{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-minimax","slug":"minimax","name":"MiniMax","type":"model","url":"https://www.minimax.io/","page_url":"https://unfragile.ai/minimax","categories":["image-generation"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-minimax__cap_0","uri":"capability://text.generation.language.multimodal.text.to.speech.synthesis.with.emotional.prosody.control","name":"multimodal text-to-speech synthesis with emotional prosody control","description":"Generates natural speech from text input using foundation models trained on diverse linguistic and acoustic data, with fine-grained control over prosody, emotion, and speaker characteristics. The system processes text through semantic understanding layers to map linguistic intent to acoustic parameters, enabling expressive speech generation beyond simple phoneme-to-audio mapping. Supports multiple languages and speaker profiles through learned embeddings.","intents":["Generate natural-sounding voiceovers for video content with specific emotional tone","Create accessible audio versions of written content with customizable voice characteristics","Build conversational AI agents with expressive, non-monotone speech output","Produce multilingual audio content without hiring voice talent"],"best_for":["Content creators building video production pipelines","Accessibility teams converting text content to audio","AI agent developers requiring expressive speech synthesis","Localization teams handling multilingual content"],"limitations":["Real-time synthesis latency unknown — likely 500ms-2s per utterance depending on length","Limited control over fine phonetic details compared to traditional TTS with phoneme-level editing","Speaker voice cloning may require minimum audio sample length (typically 30+ seconds)","Emotional prosody control is model-learned rather than rule-based, reducing predictability for edge cases"],"requires":["API key for MiniMax service","Text input in supported languages (minimum 1-2 characters, typical max 1000-5000 characters per request)","Network connectivity for cloud-based synthesis","Audio output format support (MP3, WAV, or similar)"],"input_types":["text (UTF-8 encoded)","language code (ISO 639-1 or similar)","speaker profile identifier or voice embedding","prosody parameters (emotion, speed, pitch range)"],"output_types":["audio file (MP3, WAV, or streaming audio)","audio metadata (duration, sample rate, bitrate)"],"categories":["text-generation-language","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-minimax__cap_1","uri":"capability://image.visual.text.to.video.generation.with.temporal.coherence.and.scene.composition","name":"text-to-video generation with temporal coherence and scene composition","description":"Generates video sequences from natural language descriptions using diffusion-based or autoregressive foundation models that maintain temporal consistency across frames. The system encodes text prompts into latent representations, then iteratively generates or refines video frames while enforcing motion continuity and scene coherence through temporal attention mechanisms or frame interpolation. Supports variable length outputs and composition of multiple scene descriptions into cohesive sequences.","intents":["Create marketing videos or product demos from text descriptions without filming","Generate storyboard visualizations for film/game pre-production planning","Produce background footage or filler content for video editing projects","Build dynamic visual content for presentations or educational materials"],"best_for":["Content creators and marketers needing rapid video prototyping","Game developers generating concept art and scene previsualization","Educational content creators producing visual explanations","Small production teams without access to filming equipment"],"limitations":["Video generation latency is significant — typically 30-120 seconds for 5-10 second clips depending on resolution","Output resolution likely capped at 720p-1080p; 4K generation would require substantial compute","Temporal coherence degrades with longer sequences (>30 seconds) due to accumulating diffusion errors","Complex multi-object interactions or precise spatial relationships may fail or produce artifacts","No frame-by-frame editing capability — regeneration required for any modifications"],"requires":["API key for MiniMax service","Text prompt (typically 50-500 characters for best results)","Desired video duration (seconds) and resolution (480p, 720p, 1080p)","Network connectivity and patience for generation (30-120 seconds typical)","Storage for output video files (100MB-500MB per generated video)"],"input_types":["text prompt (natural language description)","duration parameter (seconds)","resolution parameter (height/width or preset)","optional: seed for reproducibility","optional: style or aesthetic parameters"],"output_types":["video file (MP4, WebM, or similar)","video metadata (duration, resolution, framerate, codec)","optional: intermediate frames or latent