{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-mistralai-voxtral-small-24b-2507","slug":"mistralai-voxtral-small-24b-2507","name":"Mistral: Voxtral Small 24B 2507","type":"model","url":"https://openrouter.ai/models/mistralai~voxtral-small-24b-2507","page_url":"https://unfragile.ai/mistralai-voxtral-small-24b-2507","categories":["voice-audio"],"tags":["mistralai","api-access","text","audio"],"pricing":{"model":"paid","free":false,"starting_price":"$1.00e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-mistralai-voxtral-small-24b-2507__cap_0","uri":"capability://text.generation.language.speech.to.text.transcription.with.multilingual.support","name":"speech-to-text transcription with multilingual support","description":"Converts audio input (speech) directly into text transcriptions using an integrated audio encoder that processes raw audio waveforms before feeding them into the language model backbone. The model handles variable-length audio sequences and automatically detects language context from acoustic features, enabling accurate transcription across 40+ languages without requiring explicit language specification. Works with streaming and batch audio inputs up to model context limits.","intents":["I need to transcribe recorded meetings, podcasts, or user-generated audio into searchable text","I want to build a voice-first application that converts speech to text as the first step in a processing pipeline","I need to handle multilingual audio without pre-specifying the language or using separate language-specific models"],"best_for":["developers building voice-enabled applications and chatbots","teams processing large volumes of audio content for transcription workflows","multilingual SaaS platforms requiring speech-to-text without language detection overhead"],"limitations":["Audio input must be preprocessed to supported formats (WAV, MP3, M4A, FLAC); no raw PCM streaming without format wrapping","Transcription accuracy degrades with heavy background noise, music, or overlapping speakers — no built-in speaker diarization","Context window limits total audio duration; very long recordings may require chunking and reassembly logic in client code","No fine-tuning capability for domain-specific vocabulary or accent adaptation"],"requires":["API key for Mistral or OpenRouter access","Audio file in supported format (WAV, MP3, M4A, FLAC, OGG)","HTTP/REST client or SDK supporting multipart form data for audio upload","Network connectivity to Mistral API endpoints"],"input_types":["audio (WAV, MP3, M4A, FLAC, OGG)","raw audio bytes with format metadata"],"output_types":["text (transcription)","structured JSON with timestamps and confidence scores (if supported)"],"categories":["text-generation-language","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-voxtral-small-24b-2507__cap_1","uri":"capability://text.generation.language.audio.to.text.translation.with.cross.lingual.transfer","name":"audio-to-text translation with cross-lingual transfer","description":"Transcribes audio in a source language and simultaneously translates the transcribed content into a target language (or multiple targets) within a single forward pass. The model uses a shared audio encoder that extracts language-agnostic acoustic features, then routes them through language-specific decoder heads trained on parallel multilingual data. This architecture avoids cascading errors from separate transcription-then-translation pipelines.","intents":["I need to convert a French podcast into English text in one API call without separate transcription and translation steps","I want to build a real-time interpretation system that transcribes and translates simultaneously for accessibility or international meetings","I need to extract and translate speech content while preserving timing and speaker context"],"best_for":["international teams needing real-time meeting transcription and translation","content creators localizing audio content across multiple markets","accessibility platforms providing live captions in multiple languages"],"limitations":["Translation quality depends on source audio clarity; poor transcription cascades into poor translation","No explicit control over translation style (formal vs. casual) or domain-specific terminology without prompt engineering","Target language must be specified in advance; dynamic multi-target translation requires multiple API calls","Idioms and cultural references may not translate accurately due to acoustic-level training data limitations"],"requires":["API key for Mistral or OpenRouter","Source audio file in supported format","Target language code (ISO 639-1 or similar) specified in API request","HTTP client supporting multipart requests with metadata parameters"],"input_types":["audio (WAV, MP3, M4A, FLAC, OGG)","target language identifier (string)"],"output_types":["text (translated transcription)","structured JSON with source and target language labels"],"categories":["text-generation-language","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-voxtral-small-24b-2507__cap_2","uri":"capability://text.generation.language.audio.content.understanding.and.semantic.analysis","name":"audio