cap_1","uri":"capability://code.generation.editing.code.understanding.and.generation.with.structural.awareness","name":"code understanding and generation with structural awareness","description":"Analyzes and generates code across 50+ programming languages by leveraging the MoE architecture to route code-specific experts for syntax-aware completion, refactoring, and bug detection. The model maintains structural understanding of code semantics through specialized expert pathways trained on diverse codebases, enabling context-aware suggestions that respect language idioms and architectural patterns.","intents":["Generate syntactically correct code snippets in any language with proper error handling","Analyze existing code for bugs, performance issues, and architectural anti-patterns","Refactor code while preserving functionality and improving readability","Explain complex code logic and suggest improvements based on best practices"],"best_for":["Full-stack developers needing polyglot code generation across 50+ languages","Code review teams automating static analysis and architectural pattern detection","DevOps engineers generating infrastructure-as-code (Terraform, CloudFormation, Ansible)","Educational platforms teaching programming concepts with code examples"],"limitations":["Code generation quality varies by language — less common languages (Rust, Kotlin) may have lower accuracy than Python/JavaScript","Cannot execute code or verify runtime behavior — only static analysis","No built-in dependency resolution — may suggest imports that don't exist in target environment","200K context limit may truncate large monorepos or multi-file refactoring tasks","No real-time linting integration — requires post-processing validation"],"requires":["OpenRouter API key with code generation tier access","HTTP client supporting streaming for real-time code output","Language-specific linters/formatters for post-processing validation (optional but recommended)","Knowledge of target language syntax and conventions for prompt engineering"],"input_types":["code snippets (any language)","natural language descriptions of desired functionality","existing code for refactoring or analysis","error messages and stack traces"],"output_types":["code (any language)","code explanations (natural language)","refactoring suggestions with diffs","bug reports with remediation steps"],"categories":["code-generation-editing","code-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-moonshotai-kimi-k2-0905__cap_2","uri":"capability://planning.reasoning.reasoning.and.multi.step.problem.decomposition","name":"reasoning and multi-step problem decomposition","description":"Performs chain-of-thought reasoning through extended token sequences by leveraging the MoE architecture to route reasoning-specific experts that specialize in logical decomposition, constraint satisfaction, and multi-step planning. The model can break complex problems into sub-tasks, track intermediate reasoning states, and validate solutions against constraints within a single inference pass across the 200K context window.","intents":["Solve complex math problems by showing step-by-step reasoning and intermediate calculations","Break down ambiguous requirements into concrete, actionable subtasks with dependencies","Perform logical reasoning and constraint satisfaction for planning and optimization problems","Debug complex system failures by tracing root causes through multiple layers of abstraction"],"best_for":["AI agents and autonomous systems requiring multi-step task planning and execution","Educational platforms teaching problem-solving methodologies with detailed reasoning traces","Enterprise systems needing explainable AI decisions with auditable reasoning chains","Research teams analyzing complex datasets with multi-stage analytical pipelines"],"limitations":["Reasoning quality degrades on problems requiring specialized domain knowledge (advanced physics, chemistry)","No guaranteed correctness — reasoning chains can contain logical errors or circular dependencies","Computational cost scales with reasoning depth — longer chains consume more tokens and API credits","Cannot access external tools or calculators — all arithmetic must be performed within the model","Reasoning traces are not formally verifiable — require human review for critical applications"],"requires":["OpenRouter API key with reasoning model tier access","Application layer capable of parsing and extracting reasoning chains from responses","Sufficient token budget for extended reasoning (typically 2-5x tokens vs simple generation)","Human review process for validating reasoning in high-stakes applications"],"input_types":["natural language problem statements","structured constraint specifications","code or pseudocode representations of problems","multi-part questions with dependencies"],"output_types":["step-by-step reasoning traces","intermediate solution states","final answers with confidence assessments","alternative solution paths and trade-offs"],"categories":["planning-reasoning","problem-solving"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-moonshotai-kimi-k2-0905__cap_3","uri":"capability://memory.knowledge.knowledge.grounded.response.generation.with.citation.support","name":"knowledge-grounded response generation with citation support","description":"Generates responses grounded in provided context documents by maintaining semantic alignment between input passages and output text, with