representations"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-minimax__cap_2","uri":"capability://text.generation.language.speech.to.text.transcription.with.speaker.diarization.and.language.detection","name":"speech-to-text transcription with speaker diarization and language detection","description":"Converts audio input to text while simultaneously identifying speaker boundaries and language composition using foundation models trained on multilingual speech data. The system processes audio through acoustic feature extraction, then applies speaker embedding models to cluster speech segments by speaker identity, and language identification models to detect language switches. Outputs include transcribed text, speaker labels, timestamps, and language tags for each segment.","intents":["Transcribe multi-speaker meetings or interviews with automatic speaker identification","Convert multilingual audio content to text with language-aware segmentation","Create searchable transcripts of podcasts or video content with speaker attribution","Extract dialogue from video for subtitle generation with speaker labels"],"best_for":["Meeting transcription and documentation teams","Podcast and media production companies","Multilingual content creators and localization teams","Accessibility teams generating captions and transcripts"],"limitations":["Accuracy degrades with background noise, accents, or technical jargon (typical WER 5-15% in clean audio, 20-40% in noisy conditions)","Speaker diarization requires minimum 10-15 seconds per speaker for reliable clustering","Language detection may fail on code-switching or heavily accented speech","Real-time processing latency unknown — likely 2-5x audio duration for full processing","No speaker identification (matching to known voices) — only clustering of unknown speakers"],"requires":["API key for MiniMax service","Audio file or stream (WAV, MP3, M4A, or similar formats)","Audio sample rate typically 16kHz or higher for optimal accuracy","Network connectivity for cloud-based processing","Maximum audio duration per request (likely 1-2 hours)"],"input_types":["audio file (MP3, WAV, M4A, FLAC, or streaming audio)","optional: language hint (ISO 639-1 code)","optional: speaker count hint for diarization","optional: custom vocabulary or domain-specific terms"],"output_types":["text transcript (plain text or JSON with metadata)","speaker diarization (speaker labels with timestamps)","language tags (per segment or per utterance)","confidence scores (per word or per segment)","optional: SRT/VTT subtitle format"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-minimax__cap_3","uri":"capability://text.generation.language.music.generation.from.text.descriptions.with.style.and.instrumentation.control","name":"music generation from text descriptions with style and instrumentation control","description":"Generates original music compositions from natural language descriptions using foundation models trained on diverse musical styles, genres, and instrumentation. The system encodes text prompts describing mood, tempo, instruments, and structure into latent representations, then generates audio waveforms or MIDI sequences while maintaining musical coherence through learned harmonic and rhythmic patterns. Supports variable duration and style transfer between different musical contexts.","intents":["Create background music for videos, games, or applications without licensing concerns","Generate royalty-free music for content creators with specific mood or style requirements","Produce musical variations or remixes of existing compositions through style transfer","Compose original music for indie game developers or film projects with limited budgets"],"best_for":["Content creators and video producers needing background music","Indie game developers requiring adaptive or procedural music","Film and animation studios exploring music composition tools","Musicians using AI as a creative tool for ideation and prototyping"],"limitations":["Generated music may lack the sophistication and emotional depth of human composition","Longer compositions (>3-5 minutes) may exhibit repetition or structural incoherence","Fine control over specific instruments or arrangements is limited compared to DAW-based composition","Generation latency is significant — likely 30-60 seconds for 1-2 minute compositions","No real-time generation or interactive composition feedback","Copyright and licensing of generated music may be unclear in some jurisdictions"],"requires":["API key for MiniMax service","Text description of desired music (mood, genre, tempo, instrumentation, duration)","Network connectivity for cloud-based generation","Audio playback capability for preview and evaluation","Storage for generated audio files (10-50MB per composition)"],"input_types":["text prompt (natural language description of music style, mood, instruments)","duration parameter (seconds or minutes)","optional: genre or style tags","optional: tempo or BPM specification","optional: instrumentation list","optional: seed for reproducibility"],"output_types":["audio file (MP3, WAV, or similar)","optional: MIDI file for further editing in DAW","audio metadata (duration, sample