{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-xiaomi-mimo-v2-omni","slug":"xiaomi-mimo-v2-omni","name":"Xiaomi: MiMo-V2-Omni","type":"model","url":"https://openrouter.ai/models/xiaomi~mimo-v2-omni","page_url":"https://unfragile.ai/xiaomi-mimo-v2-omni","categories":["image-generation"],"tags":["xiaomi","api-access","text","image","audio","video"],"pricing":{"model":"paid","free":false,"starting_price":"$4.00e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-xiaomi-mimo-v2-omni__cap_0","uri":"capability://image.visual.unified.multimodal.input.processing.image.video.audio.text","name":"unified multimodal input processing (image, video, audio, text)","description":"Processes image, video, and audio inputs within a single native architecture rather than separate modality-specific encoders. The model uses a unified token embedding space that allows cross-modal reasoning and grounding without requiring separate preprocessing pipelines or modality-specific adapters. This architectural choice enables the model to maintain semantic relationships across modalities during inference.","intents":["I need to analyze a video with audio and extract visual events correlated with speech","I want to process mixed-media documents containing images, text, and embedded audio in a single forward pass","I need to ground visual objects in video frames using audio context from the same source"],"best_for":["teams building multimodal AI agents that need simultaneous video+audio+image understanding","developers creating accessibility tools that correlate visual and audio information","researchers prototyping cross-modal reasoning systems without modality-specific engineering"],"limitations":["Unified architecture may have lower peak performance on single-modality tasks compared to modality-optimized models","Inference latency scales with total input size across all modalities; no documented per-modality cost breakdown","Maximum input dimensions for video, audio, and image not publicly specified — may require experimentation"],"requires":["API access via OpenRouter or direct Xiaomi endpoint","Support for multipart/form-data or base64 encoding for media inputs","Sufficient context window to accommodate all modalities (exact size unknown)"],"input_types":["image (JPEG, PNG, WebP, likely others)","video (MP4, WebM, or other formats — not specified)","audio (WAV, MP3, or other formats — not specified)","text"],"output_types":["text (natural language descriptions, reasoning)","structured annotations (likely bounding boxes, timestamps, labels)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-xiaomi-mimo-v2-omni__cap_1","uri":"capability://image.visual.visual.grounding.with.spatial.temporal.localization","name":"visual grounding with spatial-temporal localization","description":"Grounds visual objects and events in images and video frames by producing spatial coordinates (bounding boxes, segmentation masks) and temporal indices. The model likely uses attention mechanisms over spatial feature maps and temporal sequences to localize entities referenced in text or audio queries. This enables precise object identification beyond semantic description.","intents":["I need to identify where in a video frame a specific object appears when described in natural language","I want to extract bounding boxes for all instances of a category from a video sequence","I need to correlate audio events (speech, sounds) with their visual locations in video"],"best_for":["developers building video annotation and labeling tools","teams creating autonomous systems that need precise object localization from multimodal input","researchers working on video-language grounding and visual question answering"],"limitations":["Grounding accuracy likely degrades with occlusion, motion blur, or extreme camera angles — no robustness metrics published","Temporal localization may have frame-level granularity rather than sub-frame precision","No documented support for instance segmentation vs bounding box vs point localization trade-offs"],"requires":["Video or image input with sufficient resolution (minimum resolution unknown)","Text or audio query describing the object or event to ground","API endpoint supporting structured output format for coordinates"],"input_types":["image","video","text query","audio query"],"output_types":["bounding boxes (x, y, width, height format likely)","temporal indices (frame numbers or timestamps)","segmentation masks (if supported)","confidence scores"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-xiaomi-mimo-v2-omni__cap_2","uri":"capability://planning.reasoning.multi.step.agentic.reasoning.with.tool.integration","name":"multi-step agentic reasoning with tool integration","description":"Executes multi-step reasoning chains where the model decomposes complex queries into subtasks, calls external tools or functions, and integrates results back into the reasoning loop. The architecture likely supports function-calling schemas (similar to OpenAI's function calling) with native bindings for common APIs. This enables the model to act as an autonomous agent that can refine understanding across multiple inference steps.","intents":["I need the model to analyze a video, extract metadata, then query an external database for related information","I want to build an agent that watches a video, identifies objects, and retrieves real-time data about those objects","I need multi-step reasoning where the model decides what additional information to fetch before answering a