Laguna by Poolside vs Qwen-Image-Layered: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Laguna by Poolside and Qwen-Image-Layered — 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.
Qwen-Image-Layered
Qwen team, Alibaba Cloud
A named image-layered component associated with the Qwen model family from the Qwen team at Alibaba Cloud.
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
- Image editing and compositional generation
- Vision-language tasks (captioning, VQA) with layered inputs
- Design and advertising content generation
- Research, fine-tuning, and benchmarking
- Integration into cloud-hosted applications via Alibaba Cloud
- Not specified in the provided content; repository likely intended for research, development, and model distribution for the Qwen3 series
- Users should consult the GitHub repository for concrete use cases, examples, and integration instructions