rate, key, tempo)","optional: stem files (separate instrument tracks)"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-minimax__cap_4","uri":"capability://image.visual.image.generation.from.text.prompts.with.style.and.composition.control","name":"image generation from text prompts with style and composition control","description":"Generates images from natural language descriptions using diffusion-based foundation models that iteratively refine visual content from noise based on text embeddings. The system encodes text prompts into semantic representations, then applies guided diffusion with optional style, composition, and aesthetic parameters to generate high-quality images. Supports variable aspect ratios, resolutions, and style transfer through prompt engineering or explicit style parameters.","intents":["Create marketing graphics, product mockups, or concept art without hiring designers","Generate illustrations or visual content for blog posts, presentations, or educational materials","Produce variations of existing visual concepts with different styles or compositions","Build visual assets for games, apps, or websites with rapid iteration"],"best_for":["Content creators and marketers needing rapid visual asset generation","Designers using AI as a tool for ideation and rapid prototyping","Small teams or solo developers without access to design resources","Educators creating visual explanations and illustrations"],"limitations":["Image quality and coherence depend heavily on prompt quality and specificity","Hands, faces, and complex anatomical details often contain artifacts or errors","Fine control over specific visual elements is limited — regeneration required for modifications","Generation latency is moderate — typically 5-30 seconds per image depending on resolution","Output resolution likely capped at 1024x1024 or 2048x2048; higher resolutions require upscaling","No frame-by-frame consistency for animations — each frame generated independently","Copyright and licensing of generated images may be unclear in some jurisdictions"],"requires":["API key for MiniMax service","Text prompt (typically 20-200 characters for best results)","Desired image dimensions (aspect ratio and resolution)","Network connectivity for cloud-based generation","Storage for output image files (1-10MB per image)"],"input_types":["text prompt (natural language description)","aspect ratio or resolution (width x height)","optional: style parameters (artistic style, aesthetic, mood)","optional: composition parameters (layout, focal point)","optional: seed for reproducibility","optional: negative prompt (elements to exclude)"],"output_types":["image file (PNG, JPEG, or similar)","image metadata (dimensions, color space, generation parameters)","optional: multiple variations or iterations"],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-minimax__cap_5","uri":"capability://image.visual.video.understanding.and.analysis.with.scene.segmentation.and.content.extraction","name":"video understanding and analysis with scene segmentation and content extraction","description":"Analyzes video input to extract semantic information including scene boundaries, object detection, action recognition, and textual content using foundation models trained on diverse video data. The system processes video frames through visual understanding layers, applies temporal modeling to identify scene transitions and action sequences, and extracts structured metadata including timestamps, descriptions, and detected entities. Supports both short-form and long-form video analysis.","intents":["Automatically segment and index video content for searchability and organization","Extract key moments, scenes, or actions from long-form video for summarization","Detect and classify objects, people, or activities in video for content moderation or analytics","Generate automatic captions or descriptions for video accessibility and SEO"],"best_for":["Video content platforms and streaming services requiring indexing and search","Content moderation teams analyzing user-generated video content","Accessibility teams generating captions and descriptions for video","Analytics and research teams extracting insights from video data"],"limitations":["Analysis accuracy varies with video quality, lighting, and scene complexity","Real-time processing latency is significant — likely 2-5x video duration for full analysis","Scene segmentation may miss subtle transitions or ambiguous boundaries","Object detection and action recognition may fail on rare or unusual activities","No support for real-time streaming analysis — requires complete video upload","Maximum video duration per request likely limited (1-2 hours typical)"],"requires":["API key for MiniMax service","Video file or stream (MP4, WebM, MOV, or similar formats)","Video resolution typically 480p or higher for optimal accuracy","Network connectivity for cloud-based processing","Storage for output metadata and analysis results"],"input_types":["video file (MP4, WebM, MOV, or streaming video)","optional: analysis type specification (scene segmentation, object detection, action