{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-xiaomi-mimo-v2-pro","slug":"xiaomi-mimo-v2-pro","name":"Xiaomi: MiMo-V2-Pro","type":"model","url":"https://openrouter.ai/models/xiaomi~mimo-v2-pro","page_url":"https://unfragile.ai/xiaomi-mimo-v2-pro","categories":["ai-agents"],"tags":["xiaomi","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$1.00e-6 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-xiaomi-mimo-v2-pro__cap_0","uri":"capability://planning.reasoning.long.context.agentic.reasoning.with.1m.token.window","name":"long-context agentic reasoning with 1m token window","description":"Processes up to 1 million tokens in a single context window, enabling agents to maintain extended conversation histories, large document sets, and complex multi-step reasoning chains without context truncation. The model architecture supports this through optimized attention mechanisms and memory-efficient transformer implementations, allowing agents to reference prior interactions and accumulated knowledge across extended sessions without losing critical context.","intents":["Build agents that maintain coherent reasoning across 50+ interaction turns without losing earlier context","Process entire codebases or documentation sets in a single inference pass for comprehensive analysis","Implement multi-document RAG systems where all retrieved chunks fit in a single context window","Create long-running autonomous agents that accumulate and reference task history over hours of operation"],"best_for":["Teams building autonomous agents requiring extended reasoning chains","Developers implementing document-heavy RAG systems with large retrieval sets","Organizations processing large codebases or knowledge bases in single inference passes","AI researchers experimenting with long-horizon planning and memory-augmented reasoning"],"limitations":["1M token window increases latency proportionally — inference time scales with context length, typically 2-5x slower than 4K context models","Memory requirements scale linearly with context size — requires GPU with 40GB+ VRAM for full context utilization","Attention computation becomes bottleneck at maximum context — practical throughput degrades significantly above 500K tokens","No built-in context compression or summarization — developers must manage context manually to avoid token waste"],"requires":["OpenRouter API key with Xiaomi model access","HTTP/2 client supporting streaming responses","GPU infrastructure with minimum 40GB VRAM for local deployment (if self-hosted)","Token counting library compatible with Xiaomi tokenizer for accurate context budgeting"],"input_types":["text","code","structured prompts with system instructions"],"output_types":["text","structured reasoning chains","code generation","JSON-formatted agent actions"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-xiaomi-mimo-v2-pro__cap_1","uri":"capability://tool.use.integration.multi.turn.agent.orchestration.with.native.function.calling","name":"multi-turn agent orchestration with native function calling","description":"Supports structured function calling and tool invocation within agentic loops, enabling the model to autonomously decide when to call external APIs, execute code, or delegate tasks. The model outputs structured JSON-formatted tool calls that integrate with standard agent frameworks, handling the decision logic for tool selection, parameter binding, and execution sequencing without requiring external routing layers.","intents":["Build agents that autonomously call APIs, databases, or code execution environments based on task requirements","Implement multi-step workflows where the model decides tool sequencing and parameter passing","Create agents that can introspect their own capabilities and select appropriate tools from a registry","Develop systems where the model handles both reasoning and tool orchestration in a single loop"],"best_for":["Teams building ReAct-style agents with tool-use loops","Developers implementing autonomous workflow systems with external integrations","Organizations deploying agents across multiple APIs and data sources","AI engineers prototyping complex multi-step reasoning with external tool dependencies"],"limitations":["Function calling output format may require post-processing to handle edge cases or malformed JSON","No built-in retry logic for failed tool calls — agents must implement their own error handling and fallback strategies","Tool registry must be managed externally — the model doesn't persist or learn new tools across sessions","Latency per tool-use cycle includes model inference + tool execution time — can exceed 5-10 seconds for complex chains"],"requires":["OpenRouter API key with function-calling support enabled","Agent framework compatible with JSON-formatted tool calls (e.g., LangChain, LlamaIndex, custom implementations)","Tool registry or API gateway to handle function execution and result formatting","Error handling layer to manage failed tool calls and retry logic"],"input_types":["text prompts with tool descriptions","structured tool schemas (JSON schema format)","prior tool execution results"],"output_types":["JSON-formatted function calls with parameters","reasoning text explaining tool selection","structured agent actions"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-xiaomi-mimo-v2-pro__cap_2","uri":"capability://code.generation.editing.code.generation.and.analysis.with.multi.language.support","name":"code generation and analysis with multi-language support","description":"Generates, completes, and analyzes code across multiple programming languages with context-aware understanding of