question"],"best_for":["teams building autonomous video analysis agents","developers creating AI systems that combine vision with external data sources","builders prototyping complex workflows that require tool orchestration"],"limitations":["Tool integration mechanism not documented — unclear if it uses standard function-calling schemas or proprietary format","No published latency metrics for multi-step chains — each tool call adds round-trip overhead","Maximum number of reasoning steps or tool calls per request unknown","State management across steps requires external persistence — no built-in session memory"],"requires":["Tool/function definitions in a supported schema format (likely JSON Schema)","API keys or credentials for external tools being called","Timeout configuration for multi-step execution","Error handling for failed tool calls"],"input_types":["image","video","audio","text query","tool definitions (JSON Schema)"],"output_types":["text (reasoning trace and final answer)","structured tool calls (function name, arguments)","tool results (integrated into reasoning)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-xiaomi-mimo-v2-omni__cap_3","uri":"capability://image.visual.video.understanding.with.temporal.event.detection","name":"video understanding with temporal event detection","description":"Analyzes video sequences to detect, classify, and describe events occurring over time. The model processes video as a sequence of frames (or using video-specific encoders) and identifies temporal boundaries of events, their categories, and relationships. This likely uses temporal attention or recurrent mechanisms to maintain context across frames and identify state changes that constitute events.","intents":["I need to detect when specific actions occur in a video and get timestamps for each occurrence","I want to classify video segments by activity type (e.g., 'person walking', 'object being manipulated')","I need to understand the temporal sequence of events in a video and their causal relationships"],"best_for":["developers building video surveillance and monitoring systems","teams creating video summarization and highlight extraction tools","researchers working on action recognition and temporal reasoning"],"limitations":["Event detection granularity unclear — may miss short-duration events or require minimum event duration","Temporal precision likely frame-based rather than sub-frame (depends on video frame rate)","No documented handling of overlapping events or hierarchical event structures","Performance may degrade with very long videos (maximum duration unknown)"],"requires":["Video input in supported format with known frame rate","Sufficient context window to process entire video or sliding window approach","Event taxonomy or query specifying which events to detect"],"input_types":["video","text query describing events of interest","optional: event taxonomy or ontology"],"output_types":["event labels (classification)","temporal boundaries (start/end timestamps or frame numbers)","confidence scores per event","event descriptions (natural language)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-xiaomi-mimo-v2-omni__cap_4","uri":"capability://image.visual.audio.visual.synchronization.and.correlation","name":"audio-visual synchronization and correlation","description":"Correlates audio and visual information to identify synchronized events and ground audio content in visual context. The model aligns audio events (speech, sounds) with corresponding visual phenomena (speaker location, sound source, visual reactions) using cross-modal attention. This enables understanding of multimodal narratives where audio and visual streams are semantically linked.","intents":["I need to identify which person in a video is speaking based on audio-visual synchronization","I want to locate the source of a sound in a video frame by correlating audio with visual motion","I need to understand how dialogue in a video relates to the visual actions occurring simultaneously"],"best_for":["teams building video understanding systems that require speaker identification","developers creating audio-visual synchronization tools for media processing","researchers working on multimodal event understanding and narrative comprehension"],"limitations":["Synchronization accuracy depends on audio-visual alignment in source material — may fail with dubbed or out-of-sync content","No documented handling of multiple simultaneous speakers or overlapping audio","Temporal alignment precision unclear — may have frame-level rather than millisecond-level accuracy","Performance on noisy audio or heavily compressed video unknown"],"requires":["Video with synchronized audio track","Audio and video streams with known temporal alignment","Sufficient model context to process both modalities simultaneously"],"input_types":["video","audio","text query about audio-visual relationships"],"output_types":["spatial locations (bounding boxes for sound sources or speakers)","temporal alignments (synchronization offsets if any)","confidence scores for correlations","natural language descriptions of audio-visual relationships"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-xiaomi-mimo-v2-omni__cap_5","uri":"capability://text.generation.language.speech.recognition.and.transcription.from.video.audio","name":"speech recognition and transcription from video audio","description":"Extracts and transcribes speech from video audio tracks, converting spoken content to text. The model likely uses a speech recognition