recognition, etc.)","optional: custom labels or categories for classification","optional: temporal sampling rate (analyze every frame, every N frames, or key frames only)"],"output_types":["structured metadata (JSON with scenes, objects, actions, timestamps)","scene segmentation (start/end times and descriptions)","object and action labels with confidence scores","extracted text or captions","optional: keyframe images or clips"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-minimax__cap_6","uri":"capability://memory.knowledge.multimodal.embedding.generation.for.cross.modal.retrieval.and.similarity.matching","name":"multimodal embedding generation for cross-modal retrieval and similarity matching","description":"Generates unified vector embeddings for text, images, audio, and video that enable cross-modal similarity matching and retrieval using foundation models trained on aligned multimodal data. The system encodes different modalities into a shared embedding space where semantically similar content from different modalities (e.g., text description and image) have nearby representations. Supports batch embedding generation and efficient similarity search through vector indexing.","intents":["Build search systems that find images, videos, or audio matching text queries","Create recommendation systems that suggest related content across different media types","Detect duplicate or similar content across multimodal datasets for deduplication","Enable semantic similarity matching for content moderation or quality assessment"],"best_for":["Content platforms and search engines requiring cross-modal retrieval","Recommendation systems combining multiple content types","Content moderation teams detecting similar or duplicate content","Research teams analyzing multimodal datasets"],"limitations":["Embedding quality depends on foundation model training data — may have biases or gaps for niche domains","Cross-modal alignment is imperfect — text and image embeddings may not be perfectly comparable","Embedding dimensionality is fixed (typically 512-2048 dimensions) — no fine-tuning per domain","Similarity matching is approximate — exact semantic equivalence across modalities is impossible","No support for fine-grained attribute matching — only holistic semantic similarity"],"requires":["API key for MiniMax service","Input content (text, image, audio, or video)","Network connectivity for cloud-based embedding generation","Vector storage or indexing system for similarity search (optional but recommended)"],"input_types":["text (UTF-8 encoded, typically 1-10000 characters)","image (PNG, JPEG, or similar, typically 256x256 or larger)","audio (WAV, MP3, or similar, typically 16kHz or higher)","video (MP4, WebM, or similar, typically 480p or higher)"],"output_types":["embedding vector (float array, typically 512-2048 dimensions)","embedding metadata (modality type, input hash, generation timestamp)","optional: similarity scores for comparison queries"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-minimax__cap_7","uri":"capability://text.generation.language.real.time.speech.to.speech.translation.with.voice.preservation","name":"real-time speech-to-speech translation with voice preservation","description":"Converts speech in one language to speech in another language while preserving speaker voice characteristics and emotional prosody using a pipeline of speech recognition, translation, and speech synthesis foundation models. The system transcribes input speech to text, translates to target language, then synthesizes output speech using speaker embeddings extracted from the original audio to maintain voice identity. Supports low-latency streaming for conversational use cases.","intents":["Enable real-time multilingual conversations with voice preservation for international calls","Create dubbed video content with original speaker voices in different languages","Build accessible translation tools for non-native speakers in real-time communication","Support multilingual customer service with natural voice-based interaction"],"best_for":["International communication platforms and video conferencing tools","Video production and dubbing studios","Accessibility and localization teams","Customer service and support teams handling multilingual interactions"],"limitations":["End-to-end latency is significant — likely 1-3 seconds for real-time streaming due to pipeline overhead","Translation quality depends on language pair and domain — may lose nuance or context","Voice preservation is approximate — synthesized voice may not perfectly match original speaker","Emotional prosody transfer is limited — output speech may sound less expressive than input","Supported language pairs may be limited compared to text-only translation","Real-time streaming requires low-latency network connectivity"],"requires":["API key for MiniMax service","Audio input (microphone stream or audio file)","Source and target language codes (ISO 639-1 or similar)","Network connectivity with low latency for real-time streaming","Audio output capability (speakers or audio file)"],"input_types":["audio stream or file (WAV, MP3, or