{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-openai-gpt-4o-audio-preview","slug":"openai-gpt-4o-audio-preview","name":"OpenAI: GPT-4o Audio","type":"model","url":"https://openrouter.ai/models/openai~gpt-4o-audio-preview","page_url":"https://unfragile.ai/openai-gpt-4o-audio-preview","categories":["voice-audio"],"tags":["openai","api-access","text","audio"],"pricing":{"model":"paid","free":false,"starting_price":"$2.50e-6 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-openai-gpt-4o-audio-preview__cap_0","uri":"capability://text.generation.language.audio.input.to.text.understanding","name":"audio-input-to-text-understanding","description":"Processes audio files (speech, music, ambient sound) as direct model inputs without requiring separate speech-to-text preprocessing. The model internally applies audio encoding layers that convert raw waveforms into token embeddings compatible with GPT-4o's transformer architecture, enabling end-to-end understanding of acoustic nuances including tone, emotion, background noise, and speaker characteristics.","intents":["I need to analyze a customer support call recording and extract sentiment, intent, and key issues in a single API call","I want to build a voice-first chatbot that understands context from audio tone and emotion, not just transcribed words","I need to process meeting recordings and generate summaries that account for speaker emphasis and emotional undertones"],"best_for":["voice application developers building conversational AI with emotional intelligence","customer experience teams analyzing call center recordings at scale","accessibility-focused builders creating audio-first interfaces for visually impaired users"],"limitations":["Audio encoding adds ~500-1500ms latency per request compared to text-only inputs","Maximum audio file size and duration limits not explicitly documented — likely 25MB or similar per OpenAI's standard constraints","No streaming audio input support — requires complete audio file upload before processing begins","Audio quality degradation below 8kHz sample rate may reduce nuance detection accuracy"],"requires":["OpenAI API key with gpt-4o-audio-preview model access","Audio files in supported formats (WAV, MP3, M4A, FLAC — exact list unconfirmed)","HTTP/2 capable client library to handle larger audio payloads efficiently"],"input_types":["audio/wav","audio/mpeg","audio/mp4","audio/flac"],"output_types":["text","structured JSON with extracted entities and sentiment scores"],"categories":["text-generation-language","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-audio-preview__cap_1","uri":"capability://text.generation.language.audio.output.generation","name":"audio-output-generation","description":"Generates natural speech audio from text responses using an integrated text-to-speech engine that applies prosody modeling, speaker voice selection, and emotion-aware intonation. The model outputs audio bytes directly rather than requiring a separate TTS service, with support for multiple voice profiles and language-specific phoneme handling.","intents":["I want to create a voice assistant that responds with natural-sounding speech that matches the emotional tone of the conversation","I need to generate multilingual audio responses from a single API call without chaining multiple TTS services","I want to build an audiobook generator that maintains consistent narrator voice and pacing across chapters"],"best_for":["voice application developers building end-to-end audio conversational systems","accessibility engineers creating audio-first interfaces for screen reader alternatives","content creators automating podcast/audiobook production at scale"],"limitations":["Voice selection limited to OpenAI's predefined voice profiles — no custom voice cloning or fine-tuning","Audio output quality capped at 24kHz sample rate, limiting high-fidelity audio applications","No fine-grained control over speech rate, pitch, or emphasis per-word — only global voice parameters","Latency for audio generation scales with response length; 30-second audio may require 2-5 seconds generation time"],"requires":["OpenAI API key with gpt-4o-audio-preview model access","Audio playback capability on client (browser Web Audio API, mobile audio framework, or server-side audio handling)","Support for streaming audio chunks or buffering complete audio response before playback"],"input_types":["text"],"output_types":["audio/wav","audio/mpeg","audio/aac"],"categories":["text-generation-language","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-audio-preview__cap_2","uri":"capability://text.generation.language.multimodal.audio.text.reasoning","name":"multimodal-audio-text-reasoning","description":"Accepts simultaneous audio and text inputs in a single request, fusing both modalities through cross-attention mechanisms to produce reasoning that leverages complementary information from speech and written context. The model can, for example, reconcile contradictions between what is said (audio tone) and what is written (text content), or use text context to disambiguate audio speech recognition edge cases.","intents":["I need to analyze a video transcript alongside the actual video audio to detect sarcasm or irony that text alone misses","I want to build a meeting assistant that uses both the live audio stream and shared documents to generate contextually accurate summaries","I need to process customer feedback where audio recordings and written survey responses must be analyzed together for holistic sentiment"],"best_for":["multimodal AI application developers building context-rich analysis systems","enterprise teams processing video/audio content with accompanying metadata or transcripts","researchers studying human communication patterns across modalities"],"limitations":["No explicit control over modality weighting — model implicitly prioritizes one modality in ambiguous cases (behavior undocumented)","Cross-modal