Mouse-1 vs Omnilingual ASR: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Mouse-1 and Omnilingual ASR — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Mouse-1
Mouse
Mouse-1 is a fast, low-cost decision model that plugs into agent harnesses to handle routing, gating and verification.
Key features
- Decision Layer for Agents: Takes agent state and options and returns probabilities so the harness can route, gate and verify.
- About 100 ms Latency: Answers faster than a chat model's first token.
- Batch Decisions: One request answers up to 128 questions.
- Prompt-Injection Resistance: Chooses only from supplied options; planted instructions changed 0% of decisions in vendor tests.
- Zero Data Retention: Requests are scored and dropped, never stored or used for training.
- Image and Document Classification: Handles content moderation, document routing and browser-state detection.
Best for
- Agent Routing: Deciding which model or tool an agent should use next.
- Action Gating: Approving or blocking risky agent actions cheaply.
- Content Moderation: Classifying images or text as safe or unsafe.
- Document Routing: Sending invoices, contracts and IDs to the right workflow.
- Browser Agents: Detecting screen state such as login required or error.
- Cost Reduction: Offloading small decisions from expensive frontier models.
Omnilingual ASR
Meta
Open-source multilingual speech recognition system that natively transcribes 1,600+ languages with low-resource adaptability.
Key features
- Wide Language Coverage: Native transcription support for over 1,600 languages, including hundreds not previously supported by ASR systems, enabling extensive global language coverage.
- Scalable Zero-Shot Learning: Model family and training procedures allow adding new languages with only a few paired examples, reducing the need for large annotated datasets or specialized expertise.
- Multilingual Audio Representation Model: Includes a large (e.g., 7-billion-parameter) multilingual audio representation model designed to generalize across languages and acoustic conditions for robust transcription.
- Large Open Corpus: Publishes a massive Omnilingual ASR corpus spanning hundreds of underserved languages (hosted on Hugging Face), enabling research, fine-tuning, and reproducible evaluation.
- Open-Source Code and Weights: Releases model weights, training/evaluation code, dataset conversion tools, and example scripts on GitHub to enable replication, customization, and community contributions.
- Low-Resource Fine-Tuning Tools: Provides workflows and tooling for efficiently fine-tuning models on small paired datasets to rapidly adapt to new languages or dialects.
- Hugging Face Integration and Demos: Offers demo spaces and dataset access on Hugging Face for quick evaluation and experimentation without custom infrastructure.
- Dataset Conversion & Processing Utilities: Includes converters (e.g., parquet conversion) and dataset management utilities to streamline preparing and using audio-text corpora.
- Supports automatic speech recognition for 1,600+ languages
- Scalable zero-shot learning to enable recognition of new languages with few paired examples
- Flexible model family suitable for adaptation and fine-tuning
- Open-source codebase hosted on GitHub (facebookresearch/omnilingual-asr)
- Associated omnilingual-asr-corpus dataset published on Hugging Face for training/evaluation
- Designed to work without large datasets or specialized expertise for adding languages
Best for
- Servicing Low-Resource Languages: Deploying transcription systems for underserved or endangered languages in community projects, local journalism, and cultural preservation with minimal labeled data.
- Multilingual Subtitling and Media Localization: Generating native-language transcriptions and subtitles for audio/video content across hundreds of languages for global media distribution.
- Accessible Technology & Assistive Tools: Integrating into accessibility products (live captioning, hearing assistance) to provide native-language support for diverse speaker populations.
- Research and Linguistic Analysis: Enabling linguists and researchers to analyze speech patterns, phonetics, and language use across many languages using an open corpus and reproducible models.
- Rapid Language Support for Apps: Adding speech transcription to consumer or enterprise apps (voice notes, search, voice commands) for new languages quickly via few-shot adaptation.
- Dataset Creation and Community Annotation: Using provided dataset tools and corpus to bootstrap community-driven data collection and annotation pipelines for local languages.
- Deploying ASR for low-resource and previously unsupported languages
- Research and development of multilingual speech models
- Rapid prototyping of speech recognition in community/localization projects
- Fine-tuning and adapting models to domain- or language-specific audio with few paired examples
- Building speech datasets and evaluation benchmarks using the provided corpus