content understanding and semantic analysis","description":"Analyzes audio input to extract semantic meaning, intent, emotion, speaker characteristics, and contextual information beyond raw transcription. The model processes audio through its integrated encoder to generate rich embeddings that capture prosody, tone, and acoustic patterns, then applies language understanding layers to infer speaker intent, sentiment, topic, and metadata. Supports queries like 'summarize the key decisions from this meeting' or 'extract action items and assign them to speakers'.","intents":["I need to automatically extract key decisions, action items, and speaker assignments from recorded meetings","I want to analyze customer support calls to detect sentiment, frustration levels, and resolution success","I need to categorize audio content by topic, intent, or quality metrics without manual review"],"best_for":["enterprise teams analyzing meeting recordings for compliance, insights, and action tracking","customer success teams monitoring support call quality and customer satisfaction","content platforms auto-tagging and categorizing audio libraries"],"limitations":["Semantic analysis quality depends on audio clarity and speaker articulation; mumbling or unclear speech reduces accuracy","No built-in speaker identification or diarization; cannot reliably assign statements to specific speakers without explicit speaker labels","Emotion and sentiment detection is probabilistic and may misinterpret sarcasm, cultural context, or domain-specific language","Analysis is performed post-hoc on complete audio; no real-time streaming analysis capability"],"requires":["API key for Mistral or OpenRouter","Complete audio file (streaming not supported for full analysis)","Optional: structured prompt specifying analysis type (sentiment, action items, summary, etc.)","HTTP client for multipart audio upload with prompt parameters"],"input_types":["audio (WAV, MP3, M4A, FLAC, OGG)","optional text prompt specifying analysis task"],"output_types":["text (analysis results, summaries, extracted entities)","structured JSON with labeled insights (sentiment scores, action items, topics)"],"categories":["text-generation-language","data-processing-analysis","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-voxtral-small-24b-2507__cap_3","uri":"capability://text.generation.language.audio.conditioned.text.generation.with.context.preservation","name":"audio-conditioned text generation with context preservation","description":"Generates coherent text responses conditioned on audio input, maintaining semantic and contextual information from the audio throughout generation. The model encodes audio into a fixed-size representation that is injected into the language model's hidden states, allowing the decoder to generate text that directly references, summarizes, or responds to audio content. Supports use cases like generating meeting summaries, answering questions about audio content, or creating follow-up messages based on conversation context.","intents":["I need to generate a meeting summary or recap email based on a recorded conversation","I want to answer user questions about the content of an audio file without requiring manual transcription first","I need to generate follow-up action items or documentation based on what was discussed in an audio recording"],"best_for":["productivity tools generating meeting summaries and action items","customer service platforms auto-generating responses or summaries from call recordings","knowledge management systems creating documentation from recorded training sessions or presentations"],"limitations":["Generated text quality depends on audio clarity and speaker coherence; rambling or disorganized audio produces unfocused summaries","No explicit control over summary length, style, or emphasis without prompt engineering","Cannot selectively focus on specific speakers or time ranges within audio without preprocessing or prompt specification","Hallucination risk exists if audio is ambiguous or if the model infers details not explicitly stated"],"requires":["API key for Mistral or OpenRouter","Audio file in supported format","Text prompt specifying generation task (summary, Q&A, action items, etc.)","HTTP client supporting multipart requests with audio and prompt parameters"],"input_types":["audio (WAV, MP3, M4A, FLAC, OGG)","text prompt (generation instruction)"],"output_types":["text (generated summary, response, or documentation)","structured JSON with labeled sections (summary, action items, key decisions)"],"categories":["text-generation-language","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-voxtral-small-24b-2507__cap_4","uri":"capability://text.generation.language.multimodal.prompt.handling.with.audio.and.text.inputs","name":"multimodal prompt handling with audio and text inputs","description":"Accepts simultaneous audio and text inputs in a single API request, allowing developers to provide context, instructions, or supplementary information via text while the model processes audio content. The model's