Laguna by Poolside vs Qwen-Image-Layered: Features, Pricing & Which Is Better (2026)

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.

Key features

• Layered image composition and analysis
• Multimodal inputs (text + image)
• Model weights and code published on GitHub
• Self-hosting and fine-tuning capability
• Playable via cloud-hosted inference when provided by Alibaba Cloud
• Public GitHub repository for the Qwen3 model series (source link provided)
• Developed and maintained by the Qwen team at Alibaba Cloud
• Repository-level hosting of model assets, documentation, and code for the Qwen3 series
• No specific feature list for 'Qwen-Image-Layered' is present in the provided content
• Technical APIs, integrations, platforms, and requirements are not detailed in the provided content

Best for