Laguna by Poolside vs Omnilingual ASR: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Laguna by Poolside and Omnilingual ASR — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Laguna by Poolside
Poolside
Poolside's family of open Mixture-of-Experts foundation models for agentic coding — XS.2 runs locally, M.1 reaches 72.5% on SWE-bench Verified.
Key features
- Two Model Sizes: Laguna XS.2 (33B total / 3B active) and Laguna M.1 (225B total / 23B active) target different latency and capability needs.
- Mixture-of-Experts Architecture: Routes each token through a subset of experts for efficiency at large scale.
- Local Deployment: XS.2 is small enough to run on a Mac with 36 GB of RAM via Ollama under an Apache 2.0 license.
- Strong SWE-bench Results: XS.2 hits 68.2% and M.1 reaches 72.5% on SWE-bench Verified.
- Bundled Coding Agent: Ships 'pool,' a lightweight terminal-based coding agent.
- Agent Client Protocol: Includes a dual ACP client-server used internally for agent RL training and evaluation.
Best for
- Local Agentic Coding: Running XS.2 on a laptop for private, offline code generation and editing.
- High-Capability Code Tasks: Using M.1 for harder, long-horizon software engineering work.
- Self-Hosted Deployments: Building on open weights to avoid third-party API dependencies.
- Research & Fine-Tuning: Adapting permissively licensed weights for custom coding workflows.
- Benchmarking: Evaluating agentic coding performance against SWE-bench Verified and Pro.
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