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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 M.1 is a new Laguna family model from Poolside for agentic work. It 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 M.1 when you use it in Poolside Agent workflows instead of as a standalone chat model. Compared with Malibu 2.2, Laguna M.1 improves performance on Poolside’s reference benchmarks for agentic coding and tool-using workflows. Use Laguna M.1 when you want an agent to:
  • Debug and fix issues across multiple files
  • Explore unfamiliar code and explain what it finds
  • Run longer task sequences that require tool use and verification
  • Work through coding tasks where tests, commands, or other checks are available

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Improvements

  • Stronger coding performance: Laguna M.1 improves SWE-bench Verified from 55.6% in Malibu 2.2 to 65.4%.
  • Better multilingual coding results: Laguna M.1 improves SWE-bench Multilingual from 31.1% in Malibu 2.2 to 57.4%.
  • Better agentic task execution: Laguna M.1 improves Terminal-Bench 2.0 from 16.9% in Malibu 2.2 to 32.7%.

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Tips for prompting

  • Give Laguna M.1 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.

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Compatibility notes

  • Laguna M.1 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 M.1 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.