Laguna by Poolside vs Qwen 3: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Laguna by Poolside and Qwen 3 — 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 3
Alibaba
Qwen 3 is the next-generation Qwen series LLM family offering multimodal, agentic, and high-reasoning capabilities across dense and MoE model variants.
Key features
- Thinking Mode: A configurable reasoning mode (enable_thinking) that lets the model engage chain-of-thought style internal reasoning to improve complex logical, mathematical, and coding responses while allowing switching to a non-thinking mode for efficient general-purpose dialogue.
- Mixture-of-Experts (MoE) & Dense Variants: A family of model sizes including large MoE configurations (e.g., extremely large-parameter MoE coder variants) and smaller dense checkpoints, enabling selection of performance vs. resource tradeoffs for inference and agentic tasks.
- Multimodal Vision-Language Capabilities: Qwen3-VL accepts image, text, and bounding-box inputs and produces unified text and localization outputs, with improved robustness to low-light, blur, tilt, and rare characters and stronger long-document visual-text understanding.
- Coder & Agentic Specializations: Qwen3-Coder variants (including very large MoE coder models) are optimized for coding, agentic browsing and tool use, and demonstrate state-of-the-art open-model performance on agentic coding and automated tool-use benchmarks.
- Long-Context Processing: Native support for context lengths up to 32,768 tokens and demonstrated methods (RoPE scaling, YaRN) to handle and validate extreme contexts up to 131,072 tokens for long-document understanding and multi-document workflows.
- Tool-Call & Integration Support: Native tooling and community integrations (Qwen-Agent, vLLM, Qwen Code CLI) support tool-call parsing, native API tool calls, and orchestration of external tools and web search to build interactive chatbots and agent pipelines.
- Developer Ecosystem & Open Access: Model checkpoints and adapters are available on Hugging Face and GitHub repositories with quickstart guidance for transformers, community code (CLI tools, agent examples), and compatibility notes for modern runtimes like vLLM and transformers versions.
- Dense and Mixture-of-Experts (MoE) model variants including large MoE models (example: Qwen3-Coder-480B-A35B-Instruct with 480B parameters and 35B active)
- Dedicated code-focused variant (Qwen3-Coder) optimized for agentic coding, browser automation, and tool use
- Multimodal vision-language variant (Qwen3-VL) accepting image, text, and bounding-box inputs; outputs text and bounding boxes
- Built-in reasoning/thinking mode (enable_thinking option enabled by default) for improved instruction following and chain-of-thought style reasoning
- Long-context support: native context up to 32,768 tokens; validated up to 131,072 tokens using YaRN and RoPE scaling techniques
- FP8 model formats and Hugging Face/Transformers compatibility (requires recent transformers versions)
- Native and ecosystem tool-call integration (Qwen-Agent, vLLM tool-call parsing, use_raw_api option guidance for Qwen3-Coder)
- CLI and developer tooling: Qwen Code CLI, Qwen-Agent repositories and demos for agent/tool integration
- Integration options with cloud tooling (PAI-DSW) and third-party routers (OpenRouter) providing API access and free tiers
Best for
- Agentic Coding Assistant: Use Qwen3-Coder to build an intelligent coding assistant that reasons over large codebases, proposes multi-step code changes, performs automated refactors, and executes tool-call workflows (e.g., run tests, open browser, edit files).
- Multimodal Document Analysis: Use Qwen3-VL to ingest long multimodal documents (images + text + bounding boxes) for structured extraction, OCR of rare/ancient characters, visual QA, and summarization of long reports or scanned books.
- Interactive Chatbots with Tool Integration: Deploy conversational agents that call web search, external APIs, or specialized tools via Qwen-Agent and built-in tool-call parsing to answer user queries with live data and actionable outputs.
- Image Understanding & Generation Workflows: Combine Qwen3-VL understanding with image-generation modules to perform tasks such as image captioning, content-aware image editing, and guided generation from textual and visual context.
- Long-Form Reasoning & Research Assistance: Leverage long-context capabilities to perform multi-document synthesis, literature review summarization, multi-step mathematical problem solving, and deep logical reasoning across large inputs.
- CLI-driven Developer Workflows: Integrate Qwen Code CLI and model checkpoints for local architecture analysis, dependency discovery, API exploration, and iterative code development using the model as an assistant in terminal-based workflows.
- Agentic coding assistants that can call external tools, browse, and automate programming tasks
- Multimodal understanding tasks such as VQA, object localization, OCR and visual grounding
- Large-context document understanding, summarization, and long conversational agents
- Tool-enabled agents that orchestrate web search, APIs, and external utilities
- Research and benchmarking of instruction-following, reasoning, and agentic capabilities