{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-minimax-minimax-m2-her","slug":"minimax-minimax-m2-her","name":"MiniMax: MiniMax M2-her","type":"model","url":"https://openrouter.ai/models/minimax~minimax-m2-her","page_url":"https://unfragile.ai/minimax-minimax-m2-her","categories":["chatbots-assistants"],"tags":["minimax","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$3.00e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-minimax-minimax-m2-her__cap_0","uri":"capability://text.generation.language.dialogue.first.multi.turn.conversation.with.character.consistency","name":"dialogue-first multi-turn conversation with character consistency","description":"MiniMax M2-her maintains coherent character personality and tone across extended multi-turn conversations through dialogue-optimized transformer architecture that tracks conversational context and character state. The model uses specialized attention mechanisms trained on roleplay and character-driven datasets to preserve personality traits, speech patterns, and emotional consistency across dozens of turns without degradation. Integration via OpenRouter API enables stateless conversation management where the client maintains turn history and passes full context to each inference call.","intents":["Build a chatbot that maintains consistent character personality across 50+ turn conversations","Create immersive roleplay experiences where NPCs stay in character with consistent voice","Develop dialogue systems where character traits and backstory remain coherent throughout interaction","Generate expressive multi-turn conversations that feel natural and emotionally consistent"],"best_for":["Game developers building NPC dialogue systems with personality persistence","Interactive fiction/narrative game creators needing character-driven storytelling","Chatbot builders focused on roleplay and character-based engagement","Teams building immersive conversational experiences with personality-driven interactions"],"limitations":["No built-in memory persistence — character state must be managed by client application","Context window limitations mean very long conversation histories may require summarization or pruning","Character consistency degrades if contradictory instructions are provided in system prompts","No fine-tuning API available — character behavior is fixed to base model training"],"requires":["OpenRouter API key with MiniMax M2-her model access","HTTP client capable of making REST API calls","Client-side conversation history management and context assembly","Understanding of prompt engineering for character definition and tone control"],"input_types":["text (user messages)","text (system prompts defining character)","text (conversation history as message array)"],"output_types":["text (character response)","structured metadata (token usage, finish reason)"],"categories":["text-generation-language","dialogue-systems"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-minimax-minimax-m2-her__cap_1","uri":"capability://text.generation.language.expressive.tone.and.emotional.modulation.in.generated.text","name":"expressive tone and emotional modulation in generated text","description":"M2-her implements tone-aware text generation through embeddings that encode emotional state and expressiveness, allowing fine-grained control over response personality (sarcastic, warm, formal, playful, etc.). The model was trained on diverse conversational datasets with emotional annotations, enabling it to modulate language register, vocabulary selection, and phrasing to match specified emotional contexts. Developers control tone through system prompts or structured metadata passed in API requests.","intents":["Generate responses with specific emotional tones (sarcastic, empathetic, formal, playful)","Create dialogue that adapts emotional expression based on character state or scene context","Build chatbots that express personality through language register and word choice variation","Produce expressive writing that feels natural and emotionally appropriate to context"],"best_for":["Game writers creating emotionally nuanced NPC dialogue","Interactive narrative designers building branching stories with emotional depth","Chatbot creators focused on personality-driven user engagement","Content creators generating expressive character-driven text"],"limitations":["Tone consistency depends on clear system prompt definition — ambiguous emotional instructions produce inconsistent results","Extreme or conflicting emotional states in prompts may cause tone instability","No quantitative tone measurement — evaluation is subjective and requires human review","Tone modulation adds minimal latency but requires careful prompt engineering for reliability"],"requires":["OpenRouter API key with MiniMax M2-her access","Clear system prompt or metadata defining desired emotional tone","Understanding of how to describe emotional states in natural language","Client application to assemble and send API requests"],"input_types":["text (user message)","text (system prompt with tone/emotion specification)"],"output_types":["text (emotionally modulated response)"],"categories":["text-generation-language","style-transfer"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-minimax-minimax-m2-her__cap_2","uri":"capability://text.generation.language.immersive.roleplay.scenario.generation.and.continuation","name":"immersive roleplay scenario generation and continuation","description":"M2-her generates and continues immersive roleplay scenarios by understanding scene context, character relationships, and narrative momentum. The model uses dialogue-optimized decoding that prioritizes narrative coherence and character-appropriate actions/dialogue over generic responses. Integration via OpenRouter API allows developers to pass scene descriptions, character rosters, and interaction history, with the model generating contextually appropriate roleplay continuations that maintain narrative tension and character authenticity.","intents":["Generate roleplay scenario continuations that feel narratively coherent and character-authentic","Create immersive interactive fiction where player choices drive NPC dialogue and actions","Build tabletop RPG assistant tools that generate contextually appropriate NPC responses","Develop collaborative storytelling tools where the model acts as a narrative co-creator"],"best_for":["Tabletop RPG game masters using AI for NPC dialogue generation","Interactive fiction developers building choice-driven narratives","Game developers creating immersive dialogue-heavy experiences","Collaborative storytelling platform creators"],"limitations":["Scenario coherence degrades if scene context is incomplete or