encoder (possibly shared with the audio processing pipeline) to identify speech segments, recognize phonemes/words, and produce timestamped transcriptions. This integrates with the multimodal architecture to enable text-based querying of video content.","intents":["I need to extract a full transcript of dialogue from a video with timestamps","I want to search for specific spoken phrases within a video and get their locations","I need to identify speakers and attribute dialogue to them based on audio-visual cues"],"best_for":["developers building video search and indexing systems","teams creating video accessibility tools (captions, transcripts)","researchers working on video understanding that requires dialogue analysis"],"limitations":["Transcription accuracy depends on audio quality, accents, and background noise — no published WER (Word Error Rate) metrics","Speaker diarization (attribution to specific speakers) may require additional processing or may be limited to 2-3 speakers","No documented support for multiple languages or code-switching","Timestamp precision likely at word or phrase level rather than character level"],"requires":["Video with clear audio track","Audio quality sufficient for speech recognition (SNR, sample rate unknown)","Optional: language specification or language detection"],"input_types":["video","audio","optional: language code"],"output_types":["text (transcription)","timestamps (word-level or phrase-level)","speaker labels (if diarization enabled)","confidence scores per word or segment"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-xiaomi-mimo-v2-omni__cap_6","uri":"capability://image.visual.image.description.and.visual.question.answering","name":"image description and visual question answering","description":"Generates natural language descriptions of image content and answers questions about images by analyzing visual features, objects, relationships, and context. The model uses vision encoders to extract visual representations and language decoders to produce coherent text. This capability extends to complex reasoning about image content, including counterfactual questions and abstract concepts.","intents":["I need to generate alt-text or captions for images in a batch","I want to ask questions about image content and get detailed answers","I need to analyze images for specific attributes or relationships between objects"],"best_for":["teams building image accessibility tools","developers creating image search and discovery systems","content creators needing automated image annotation"],"limitations":["Description quality varies with image complexity and clarity — no published metrics","VQA accuracy depends on question type; abstract or reasoning-heavy questions may have lower accuracy","No documented support for very high-resolution images (maximum resolution unknown)","Descriptions may be generic or miss fine-grained details in cluttered scenes"],"requires":["Image input in supported format (JPEG, PNG, WebP, etc.)","Sufficient context window for generated descriptions","Optional: question or query for VQA mode"],"input_types":["image","optional: text query or question"],"output_types":["text (description or answer)","structured data (object lists, attributes)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-xiaomi-mimo-v2-omni__cap_7","uri":"capability://data.processing.analysis.audio.classification.and.sound.event.detection","name":"audio classification and sound event detection","description":"Classifies audio content and detects specific sound events within audio streams. The model processes audio spectrograms or waveforms to identify sound categories (speech, music, environmental sounds, etc.) and locate temporal boundaries of specific events. This likely uses audio-specific encoders with temporal convolutions or attention mechanisms to capture acoustic patterns.","intents":["I need to detect when music, speech, or silence occurs in a video audio track","I want to classify environmental sounds (traffic, rain, applause) in audio","I need to identify specific sound effects or acoustic events in a video"],"best_for":["developers building audio indexing and search systems","teams creating video content analysis tools","researchers working on audio event detection and acoustic scene analysis"],"limitations":["Classification accuracy depends on audio quality and background noise levels","Sound event detection may have limited temporal precision (frame-based rather than sample-based)","No documented support for custom sound classes — likely limited to pre-trained categories","Performance on overlapping or simultaneous sounds unknown"],"requires":["Audio input in supported format with known sample rate","Audio quality sufficient for classification (SNR, frequency range unknown)","Optional: sound event taxonomy or class labels"],"input_types":["audio","optional: text query describing sounds to detect"],"output_types":["sound class labels","temporal boundaries (timestamps or frame numbers)","confidence scores","acoustic features (if exposed)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-xiaomi-mimo-v2-omni__cap_8","uri":"capability://search.retrieval.cross.modal.semantic.search.and.retrieval","name":"cross-modal semantic search and retrieval","description":"Enables searching across multimodal content (images, videos, audio) using queries in any modality (text, image, audio). The model encodes queries and documents into a shared semantic space and retrieves relevant content based on cross-modal similarity. This