architecture supports interleaved audio and text tokens, enabling prompts like 'Transcribe this audio [AUDIO] and answer the question: [TEXT]' or 'Summarize this meeting [AUDIO] focusing on decisions about [TEXT TOPIC]'. Text and audio are encoded through separate pathways and fused in the model's hidden layers.","intents":["I want to ask questions about audio content in the same API call, like 'What did the speaker say about budget?' while providing the audio","I need to provide context or instructions alongside audio, such as 'Transcribe this call and extract only customer complaints'","I want to combine audio analysis with text-based reasoning, like 'Does this audio match the transcript provided?'"],"best_for":["developers building interactive audio analysis tools with dynamic prompting","QA and compliance teams verifying audio content against transcripts or policies","research applications combining audio and text modalities for multimodal understanding"],"limitations":["Audio and text must be provided in the same request; no separate streaming or sequential processing","Token counting for mixed audio-text inputs is non-trivial; developers must account for audio encoding overhead","No explicit control over how audio and text are weighted or fused in the model; fusion is implicit in training","Prompt engineering for multimodal inputs is less mature than text-only prompting; results may be less predictable"],"requires":["API key for Mistral or OpenRouter","Audio file in supported format","Text prompt or context string","HTTP client supporting multipart requests with both audio and text parameters","Understanding of how audio and text tokens are counted for billing/rate limiting"],"input_types":["audio (WAV, MP3, M4A, FLAC, OGG)","text (prompt, context, or instructions)"],"output_types":["text (response, analysis, or answer)","structured JSON with multimodal analysis results"],"categories":["text-generation-language","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-voxtral-small-24b-2507__cap_5","uri":"capability://text.generation.language.real.time.audio.streaming.with.incremental.transcription","name":"real-time audio streaming with incremental transcription","description":"Processes audio input as a continuous stream rather than requiring complete file uploads, enabling low-latency transcription and analysis of live audio sources (meetings, broadcasts, phone calls). The model uses a streaming encoder that processes audio chunks incrementally and generates partial transcriptions as audio arrives, with optional refinement as more context becomes available. Supports WebSocket or HTTP chunked transfer encoding for continuous audio delivery.","intents":["I need to transcribe live meetings or calls in real-time with minimal latency for live captioning","I want to build a voice assistant that responds to user speech as it's being spoken","I need to monitor and analyze audio streams continuously without buffering entire recordings"],"best_for":["live captioning and accessibility platforms for real-time events","voice assistant and conversational AI applications requiring sub-second response latency","broadcast and streaming platforms needing real-time transcription and moderation"],"limitations":["Streaming transcription may produce partial or incorrect results that are corrected as more context arrives; clients must handle refinement logic","Latency is higher than batch processing due to streaming overhead; typical latency is 1-3 seconds behind real-time audio","No built-in buffering or error recovery; network interruptions may cause transcription gaps or require reconnection","Streaming state is not persisted; reconnection requires restarting transcription from the current point, not from the beginning"],"requires":["API key for Mistral or OpenRouter with streaming support enabled","WebSocket or HTTP/2 connection for streaming audio chunks","Audio source capable of continuous streaming (microphone, audio device, or network stream)","Client-side logic to handle partial results, refinements, and error recovery"],"input_types":["audio stream (chunked WAV, MP3, or raw PCM with format metadata)"],"output_types":["text (incremental transcription updates)","structured JSON with partial results and confidence scores"],"categories":["text-generation-language","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"low","permissions":["API key for Mistral or OpenRouter access","Audio file in supported format (WAV, MP3, M4A, FLAC, OGG)","HTTP/REST client or SDK supporting multipart form data for audio upload","Network connectivity to Mistral API endpoints","API key for Mistral or OpenRouter","Source audio file in supported format","Target language code (ISO 639-1 or similar) specified in API request","HTTP client supporting multipart requests with metadata parameters","Complete audio file (streaming not supported for full analysis)","Optional: structured prompt specifying analysis type (sentiment, action items, summary, etc.)"],"failure_modes":["Audio input must be preprocessed to supported formats (WAV, MP3, M4A, FLAC); 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