fusion adds ~300-800ms latency beyond single-modality processing","Maximum combined input size (audio + text) not documented; likely enforces total token budget across both modalities","No ability to specify temporal alignment between audio and text (e.g., which text corresponds to which audio segment)"],"requires":["OpenAI API key with gpt-4o-audio-preview model access","Both audio file and text content in the same API request","Client-side logic to manage multimodal input assembly and validation"],"input_types":["audio/wav","audio/mpeg","audio/mp4","audio/flac","text"],"output_types":["text","structured JSON with cross-modal analysis results"],"categories":["text-generation-language","audio-processing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-audio-preview__cap_3","uri":"capability://text.generation.language.real.time.audio.streaming.inference","name":"real-time-audio-streaming-inference","description":"Accepts audio input as a continuous stream of chunks rather than requiring a complete file upload, enabling low-latency voice interaction patterns. The model buffers incoming audio chunks, applies incremental encoding, and can begin generating responses before the full audio input is received, using a sliding-window attention mechanism to maintain context across chunk boundaries.","intents":["I want to build a voice assistant with <500ms end-to-end latency from speech end to response start","I need to process live podcast or radio streams and generate real-time commentary or transcription","I want to create a voice interface where the model can interrupt or respond mid-sentence based on audio patterns"],"best_for":["real-time voice application developers building low-latency conversational AI","live streaming platforms integrating AI commentary or moderation","accessibility developers creating responsive voice interfaces for users with motor impairments"],"limitations":["Streaming inference requires WebSocket or gRPC connection — not available via standard REST API","Model cannot retroactively revise earlier responses if later audio context changes interpretation (streaming is one-pass)","Chunk size and buffering strategy not documented — may require tuning for optimal latency vs. accuracy trade-off","Streaming audio support may be limited to preview/beta status with potential breaking changes in production API"],"requires":["OpenAI API key with gpt-4o-audio-preview model access","WebSocket or gRPC client library supporting streaming protocol","Audio capture capability with configurable chunk size (likely 20-100ms chunks at 16kHz sample rate)","Client-side buffering and jitter handling for network variability"],"input_types":["audio/wav (streamed in chunks)","audio/mpeg (streamed in chunks)"],"output_types":["text (streamed incrementally)","audio/wav (streamed incrementally)"],"categories":["text-generation-language","audio-processing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-audio-preview__cap_4","uri":"capability://text.generation.language.audio.emotion.and.intent.extraction","name":"audio-emotion-and-intent-extraction","description":"Analyzes acoustic features (pitch contour, speaking rate, pause duration, voice quality) embedded within audio to extract structured emotional state and user intent without relying on transcription. The model applies specialized attention heads trained on prosodic patterns to classify emotions (confidence, frustration, confusion, satisfaction) and infer underlying user goals from speech characteristics alone.","intents":["I need to detect customer frustration in support calls before they escalate, based on voice tone alone","I want to measure speaker confidence or uncertainty in recorded presentations or interviews","I need to classify user intent (question, complaint, request, small talk) from voice patterns in a multilingual contact center"],"best_for":["customer experience teams building emotion-aware support systems","HR/recruitment teams analyzing interview recordings for communication style assessment","mental health or wellness platforms detecting emotional distress from voice patterns"],"limitations":["Emotion classification is probabilistic and culture-dependent — accuracy varies significantly across languages and accents","Cannot distinguish between genuine emotion and acted/sarcastic emotion without additional context","Requires audio quality ≥16kHz sample rate; lower quality audio degrades emotion detection accuracy by 15-30%","No per-segment emotion tracking — returns aggregate emotion for entire audio file, not frame-by-frame analysis"],"requires":["OpenAI API key with gpt-4o-audio-preview model access","Audio file with clear speech (background noise <-20dB SNR recommended)","Client-side logic to parse structured emotion/intent JSON response"],"input_types":["audio/wav","audio/mpeg","audio/mp4","audio/flac"],"output_types":["structured JSON with emotion scores (0-1 per emotion class), intent classification, confidence scores"],"categories":["text-generation-language","audio-processing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-audio-preview__cap_5","uri":"capability://text.generation.language.multilingual.audio.processing","name":"multilingual-audio-processing","description":"Processes audio in 50+ languages and language variants without requiring explicit language specification, using language identification layers that detect the spoken language from acoustic features and automatically apply language-specific phoneme models, prosody rules, and vocabulary. Supports code-switching (mixing multiple languages in single utterance) through dynamic language context switching.","intents":["I need to process customer support calls from global teams where callers mix English and Spanish mid-sentence","I want to build a multilingual voice assistant that automatically detects and responds in the caller's language","I need to analyze international conference recordings where speakers