Hy4 preview vs Omnilingual ASR: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Hy4 preview and Omnilingual ASR — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Hy4 preview
Tencent
Tencent's open-weight Hy4 preview, a 770B-parameter Mixture-of-Experts model with 49B active parameters and a 1M-token context window.
Key features
- 770B Mixture-of-Experts Architecture: Holds 770 billion total parameters while activating only 49 billion per token, so capacity scales without proportional inference cost.
- 1M-Token Context Window: Accepts inputs exceeding one million tokens, allowing whole codebases, long document sets or extended agent traces in a single prompt.
- Apache 2.0 Open Weights: Released under a permissive licence that allows commercial use, modification and redistribution with no separate agreement.
- Productivity Task Focus: Tuned for real-world coding, office work and scientific research rather than narrow benchmark optimisation.
- Multi-Product Availability: Accessible globally through Tencent's WorkBuddy, CodeBuddy, Yuanbao and ima applications in addition to the raw weights.
- API Access via TokenHub and OpenRouter: Can be called through Tencent Cloud TokenHub or OpenRouter for teams that prefer hosted inference over self-hosting.
Best for
- Whole-Repository Code Work: Load an entire codebase into the million-token context to reason about refactors and cross-file dependencies at once.
- Long-Horizon Agent Tasks: Drive multi-step agent workflows where the full history of tool calls and intermediate results must stay in context.
- Self-Hosted Deployment: Run a frontier-scale open-weight model on private infrastructure where data cannot leave the organisation.
- Scientific Literature Analysis: Ingest large collections of papers or experimental logs and synthesise findings without chunking the input.
- Office Document Processing: Summarise, draft and restructure long reports, contracts and spreadsheets in enterprise workflows.
- Commercial Fine-Tuning: Adapt the weights for a proprietary product under the Apache 2.0 licence without negotiating a model licence.
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