syntax, semantics, and best practices. The model leverages its 1T parameter scale and agentic training to produce code that integrates with existing codebases, handle complex refactoring tasks, and provide architectural recommendations based on full codebase context.","intents":["Generate production-ready code snippets with language-specific idioms and error handling","Analyze large codebases to identify architectural issues, performance bottlenecks, or security vulnerabilities","Perform multi-file refactoring tasks that require understanding of cross-file dependencies","Implement code completion that respects project structure and existing patterns"],"best_for":["Development teams using AI-assisted coding within existing projects","Solo developers building prototypes or MVPs with code generation","Organizations performing large-scale code migrations or refactoring","Teams requiring code analysis and architectural recommendations"],"limitations":["Code generation quality varies by language — likely stronger for popular languages (Python, JavaScript, Java) than niche languages","No built-in execution environment — generated code must be tested and validated separately","Context window, while large, may still be insufficient for entire enterprise codebases — requires selective file inclusion","No real-time IDE integration — requires API calls for each completion, introducing latency vs local models"],"requires":["OpenRouter API key","Code context in text format (files, snippets, or AST representations)","Language specification or inference from file extensions","Optional: code formatter or linter for post-processing generated code"],"input_types":["code snippets","full source files","natural language descriptions of desired functionality","existing codebase context"],"output_types":["code","code explanations","refactoring suggestions","architectural analysis"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-xiaomi-mimo-v2-pro__cap_3","uri":"capability://text.generation.language.conversational.ai.with.extended.dialogue.coherence","name":"conversational ai with extended dialogue coherence","description":"Maintains coherent, contextually-aware multi-turn conversations with the ability to reference prior exchanges, correct misunderstandings, and build on previous context. The 1M token window enables the model to preserve full conversation history without summarization, allowing for natural dialogue that spans dozens or hundreds of exchanges while maintaining consistency in tone, knowledge, and reasoning.","intents":["Build chatbots that maintain personality and context across extended conversations","Implement customer support agents that reference entire ticket histories without losing context","Create educational tutoring systems that adapt based on accumulated student interactions","Develop collaborative AI assistants that remember user preferences and prior decisions"],"best_for":["Teams building conversational AI products with extended user sessions","Customer support organizations implementing AI-assisted or fully autonomous support agents","Educational platforms requiring context-aware tutoring and personalization","Enterprise applications where conversation history is critical for decision-making"],"limitations":["Conversation quality may degrade if context becomes too large — no automatic summarization or pruning","Token costs scale linearly with conversation length — long conversations become expensive at scale","No built-in user profiling or preference learning — personalization requires external systems","Latency increases with conversation length — early turns may be processed faster than later ones"],"requires":["OpenRouter API key","Conversation history management system (in-memory or database)","Token counting for budget management across conversation lifecycle","Optional: conversation summarization layer for very long sessions (100+ turns)"],"input_types":["text messages","conversation history","system prompts with personality/role definitions"],"output_types":["text responses","structured dialogue acts","reasoning explanations"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-xiaomi-mimo-v2-pro__cap_4","uri":"capability://data.processing.analysis.structured.data.extraction.and.json.generation","name":"structured data extraction and json generation","description":"Extracts structured information from unstructured text and generates valid JSON outputs conforming to specified schemas. The model uses its reasoning capabilities to parse complex documents, identify relevant entities and relationships, and format outputs according to developer-specified schemas, with support for nested structures, arrays, and type validation.","intents":["Extract entities, relationships, and metadata from documents, emails, or web content","Convert unstructured text into structured JSON for database ingestion or API consumption","Generate synthetic structured data for testing, training, or prototyping","Validate and normalize user inputs against predefined schemas"],"best_for":["Data engineering teams building ETL pipelines with LLM-based extraction","Organizations processing large volumes of unstructured documents","Teams building knowledge graphs or structured databases from text sources","Developers implementing form-filling or data validation systems"],"limitations":["JSON generation may produce malformed output for complex nested schemas — requires post-validation","No built-in schema validation — developers must implement JSON schema checking separately","Extraction accuracy varies by domain — performs better on well-formatted documents than messy real-world text","No streaming