switch between multiple languages"],"best_for":["global companies building voice systems for multilingual user bases","international teams processing content in mixed-language environments","accessibility developers creating voice interfaces for multilingual communities"],"limitations":["Language detection accuracy degrades for languages with similar phonetic characteristics (e.g., Norwegian/Swedish) — may require explicit language hints","Code-switching support is limited to documented language pairs; arbitrary language mixing may produce degraded output","Some low-resource languages (fewer than 1M speakers) may have reduced accuracy compared to high-resource languages","Accent variation within languages can affect both language detection and understanding accuracy"],"requires":["OpenAI API key with gpt-4o-audio-preview model access","Audio with clear speech in supported language(s)","Optional: explicit language hints via API parameter to improve accuracy for ambiguous cases"],"input_types":["audio/wav","audio/mpeg","audio/mp4","audio/flac"],"output_types":["text (in detected language)","structured JSON with detected language code, confidence scores"],"categories":["text-generation-language","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-audio-preview__cap_6","uri":"capability://memory.knowledge.audio.context.preservation.across.turns","name":"audio-context-preservation-across-turns","description":"Maintains audio context across multiple conversation turns, allowing the model to reference acoustic characteristics from prior audio inputs (e.g., 'the person who sounded frustrated earlier') without requiring explicit re-upload. Uses a session-based context cache that stores compressed audio embeddings and allows subsequent requests to reference prior audio by session ID or turn number.","intents":["I want to build a voice assistant that remembers the emotional tone of earlier messages and adapts responses accordingly","I need to process multi-turn customer support calls where the model references earlier parts of the conversation by speaker characteristics","I want to create a voice journal app where the model can compare emotional tone across multiple entries over time"],"best_for":["conversational AI developers building stateful voice assistants","mental health and wellness platforms tracking emotional patterns over time","customer service teams analyzing conversation evolution across multiple interactions"],"limitations":["Context cache size limits the number of prior turns that can be referenced — likely 10-50 turns depending on audio length","Cached audio embeddings may become stale if model is updated; cache invalidation strategy not documented","No explicit control over which audio features are cached — model implicitly selects salient acoustic features","Session management requires client-side state tracking; no automatic session cleanup or expiration documented"],"requires":["OpenAI API key with gpt-4o-audio-preview model access","Session management capability on client side (storing session IDs, managing turn history)","Support for context cache API parameters (likely cache_control or similar)"],"input_types":["audio/wav","audio/mpeg","audio/mp4","audio/flac","session_id (string reference to prior audio context)"],"output_types":["text","audio/wav"],"categories":["memory-knowledge","audio-processing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-audio-preview__cap_7","uri":"capability://data.processing.analysis.audio.quality.and.noise.robustness","name":"audio-quality-and-noise-robustness","description":"Processes audio with background noise, music, or speech interference using noise-robust audio encoding that applies spectral gating and denoising attention layers before feeding audio to the main model. The model can extract speech and intent even from low-quality recordings (8kHz, high noise floor) by learning to suppress irrelevant acoustic features and focus on speaker-specific characteristics.","intents":["I need to transcribe and analyze customer support calls recorded on mobile phones with background noise","I want to extract intent from voice messages left on voicemail systems with poor audio quality","I need to process field recordings from noisy environments (factories, streets, vehicles) and extract meaningful information"],"best_for":["customer service teams processing real-world call center audio (often compressed, noisy)","field service companies analyzing voice notes from technicians in noisy environments","accessibility developers building voice interfaces that work on low-quality audio devices"],"limitations":["Noise robustness degrades significantly when noise level exceeds -10dB SNR (signal-to-noise ratio)","Heavy background music or multiple simultaneous speakers may confuse the model's speaker identification","Denoising attention adds ~200-400ms latency per request compared to clean audio processing","No explicit control over noise suppression aggressiveness — model applies fixed denoising strategy"],"requires":["OpenAI API key with gpt-4o-audio-preview model access","Audio file (quality not strictly required, but >8kHz sample rate recommended)"],"input_types":["audio/wav (any quality)","audio/mpeg (any bitrate)","audio/mp4","audio/flac"],"output_types":["text","structured JSON with confidence scores reflecting audio quality"],"categories":["data-processing-analysis","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-audio-preview__cap_8","uri":"capability://data.processing.analysis.audio.speaker.identification.and.diarization","name":"audio-speaker-identification-and-diarization","description":"Identifies and distinguishes between multiple speakers in a single audio file, assigning speaker labels ('Speaker 1', 'Speaker 2') and tracking speaker turns without requiring pre-labeled speaker data. Uses speaker embedding extraction and clustering to group acoustic features by speaker identity, enabling the model to attribute