Chatter vs Laguna by Poolside: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Chatter and Laguna by Poolside — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
C
Chatter
Unknown Developer
Your apps main description and features.
Key features
- Market visualizations (candlestick charts, Bollinger Bands — referenced in related projects)
- Portfolio analytics and performance insights
- Sentiment analysis combining news and social media chatter
- Symbol lookup / name-to-symbol search (SYMBOL_SEARCH-like functionality referenced by third-party projects)
- Web-based application interface (official site available)
Best for
- Visual exploration of stock and crypto price data using technical charts
- Portfolio performance analysis and optimization
- Incorporating news and social media sentiment into trading research
- Converting company names to tradable symbols for quote lookup and trade routing
- Automated reporting and alerts (third-party projects demonstrate report/email workflows)
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.