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April 2026
  • Context window: 128K (131,072 tokens)
  • Model type: Mixture of Experts (MoE)
  • Supported modes: Agentic, Chat
  • Modality: Text only

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Summary

Laguna XS.2 is the second-generation model in the new Laguna family from Poolside. It is an agentic model that is strongest on coding tasks that require multiple steps, tool use, and validation, such as exploring a codebase, editing files, running tests, and iterating on a fix. You get the most value from Laguna XS.2 when you use it in Poolside Agent workflows instead of as a standalone chat model. Laguna XS.2 trades performance on Poolside’s reference benchmarks for faster agentic coding, making it a strong companion to its larger counterpart. Use Laguna XS.2 when you want an agent to:
  • Debug and fix issues across multiple files
  • Explore unfamiliar code and explain what it finds
  • Run well-defined coding tasks that require fast performance
  • Deploy Poolside on hardware with GPU constraints

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Improvements

  • Stronger coding performance: Laguna XS.2 improves SWE-bench Verified from 55.6% in Malibu 2.2 to 64%.
  • Better multilingual coding results: Laguna XS.2 improves SWE-bench Multilingual from 31.1% in Malibu 2.2 to 60%.
  • Better agentic task execution: Laguna XS.2 improves Terminal-Bench 2.0 from 16.9% in Malibu 2.2 to 29%.

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Tips for prompting

  • Give Laguna XS.2 a clear task with the specific outcome you want.
  • Include relevant context such as file paths, error messages, failing tests, or reference material.
  • State any important constraints up front, such as coding standards, files to avoid, or commands the agent should run.
  • Expect better results when the environment includes tools the agent can use to verify its work.
  • Keep tasks self-contained by planning with the model.

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Compatibility notes

  • Laguna XS.2 is designed for Poolside Agent workflows in Poolside Assistant for VS Code and Visual Studio, Poolside Agent CLI, Poolside Chat, and OpenAI-compatible API integrations.
  • Laguna XS.2 is text-in, text-out only and does not support vision inputs.
  • Performance depends on the quality of the instructions and context you provide, the tools available to the agent, and whether the environment supports validation steps such as tests or executable checks.