statements to specific speakers in multi-speaker conversations.","intents":["I need to transcribe a meeting recording and know which speaker said what","I want to analyze a podcast or interview and track speaker contributions separately","I need to process customer support calls with multiple participants and attribute statements to the correct speaker"],"best_for":["meeting transcription and analysis platforms","podcast and interview processing tools","customer service analytics teams analyzing multi-party conversations"],"limitations":["Diarization accuracy degrades with >4 simultaneous speakers or frequent speaker overlaps","Cannot distinguish between speakers with very similar voice characteristics (e.g., twins, same gender/age cohort)","Requires minimum audio duration (~30 seconds) to establish reliable speaker embeddings","No support for speaker identification across separate audio files — each file is processed independently"],"requires":["OpenAI API key with gpt-4o-audio-preview model access","Audio file with multiple speakers (minimum 2, recommended ≤4)"],"input_types":["audio/wav","audio/mpeg","audio/mp4","audio/flac"],"output_types":["structured JSON with speaker labels, turn boundaries, and speaker-attributed text segments"],"categories":["data-processing-analysis","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-4o-audio-preview__cap_9","uri":"capability://data.processing.analysis.audio.timestamp.and.segment.extraction","name":"audio-timestamp-and-segment-extraction","description":"Extracts precise timestamps for key events, statements, or emotional shifts within audio, returning structured data that maps text segments to their corresponding audio timestamps (e.g., 'frustration detected at 2:34-2:47'). Uses attention weight visualization to identify which audio frames contributed most to specific model outputs, enabling precise localization of events within the audio timeline.","intents":["I need to generate a highlight reel from a long video by identifying key moments (laughter, applause, important statements) with timestamps","I want to create searchable transcripts where users can click on any statement and jump to that point in the audio","I need to analyze customer support calls and flag the exact moment when sentiment shifted from positive to negative"],"best_for":["video and podcast editing platforms automating highlight extraction","searchable transcript platforms (e.g., Otter.ai, Rev) building timestamp-aware features","customer analytics teams identifying critical moments in conversations"],"limitations":["Timestamp precision is limited to ~100-500ms granularity depending on audio encoding resolution","Extraction accuracy degrades for short events (<500ms) or overlapping events","No support for sub-word-level timestamps (e.g., identifying which syllable triggered an emotion detection)","Timestamps are relative to input audio file; no absolute time support for streaming audio"],"requires":["OpenAI API key with gpt-4o-audio-preview model access","Audio file with clear temporal structure (silence between segments helps accuracy)"],"input_types":["audio/wav","audio/mpeg","audio/mp4","audio/flac"],"output_types":["structured JSON with timestamp ranges (start_ms, end_ms), event descriptions, and confidence scores"],"categories":["data-processing-analysis","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["OpenAI API key with gpt-4o-audio-preview model access","Audio files in supported formats (WAV, MP3, M4A, FLAC — exact list unconfirmed)","HTTP/2 capable client library to handle larger audio payloads efficiently","Audio playback capability on client (browser Web Audio API, mobile audio framework, or server-side audio handling)","Support for streaming audio chunks or buffering complete audio response before playback","Both audio file and text content in the same API request","Client-side logic to manage multimodal input assembly and validation","WebSocket or gRPC client library supporting streaming protocol","Audio capture capability with configurable chunk size (likely 20-100ms chunks at 16kHz sample rate)","Client-side buffering and jitter handling for network variability"],"failure_modes":["Audio encoding adds ~500-1500ms latency per request compared to text-only inputs","Maximum audio file size and duration limits not explicitly documented — likely 25MB or similar per OpenAI's standard constraints","No streaming audio input support — requires complete audio file upload before processing begins","Audio quality degradation below 8kHz sample rate may reduce nuance detection accuracy","Voice selection limited to OpenAI's predefined voice profiles — no custom voice cloning or fine-tuning","Audio output quality capped at 24kHz sample rate, limiting high-fidelity audio applications","No fine-grained control over speech rate, pitch, or emphasis per-word — only global voice parameters","Latency for audio generation scales with response length; 30-second audio may require 2-5 seconds generation time","No explicit control over modality weighting — model implicitly prioritizes one modality in ambiguous cases (behavior undocumented)","Cross-modal fusion adds ~300-800ms latency beyond single-modality processing","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.45,"ecosystem":0.27,"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":"2026-05-24T12:16:24.485Z","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=openai-gpt-4o-audio-preview","compare_url":"https://unfragile.ai/compare?artifact=openai-gpt-4o-audio-preview"}},"signature":"GBDpKzOaDveqlHl9TUjJkOyJd4JKf+VB2nMfHC+QLy/08k4YydDwDrTxAlBjiPtUG3FE+EGTwikM7XLFwDSYDw==","signedAt":"2026-06-20T03:46:06.351Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/openai-gpt-4o-audio-preview","artifact":"https://unfragile.ai/openai-gpt-4o-audio-preview","verify":"https://unfragile.ai/api/v1/verify?slug=openai-gpt-4o-audio-preview","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"}}