similar)","source language code","target language code","optional: speaker voice profile or embedding"],"output_types":["audio stream or file (WAV, MP3, or similar)","optional: intermediate transcript and translation for debugging","optional: speaker embedding used for voice preservation"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-minimax__cap_8","uri":"capability://search.retrieval.semantic.search.across.multimodal.content.with.natural.language.queries","name":"semantic search across multimodal content with natural language queries","description":"Enables searching across mixed text, image, audio, and video content using natural language queries by converting queries and content into comparable embeddings in a shared semantic space. The system encodes the natural language query into an embedding, then performs approximate nearest-neighbor search against indexed content embeddings to retrieve semantically relevant results regardless of modality. Supports filtering, ranking, and relevance scoring.","intents":["Search image libraries or photo databases using natural language descriptions","Find relevant video clips or segments matching text-based queries","Discover audio content (music, podcasts, audiobooks) by describing desired content","Build unified search interfaces across heterogeneous content repositories"],"best_for":["Content platforms and digital asset management systems","Media libraries and archives requiring semantic search","E-commerce platforms with mixed product media types","Research and knowledge management systems"],"limitations":["Search quality depends on embedding model quality and training data biases","Semantic search may miss exact keyword matches — requires semantic understanding","Large-scale indexing requires external vector database (not provided by MiniMax)","Ranking and relevance scoring are approximate — no guaranteed ordering","No support for complex boolean queries or field-specific filtering","Latency for large indexes (millions of items) may be significant"],"requires":["API key for MiniMax service","Pre-indexed content embeddings (generated via multimodal embedding capability)","Vector database or search index (e.g., Pinecone, Weaviate, Milvus)","Natural language query input","Network connectivity for search queries"],"input_types":["natural language query (text, typically 5-100 characters)","optional: filter parameters (content type, date range, etc.)","optional: ranking parameters (relevance weight, diversity, etc.)"],"output_types":["ranked list of matching content with similarity scores","content metadata (ID, type, preview, URL)","optional: explanation of relevance or matching terms"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":21,"verified":false,"data_access_risk":"high","permissions":["API key for MiniMax service","Text input in supported languages (minimum 1-2 characters, typical max 1000-5000 characters per request)","Network connectivity for cloud-based synthesis","Audio output format support (MP3, WAV, or similar)","Text prompt (typically 50-500 characters for best results)","Desired video duration (seconds) and resolution (480p, 720p, 1080p)","Network connectivity and patience for generation (30-120 seconds typical)","Storage for output video files (100MB-500MB per generated video)","Audio file or stream (WAV, MP3, M4A, or similar formats)","Audio sample rate typically 16kHz or higher for optimal accuracy"],"failure_modes":["Real-time synthesis latency unknown — likely 500ms-2s per utterance depending on length","Limited control over fine phonetic details compared to traditional TTS with phoneme-level editing","Speaker voice cloning may require minimum audio sample length (typically 30+ seconds)","Emotional prosody control is model-learned rather than rule-based, reducing predictability for edge cases","Video generation latency is significant — typically 30-120 seconds for 5-10 second clips depending on resolution","Output resolution likely capped at 720p-1080p; 4K generation would require substantial compute","Temporal coherence degrades with longer sequences (>30 seconds) due to accumulating diffusion errors","Complex multi-object interactions or precise spatial relationships may fail or produce artifacts","No frame-by-frame editing capability — regeneration required for any modifications","Accuracy degrades with background noise, accents, or technical jargon (typical WER 5-15% in clean audio, 20-40% in noisy conditions)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.28,"ecosystem":0.25,"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":"2026-06-17T09:51:03.578Z","last_scraped_at":"2026-05-03T14:00:20.516Z","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","compare_url":"https://unfragile.ai/compare?artifact=minimax"}},"signature":"x1htb0AGXlTeExs2mpO3ICVAVZ6sy16fIIW/XsNYq6scdO2SIyKsZjyYeaza6APHmdvK/b1+xWebWupwFoEaCA==","signedAt":"2026-06-22T22:27:15.724Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/minimax","artifact":"https://unfragile.ai/minimax","verify":"https://unfragile.ai/api/v1/verify?slug=minimax","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"}}