support for large JSON outputs — entire response must be buffered before parsing"],"requires":["OpenRouter API key","JSON schema definition for output format","Input text in structured or semi-structured format","JSON parsing and validation library (e.g., jsonschema for Python)"],"input_types":["unstructured text","documents","emails","web content","JSON schema specifications"],"output_types":["JSON","structured data","validated records"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-xiaomi-mimo-v2-pro__cap_5","uri":"capability://text.generation.language.knowledge.synthesis.and.summarization.across.large.documents","name":"knowledge synthesis and summarization across large documents","description":"Synthesizes information across large documents or document sets to produce coherent summaries, identify key insights, and answer questions based on comprehensive document understanding. The 1M token window allows the model to process entire books, research papers, or document collections in a single pass, enabling synthesis without intermediate summarization steps that lose nuance.","intents":["Summarize long documents or document sets while preserving critical details and nuance","Answer complex questions that require synthesizing information across multiple sources","Identify contradictions, gaps, or inconsistencies in large document collections","Generate executive summaries or research overviews from comprehensive source material"],"best_for":["Research organizations processing large literature reviews or document collections","Legal teams analyzing contracts, depositions, or case law","Enterprise knowledge management systems requiring document synthesis","Academic institutions supporting research and literature analysis"],"limitations":["Summarization quality depends on document structure — works better with well-formatted documents than scanned PDFs or OCR output","No built-in citation tracking — synthesized information may not preserve source attribution","Token costs scale with document size — processing very large collections becomes expensive","No incremental processing — entire document set must be processed in single inference pass"],"requires":["OpenRouter API key","Documents in text format (plain text, markdown, or structured formats)","Optional: document parsing layer for PDFs or other formats","Optional: citation tracking system for source attribution"],"input_types":["text documents","document collections","natural language queries","summarization instructions"],"output_types":["summaries","synthesized insights","answers to complex questions","structured analysis"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-xiaomi-mimo-v2-pro__cap_6","uri":"capability://planning.reasoning.reasoning.based.problem.solving.with.step.by.step.explanation","name":"reasoning-based problem solving with step-by-step explanation","description":"Decomposes complex problems into reasoning steps, providing transparent explanations for conclusions and recommendations. The model uses chain-of-thought patterns to work through multi-step logic, mathematical reasoning, and decision-making processes, outputting both final answers and the reasoning path used to arrive at them.","intents":["Solve complex math, logic, or algorithmic problems with step-by-step reasoning","Generate explanations for technical decisions or architectural recommendations","Debug complex systems by reasoning through failure modes and root causes","Provide transparent decision-making for high-stakes applications (medical, legal, financial)"],"best_for":["Educational platforms requiring explainable problem-solving","Technical teams needing transparent reasoning for architectural decisions","Organizations in regulated industries requiring explainable AI decisions","Developers building debugging or diagnostic systems"],"limitations":["Reasoning quality varies by problem domain — stronger for logic/math than subjective judgment","Step-by-step reasoning increases token consumption — can be 2-3x more expensive than direct answers","No formal verification of reasoning steps — logical errors may not be caught","Reasoning paths may be verbose or include unnecessary steps, reducing efficiency"],"requires":["OpenRouter API key","Problem statement in natural language or structured format","Optional: domain-specific context or constraints","Optional: reasoning format specification (e.g., XML tags for step delineation)"],"input_types":["natural language problems","mathematical expressions","code or system descriptions","decision scenarios"],"output_types":["reasoning steps","final answers","explanations","recommendations with justification"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-xiaomi-mimo-v2-pro__cap_7","uri":"capability://text.generation.language.adaptive.response.generation.with.context.aware.tone.and.style","name":"adaptive response generation with context-aware tone and style","description":"Generates responses that adapt to context, user preferences, and communication style, maintaining consistency in tone, formality, and approach across interactions. The model uses contextual understanding to match communication style to audience (technical vs non-technical, formal vs casual) and adjusts complexity and depth based on inferred user expertise.","intents":["Generate customer-facing communications that match brand voice and tone","Adapt technical explanations to audience expertise level (expert vs novice)","Maintain consistent personality across different interaction channels","Generate responses that respect cultural or contextual communication norms"],"best_for":["Customer-facing AI systems requiring