optional citation markers indicating source spans. The model uses attention mechanisms to track information provenance through the 200K context window, enabling builders to implement retrieval-augmented generation (RAG) pipelines where external knowledge is injected as context and traced back to sources.","intents":["Build RAG systems where responses cite specific passages from knowledge bases or documents","Generate fact-checked answers by grounding responses in provided reference materials","Create question-answering systems that attribute claims to source documents","Implement knowledge-intensive applications (legal research, medical decision support) with auditability"],"best_for":["Teams building RAG pipelines with citation requirements for compliance or transparency","Legal and medical AI systems requiring source attribution for liability protection","Customer support platforms needing to ground responses in knowledge bases","Research platforms requiring traceable evidence for claims"],"limitations":["Citation accuracy depends on input context quality — hallucinations can occur if context is ambiguous or contradictory","No automatic fact-checking — model may cite sources that don't actually support the claim","Requires external vector database or retrieval system — model alone cannot search knowledge bases","Citation format must be specified in prompt — no standardized output format","Context injection reduces available tokens for generation — trade-off between knowledge coverage and response length"],"requires":["OpenRouter API key","External vector database or retrieval system (Pinecone, Weaviate, Milvus, etc.)","Document chunking and embedding pipeline (e.g., LangChain, LlamaIndex)","Prompt engineering to specify citation format and source attribution rules","Post-processing to validate citations match actual source content"],"input_types":["user queries (natural language)","context documents (text passages, up to 200K tokens total)","structured metadata about sources (optional)"],"output_types":["text with inline citations","structured JSON with claims and source references","confidence scores per claim (optional)"],"categories":["memory-knowledge","rag-systems"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-moonshotai-kimi-k2-0905__cap_4","uri":"capability://text.generation.language.conversational.context.management.with.multi.turn.memory","name":"conversational context management with multi-turn memory","description":"Maintains coherent conversation state across extended multi-turn exchanges by treating the entire conversation history as context within the 200K token window. The model preserves speaker identity, topic continuity, and implicit context from previous turns without requiring explicit state management, enabling natural dialogue flows where references to earlier statements are resolved automatically through attention mechanisms.","intents":["Build chatbots that maintain conversation context across 100+ turns without losing coherence","Create conversational agents that reference earlier statements and build on previous reasoning","Implement customer support systems where context persists across multiple interactions","Develop interactive tutoring systems where learning context accumulates across sessions"],"best_for":["Teams building conversational AI products with long-running user sessions","Customer support platforms requiring context persistence across multiple agent handoffs","Educational platforms implementing interactive tutoring with cumulative learning context","Dialogue research systems studying long-horizon conversation dynamics"],"limitations":["Context window is finite (200K tokens) — very long conversations will eventually exceed capacity","No persistent memory across separate API calls — each request must include full conversation history","Token consumption scales linearly with conversation length — longer histories increase API costs","No automatic context summarization — builders must implement their own summarization for long conversations","Context ordering matters — model may weight recent turns more heavily than distant history"],"requires":["OpenRouter API key","Application layer managing conversation history and token counting","Strategy for handling context overflow (summarization, pruning, or session reset)","HTTP client supporting streaming for real-time response delivery","Optional: conversation state store (database) for persistence across API calls"],"input_types":["user messages (natural language)","conversation history (previous turns)","system instructions (optional, for persona/behavior specification)"],"output_types":["assistant responses (streaming or batch)","conversation metadata (turn count, token usage)","structured dialogue acts (optional, with custom parsing)"],"categories":["text-generation-language","conversation-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-moonshotai-kimi-k2-0905__cap_5","uri":"capability://data.processing.analysis.structured.output.generation.with.schema.validation","name":"structured output generation with schema validation","description":"Generates structured data (JSON, XML, YAML) that conforms to specified schemas by incorporating schema constraints into the generation process through prompt engineering and output validation. The model can be instructed to produce machine-readable outputs for specific formats, enabling integration with downstream systems that require structured data without manual