contradictory","No built-in world state management — developers must track and pass all relevant context","Long scenario histories may exceed context window, requiring summarization","Character relationships and faction dynamics must be explicitly defined in prompts"],"requires":["OpenRouter API key with MiniMax M2-her model access","Structured scene context (location, characters present, recent events)","Character definitions including personality, goals, and relationships","Client application to manage scenario state and assemble API requests"],"input_types":["text (scene description)","text (character roster with descriptions)","text (interaction history/dialogue log)","text (player action or prompt)"],"output_types":["text (NPC response or narrative continuation)"],"categories":["text-generation-language","narrative-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-minimax-minimax-m2-her__cap_3","uri":"capability://tool.use.integration.api.based.inference.with.stateless.conversation.management","name":"api-based inference with stateless conversation management","description":"M2-her is accessed exclusively through OpenRouter's REST API, which implements stateless inference where clients maintain full conversation history and pass it with each request. The API accepts message arrays in OpenAI-compatible format, returns streaming or non-streaming responses, and provides token usage metrics. This architecture requires client-side responsibility for context assembly, turn management, and conversation persistence, but enables flexible deployment across web, mobile, and backend applications without server-side session state.","intents":["Integrate MiniMax M2-her into web applications without managing server-side conversation state","Build mobile apps that call M2-her API for character dialogue generation","Create backend services that orchestrate M2-her calls alongside other APIs","Implement conversation systems where context is managed by the client application"],"best_for":["Web developers building chat interfaces with stateless API backends","Mobile app developers integrating LLM dialogue without server infrastructure","Backend engineers building microservices that call M2-her for dialogue generation","Teams using OpenRouter as a unified LLM API gateway"],"limitations":["Stateless design means clients must assemble and send full conversation history with each request, increasing payload size and latency for long conversations","No server-side session persistence — conversation history loss if client doesn't maintain it","Token costs scale with conversation length since full history is sent with each request","Rate limiting and quota management are handled by OpenRouter, not by the model itself"],"requires":["OpenRouter API key (paid account)","HTTP client library (curl, fetch, axios, requests, etc.)","Understanding of OpenAI-compatible message format","Client-side conversation history storage and management","Network connectivity to OpenRouter API endpoints"],"input_types":["JSON (message array in OpenAI format)","JSON (model parameters: temperature, max_tokens, etc.)"],"output_types":["JSON (completion object with text, token usage, finish reason)","text/event-stream (for streaming responses)"],"categories":["tool-use-integration","api-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-minimax-minimax-m2-her__cap_4","uri":"capability://text.generation.language.streaming.response.generation.for.real.time.dialogue","name":"streaming response generation for real-time dialogue","description":"M2-her supports streaming responses via Server-Sent Events (SSE) through OpenRouter API, enabling real-time token-by-token delivery of generated dialogue. Clients open a persistent connection and receive response tokens as they're generated, allowing UI updates and perceived responsiveness improvements. The streaming implementation maintains character consistency and tone across token boundaries, with proper handling of special tokens and response completion signals.","intents":["Display character dialogue in real-time as tokens are generated, improving perceived responsiveness","Build interactive chat UIs that update incrementally rather than waiting for full response","Create immersive dialogue experiences where text appears character-by-character","Reduce perceived latency in conversational interfaces by showing partial responses"],"best_for":["Web developers building real-time chat interfaces","Game developers creating immersive dialogue UIs with token-by-token text reveal","Mobile app developers needing responsive dialogue generation","Teams building conversational experiences where perceived latency matters"],"limitations":["Streaming adds complexity to error handling — partial responses may be displayed if connection drops","Token-by-token delivery can expose model generation artifacts or incomplete thoughts mid-stream","Streaming responses cannot be easily edited or regenerated mid-stream","Client must handle SSE connection management, reconnection logic, and timeout handling"],"requires":["OpenRouter API key with streaming support enabled","HTTP client with SSE support (fetch API, axios with adapters, etc.)","Client-side event handling for stream parsing and UI updates","Understanding of Server-Sent Events protocol and text/event-stream MIME type"],"input_types":["JSON (message array with stream: true parameter)"],"output_types":["text/event-stream (SSE format with delta tokens)"],"categories":["text-generation-language","streaming-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-minimax-minimax-m2-her__cap_5","uri":"capability://text.generation.language.system.prompt.based.character.definition.and.behavior.control","name":"system prompt-based character definition and behavior control","description":"M2-her accepts system prompts that define character personality, background, speech patterns, emotional state, and behavioral constraints. The model uses these prompts as conditioning signals during generation, with the dialogue-optimized architecture ensuring system prompt instructions are respected throughout multi-turn conversations. Developers can specify detailed character profiles, relationship dynamics, and interaction rules through natural language system prompts, which the model interprets and applies consistently across turns.","intents":["Define detailed character personalities and ensure the model adheres to character specifications","Create NPC behavior rules and constraints that persist across conversation turns","Specify character backstory, relationships, and emotional state to influence dialogue generation","Control character speech patterns, vocabulary, and communication style through prompts"],"best_for":["Game developers