brand consistency","Educational platforms adapting explanations to student level","Multilingual or multicultural organizations requiring context-aware communication","Enterprise systems where tone and formality matter for user experience"],"limitations":["Tone adaptation is heuristic-based — may not perfectly match brand guidelines without explicit instruction","No built-in user profiling — requires external systems to track user preferences and expertise","Style consistency depends on conversation history — may drift if context becomes too large","Cultural adaptation is limited — may not handle nuanced cultural communication norms"],"requires":["OpenRouter API key","System prompt or context specifying desired tone and style","Optional: user profile or preference data","Optional: brand guidelines or communication standards"],"input_types":["natural language prompts","user context or profile data","style or tone specifications","conversation history"],"output_types":["adapted text responses","styled communications","tone-matched explanations"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-xiaomi-mimo-v2-pro__cap_8","uri":"capability://code.generation.editing.multi.modal.reasoning.with.text.and.code.integration","name":"multi-modal reasoning with text and code integration","description":"Integrates reasoning across text and code domains, enabling the model to explain code in natural language, generate code from descriptions, and reason about code behavior and correctness. The model understands both programming semantics and natural language explanations, enabling bidirectional translation between code and prose.","intents":["Generate natural language explanations for complex code or algorithms","Translate natural language specifications into executable code","Reason about code correctness, performance, or security implications","Generate documentation or comments that accurately reflect code behavior"],"best_for":["Development teams requiring code documentation and explanation generation","Technical writing teams translating code into prose documentation","Code review systems requiring automated explanation of changes","Educational platforms teaching programming through code explanation"],"limitations":["Code-to-prose translation may miss subtle implementation details or edge cases","Prose-to-code translation requires precise specifications — ambiguous descriptions may produce incorrect code","No execution environment — generated code must be tested separately","Performance analysis is heuristic-based — may not identify actual bottlenecks without profiling data"],"requires":["OpenRouter API key","Code in text format (source files or snippets)","Optional: natural language specifications or requirements","Optional: code execution environment for validation"],"input_types":["code snippets or files","natural language descriptions","algorithm specifications","documentation requirements"],"output_types":["code","natural language explanations","documentation","correctness analysis"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["OpenRouter API key with Xiaomi model access","HTTP/2 client supporting streaming responses","GPU infrastructure with minimum 40GB VRAM for local deployment (if self-hosted)","Token counting library compatible with Xiaomi tokenizer for accurate context budgeting","OpenRouter API key with function-calling support enabled","Agent framework compatible with JSON-formatted tool calls (e.g., LangChain, LlamaIndex, custom implementations)","Tool registry or API gateway to handle function execution and result formatting","Error handling layer to manage failed tool calls and retry logic","OpenRouter API key","Code context in text format (files, snippets, or AST representations)"],"failure_modes":["1M token window increases latency proportionally — inference time scales with context length, typically 2-5x slower than 4K context models","Memory requirements scale linearly with context size — requires GPU with 40GB+ VRAM for full context utilization","Attention computation becomes bottleneck at maximum context — practical throughput degrades significantly above 500K tokens","No built-in context compression or summarization — developers must manage context manually to avoid token waste","Function calling output format may require post-processing to handle edge cases or malformed JSON","No built-in retry logic for failed tool calls — agents must implement their own error handling and fallback strategies","Tool registry must be managed externally — the model doesn't persist or learn new tools across sessions","Latency per tool-use cycle includes model inference + tool execution time — can exceed 5-10 seconds for complex chains","Code generation quality varies by language — likely stronger for popular languages (Python, JavaScript, Java) than niche languages","No built-in execution environment — generated code must be tested and validated separately","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.43,"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":"2026-05-24T12:16:25.059Z","last_scraped_at":"2026-05-03T15:20:45.775Z","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=xiaomi-mimo-v2-pro","compare_url":"https://unfragile.ai/compare?artifact=xiaomi-mimo-v2-pro"}},"signature":"WsonkmsCu3T/VnvakHjkgWyKtuyqYaVmlg1N7WFwyPraxcAS2j3ifoNNzSjW+cKgdu42dNylu25ukubPTvoRDQ==","signedAt":"2026-06-22T10:48:03.470Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/xiaomi-mimo-v2-pro","artifact":"https://unfragile.ai/xiaomi-mimo-v2-pro","verify":"https://unfragile.ai/api/v1/verify?slug=xiaomi-mimo-v2-pro","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"}}