parsing or transformation.","intents":["Extract structured data from unstructured text (entities, relationships, attributes)","Generate API responses in specific JSON schemas for integration with client applications","Create configuration files (YAML, TOML) from natural language specifications","Produce structured reports with consistent formatting for automated processing"],"best_for":["Data engineering teams extracting structured information from documents or logs","API developers generating responses that must conform to OpenAPI schemas","Configuration management systems generating infrastructure-as-code from specifications","Data pipeline builders requiring consistent structured outputs for downstream processing"],"limitations":["No native schema validation — model may generate invalid JSON or violate schema constraints","Requires post-processing validation and error handling for malformed outputs","Schema complexity is limited by prompt length — very large schemas may not fit in context","No guaranteed deterministic output — same input may produce slightly different JSON structures","Nested structures with many fields may cause token inefficiency or incomplete generation"],"requires":["OpenRouter API key","JSON schema definition or format specification in prompt","Post-processing validation library (jsonschema, pydantic, etc.)","Error handling and retry logic for invalid outputs","Optional: custom prompt templates for schema-specific generation"],"input_types":["natural language descriptions of desired structure","unstructured text to extract data from","JSON schema specifications","example outputs showing desired format"],"output_types":["JSON (with optional schema validation)","XML or YAML (with format specification in prompt)","CSV or TSV (with header specification)","structured error messages if generation fails"],"categories":["data-processing-analysis","structured-extraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-moonshotai-kimi-k2-0905__cap_6","uri":"capability://text.generation.language.cross.lingual.semantic.understanding.and.translation","name":"cross-lingual semantic understanding and translation","description":"Understands and translates between 40+ languages by leveraging unified multilingual embeddings and cross-lingual expert routing within the MoE architecture. The model maintains semantic equivalence across language pairs without requiring separate translation models, enabling builders to implement multilingual applications where language switching is transparent to the underlying reasoning and generation processes.","intents":["Translate content between 40+ languages while preserving semantic meaning and cultural nuance","Build multilingual chatbots that seamlessly switch between languages within conversations","Analyze sentiment, intent, or entities across multiple languages in unified pipelines","Create multilingual search systems where queries and documents in different languages are semantically aligned"],"best_for":["Global teams building products for international markets with multiple language requirements","Content platforms requiring translation at scale without separate translation services","Multilingual search and recommendation systems","International customer support systems handling queries in multiple languages"],"limitations":["Translation quality varies by language pair — high-resource pairs (English-Spanish) are better than low-resource pairs (English-Amharic)","Cultural and idiomatic nuances may be lost in translation — requires human review for marketing or sensitive content","No language detection — builders must specify source and target languages explicitly","Transliteration and script conversion may introduce errors for non-Latin scripts","Specialized terminology (medical, legal, technical) may be mistranslated without domain-specific fine-tuning"],"requires":["OpenRouter API key","Language pair specification in prompts (source and target languages)","Optional: domain-specific terminology glossaries for specialized content","Human review process for quality assurance in high-stakes translations","Post-processing for script normalization and formatting preservation"],"input_types":["text in any of 40+ supported languages","language pair specifications (source → target)","domain context (optional, for terminology accuracy)","formatting instructions (preserve line breaks, HTML tags, etc.)"],"output_types":["translated text in target language","confidence scores per translation (optional)","alternative translations with nuance explanations","terminology mappings for custom glossaries"],"categories":["text-generation-language","translation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-moonshotai-kimi-k2-0905__cap_7","uri":"capability://text.generation.language.instruction.following.and.task.adaptation","name":"instruction-following and task adaptation","description":"Follows complex, multi-part instructions and adapts behavior based on system prompts and in-context examples through instruction-tuning mechanisms that enable the model to interpret and execute diverse tasks without task-specific fine-tuning. The model can switch between different personas, output formats, and reasoning styles based on explicit instructions, enabling builders to implement flexible AI systems that handle varied use cases through prompt engineering alone.","intents":["Create task-specific AI assistants by specifying behavior through system prompts without