defining NPC personalities and behavior rules","Interactive fiction creators specifying character traits and constraints","Chatbot builders creating consistent character personas","Narrative designers controlling character behavior through prompts"],"limitations":["System prompt adherence depends on clarity and specificity — vague character definitions produce inconsistent results","Contradictory system prompt instructions can cause behavior instability","Very long system prompts consume context window, reducing space for conversation history","No validation that generated responses actually follow system prompt constraints — requires manual review"],"requires":["OpenRouter API key with MiniMax M2-her access","Skill in writing detailed, unambiguous character descriptions","Understanding of how to specify behavioral constraints in natural language","Client application to assemble and send API requests with system prompts"],"input_types":["text (system prompt with character definition)"],"output_types":["text (character response adhering to system prompt specifications)"],"categories":["text-generation-language","prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-minimax-minimax-m2-her__cap_6","uri":"capability://memory.knowledge.message.history.context.assembly.and.turn.management","name":"message history context assembly and turn management","description":"M2-her requires clients to assemble full conversation history as a message array (following OpenAI format) and pass it with each API request. The model processes the entire history to generate contextually appropriate responses, with the dialogue-optimized architecture understanding turn-taking patterns, speaker roles, and conversational flow. Clients are responsible for maintaining message history, managing turn order, and ensuring proper speaker attribution (user vs assistant roles).","intents":["Maintain conversation context across multiple turns without server-side session state","Assemble conversation history in a format the model can process for context-aware responses","Manage speaker roles and turn order to ensure the model understands who is speaking","Build conversation systems where context is explicitly managed by the client"],"best_for":["Web developers building stateless chat applications","Backend engineers implementing conversation APIs","Mobile app developers managing conversation state on the client","Teams using OpenRouter for multi-model conversation management"],"limitations":["Clients must implement their own conversation history persistence (database, local storage, etc.)","Full history is sent with each request, increasing payload size and latency for long conversations","Token costs scale linearly with conversation length since full history is always included","No built-in conversation pruning or summarization — clients must implement if needed"],"requires":["OpenRouter API key","Client-side conversation history storage","Understanding of OpenAI message format (role, content fields)","HTTP client for API requests","Logic to assemble message arrays and manage turn order"],"input_types":["JSON (message array with role and content fields)"],"output_types":["JSON (completion object with generated message)"],"categories":["memory-knowledge","context-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-minimax-minimax-m2-her__cap_7","uri":"capability://text.generation.language.multi.language.dialogue.generation.with.cultural.context.awareness","name":"multi-language dialogue generation with cultural context awareness","description":"unknown — insufficient data. The artifact description mentions support for rich messages but does not specify language support, multilingual capabilities, or cultural context handling. Without documentation on supported languages, character encoding, or cultural adaptation mechanisms, specific architectural details cannot be determined.","intents":["Generate dialogue in languages other than English","Create culturally appropriate character responses","Build multilingual conversational experiences"],"best_for":["International game developers","Multilingual chatbot builders"],"limitations":["Language support unknown — requires documentation review","Cultural context handling approach unknown","Character consistency across languages unknown"],"requires":["OpenRouter API key","Documentation on supported languages"],"input_types":["text (language unspecified)"],"output_types":["text (language unspecified)"],"categories":["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 MiniMax M2-her model access","HTTP client capable of making REST API calls","Client-side conversation history management and context assembly","Understanding of prompt engineering for character definition and tone control","OpenRouter API key with MiniMax M2-her access","Clear system prompt or metadata defining desired emotional tone","Understanding of how to describe emotional states in natural language","Client application to assemble and send API requests","Structured scene context (location, characters present, recent events)","Character definitions including personality, goals, and relationships"],"failure_modes":["No built-in memory persistence — character state must be managed by client application","Context window limitations mean very long conversation histories may require summarization or pruning","Character consistency degrades if contradictory instructions are provided in system prompts","No fine-tuning API available — character behavior is fixed to base model training","Tone consistency depends on clear system prompt definition — ambiguous emotional instructions produce inconsistent results","Extreme or conflicting emotional states in prompts may cause tone instability","No quantitative tone measurement — evaluation is subjective and requires human review","Tone modulation adds minimal latency but requires careful prompt engineering for reliability","Scenario coherence degrades if scene context is incomplete or contradictory","No built-in world state management — developers must track and pass all relevant context","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":"2026-05-24T12:16:24.484Z","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=minimax-minimax-m2-her","compare_url":"https://unfragile.ai/compare?artifact=minimax-minimax-m2-her"}},"signature":"HtdcOqqmvRn9Kdh7IEHkg66QkV5SIPLWPkBYvCoSbLpUMxbSR0JzKccxxKo9SMWCo5ImRyzSrZQog15yk6iEBQ==","signedAt":"2026-06-21T03:13:22.014Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/minimax-minimax-m2-her","artifact":"https://unfragile.ai/minimax-minimax-m2-her","verify":"https://unfragile.ai/api/v1/verify?slug=minimax-minimax-m2-her","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"}}