likely uses contrastive learning objectives to align embeddings across modalities.","intents":["I need to find video clips matching a text description of an action or scene","I want to search for images similar to a reference image across a large collection","I need to find audio segments matching a sound description or reference audio"],"best_for":["teams building multimodal content management and discovery systems","developers creating video or image search engines","researchers working on cross-modal retrieval and similarity learning"],"limitations":["Retrieval quality depends on semantic alignment between query and document modalities — may miss results with different visual styles or presentation","No documented support for filtering by metadata or structured attributes","Ranking may not account for relevance beyond semantic similarity (e.g., popularity, recency)","Requires pre-computed embeddings for large-scale retrieval — no real-time indexing documented"],"requires":["Query in any supported modality (text, image, audio, video)","Document collection with pre-computed embeddings or on-demand embedding generation","Vector similarity search infrastructure (e.g., vector database)"],"input_types":["text query","image query","audio query","video query"],"output_types":["ranked list of matching documents","similarity scores","document metadata"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-xiaomi-mimo-v2-omni__cap_9","uri":"capability://data.processing.analysis.structured.data.extraction.from.multimodal.content","name":"structured data extraction from multimodal content","description":"Extracts structured information (entities, relationships, attributes, metadata) from images, videos, and audio. The model identifies and classifies objects, people, text, and events, then outputs structured formats (JSON, tables, knowledge graphs). This likely uses named entity recognition, relation extraction, and semantic parsing techniques adapted for multimodal inputs.","intents":["I need to extract all product names, prices, and descriptions from product videos","I want to identify all people in a video and extract their attributes (clothing, actions, relationships)","I need to extract structured metadata (speakers, topics, timestamps) from meeting recordings"],"best_for":["teams building data extraction pipelines from video or image sources","developers creating knowledge base population systems from multimodal content","researchers working on information extraction and semantic understanding"],"limitations":["Extraction accuracy depends on content clarity and schema complexity — no published F1 scores","Schema definition required upfront — no automatic schema discovery","Handling of ambiguous or incomplete information not documented","No documented support for custom entity types or domain-specific extraction"],"requires":["Multimodal input (image, video, audio, or combination)","Output schema definition (JSON Schema or similar)","Optional: domain-specific ontology or entity taxonomy"],"input_types":["image","video","audio","text","schema definition"],"output_types":["JSON (structured data)","CSV/tables","knowledge graphs","entity lists with attributes"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["API access via OpenRouter or direct Xiaomi endpoint","Support for multipart/form-data or base64 encoding for media inputs","Sufficient context window to accommodate all modalities (exact size unknown)","Video or image input with sufficient resolution (minimum resolution unknown)","Text or audio query describing the object or event to ground","API endpoint supporting structured output format for coordinates","Tool/function definitions in a supported schema format (likely JSON Schema)","API keys or credentials for external tools being called","Timeout configuration for multi-step execution","Error handling for failed tool calls"],"failure_modes":["Unified architecture may have lower peak performance on single-modality tasks compared to modality-optimized models","Inference latency scales with total input size across all modalities; no documented per-modality cost breakdown","Maximum input dimensions for video, audio, and image not publicly specified — may require experimentation","Grounding accuracy likely degrades with occlusion, motion blur, or extreme camera angles — no robustness metrics published","Temporal localization may have frame-level granularity rather than sub-frame precision","No documented support for instance segmentation vs bounding box vs point localization trade-offs","Tool integration mechanism not documented — unclear if it uses standard function-calling schemas or proprietary format","No published latency metrics for multi-step chains — each tool call adds round-trip overhead","Maximum number of reasoning steps or tool calls per request unknown","State management across steps requires external persistence — no built-in session memory","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.45,"ecosystem":0.33,"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-omni","compare_url":"https://unfragile.ai/compare?artifact=xiaomi-mimo-v2-omni"}},"signature":"tToTrXnD+kz5FplvIIp+11x+AYunksJBGyJEySFeOy2EIGkxoFcO3T/W4c4n7omRmyotjVQ4S2Qym8f77Xj3Bg==","signedAt":"2026-06-20T09:38:56.411Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/xiaomi-mimo-v2-omni","artifact":"https://unfragile.ai/xiaomi-mimo-v2-omni","verify":"https://unfragile.ai/api/v1/verify?slug=xiaomi-mimo-v2-omni","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"}}