fine-tuning","Implement multi-step workflows where each step requires different reasoning or output formats","Build adaptive systems that adjust tone, formality, and detail level based on user preferences","Develop few-shot learning systems where task behavior is specified through examples"],"best_for":["Product teams building configurable AI assistants for multiple use cases","Prompt engineering teams optimizing instruction sets for specific tasks","No-code AI platforms enabling non-technical users to create custom AI behaviors","Research teams studying instruction-following and in-context learning"],"limitations":["Instruction-following quality degrades with instruction complexity — very long or ambiguous instructions may be misinterpreted","No guaranteed instruction adherence — model may ignore or partially follow instructions","Instruction conflicts (contradictory directives) may cause unpredictable behavior","Few-shot learning requires careful example selection — poor examples degrade performance","Instruction injection attacks possible if user input is not properly sanitized"],"requires":["OpenRouter API key","Prompt engineering expertise for effective instruction design","Testing and validation framework for instruction quality assurance","Input sanitization to prevent instruction injection attacks","Optional: prompt optimization tools (e.g., DSPy, LangSmith) for iterative refinement"],"input_types":["system prompts (behavior specification)","user queries (task requests)","in-context examples (few-shot demonstrations)","structured instructions (step-by-step task definitions)"],"output_types":["task-specific outputs (format determined by instructions)","reasoning traces (if requested in instructions)","structured responses (JSON, XML, etc., if specified)","multi-part outputs (if instructions define multiple components)"],"categories":["text-generation-language","task-adaptation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-moonshotai-kimi-k2-0905__cap_8","uri":"capability://tool.use.integration.api.integration.and.function.calling.with.schema.based.routing","name":"api integration and function calling with schema-based routing","description":"Supports function calling and API integration through schema-based tool definitions that enable the model to decide when and how to invoke external functions. The model receives tool schemas as context, reasons about which tools are appropriate for a given task, and generates structured function calls that can be executed by the application layer. This enables builders to create agent systems where the model orchestrates external APIs and tools.","intents":["Build AI agents that autonomously call APIs to retrieve real-time data (weather, stock prices, news)","Create task automation systems where the model decides which tools to use for multi-step workflows","Implement question-answering systems that combine web search, database queries, and calculations","Develop interactive systems where the model can trigger actions in external systems (send emails, create tickets)"],"best_for":["Teams building autonomous AI agents with tool orchestration capabilities","Enterprise systems integrating AI with existing APIs and microservices","Chatbot platforms requiring real-time data access and action execution","Workflow automation systems where AI decides which tools to invoke"],"limitations":["Tool selection depends on schema clarity — ambiguous tool descriptions lead to incorrect function calls","No guaranteed tool execution — model may call non-existent tools or use incorrect parameters","Requires application layer to implement actual tool execution and error handling","Tool context consumes tokens — many tools reduce available context for reasoning","No built-in retry logic — failed tool calls require explicit error handling in application layer","Hallucination risk — model may invent tool parameters that don't match actual API signatures"],"requires":["OpenRouter API key","Tool/function schema definitions (JSON Schema format)","Application layer implementing tool execution and error handling","API credentials and endpoints for external tools","Validation logic to verify tool calls match actual API signatures","Optional: tool execution framework (LangChain, LlamaIndex, Anthropic SDK)"],"input_types":["user queries requesting tool-dependent tasks","tool/function schema definitions","tool execution results (for multi-step workflows)","error messages from failed tool calls"],"output_types":["structured function calls (JSON with function name and parameters)","reasoning about tool selection","final answers combining tool results with model reasoning","error messages if tool calls fail"],"categories":["tool-use-integration","function-calling"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["OpenRouter API key or direct Moonshot AI API credentials","HTTP/REST client capable of streaming responses","Support for 200K token batching in application layer","Network connectivity to Moonshot AI inference servers","OpenRouter API key with code generation tier access","HTTP client supporting streaming for real-time code output","Language-specific linters/formatters for post-processing validation (optional but recommended)","Knowledge of target language syntax and conventions for prompt engineering","OpenRouter API key with reasoning model tier access","Application layer capable of parsing and extracting reasoning chains from 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