Poolside: Laguna M.1
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Laguna M.1 is Poolside's flagship agentic coding model, built for complex, long-horizon software engineering tasks. It's a 225B-parameter Mixture-of-Experts model with 23B activated parameters, offering a 128K context window and support for tool calling and reasoning.
On SWE-bench Verified it scores 72.5%, and it reaches 46.9% on the harder SWE-bench Pro. These results place it in the same tier as far larger models like Qwen3.5 and DeepSeek V4-Flash while using a fraction of the active compute.
Laguna M.1 is purpose-built for agentic workflows — writing code, running tests, inspecting failures, and iterating across files. If you need a model that can plan and execute multi-step engineering tasks end to end, this is Poolside's strongest option.
Context Window 131K
tokens
Max Output 8K
tokens
Input Cost $0
per million tokens
Output Cost $0
per million tokens
Release Date Apr 28, 2026
Code Example
Add AI to your app with the Puter.js AI API — no API keys or setup required.
// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';
puter.ai.chat("Explain quantum computing in simple terms").then(response => {
document.body.innerHTML = response.message.content;
});
<html>
<body>
<script src="https://js.puter.com/v2/"></script>
<script>
puter.ai.chat("Explain quantum computing in simple terms").then(response => {
document.body.innerHTML = response.message.content;
});
</script>
</body>
</html>
More AI Models From Poolside
Laguna S 2.1
Laguna S 2.1 is Poolside's mid-size agentic coding model, sitting between the Laguna XS and M families. It's a 118B-parameter Mixture-of-Experts model with 8B activated parameters, released under the OpenMDW-1.1 license with a 1M-token context window. It scores 78.5% on SWE-bench Multilingual, 59.4% on SWE-bench Pro, and 70.2% on Terminal-Bench 2.1. Poolside reports these results match or exceed models with two to eight times as many active parameters, including DeepSeek-V4-Flash and NVIDIA's Nemotron 3 Ultra. Laguna S 2.1 supports tool calling, thinking mode, and multi-step reasoning for agentic coding workflows. It suits developers who need stronger coding performance than XS 2.1 without the cost of the larger M.1 model.
ChatLaguna XS 2.1
Laguna XS 2.1 is Poolside's updated compact agentic coding model, a 33B-parameter Mixture-of-Experts architecture with 3B activated parameters, released under the OpenMDW-1.1 license as a successor to Laguna XS.2. It scores 70.9% on SWE-bench Verified, 63.1% on SWE-bench Multilingual, 47.6% on SWE-bench Pro, and 37.5% on Terminal-Bench 2.0, each an improvement over XS.2, with the largest gain (+5.4 points) on the multilingual benchmark. It trails larger models like Qwen3.6-35B-A3B on multilingual coding but stays competitive within its size class. The model keeps the same 262K context window, tool calling, and reasoning support as its predecessor. It suits developers already using Laguna XS.2 who want incremental gains on multilingual and terminal-style coding tasks without moving to a larger model.
Frequently Asked Questions
You can access Laguna M.1 by Poolside through Puter.js AI API. Include the library in your web app or Node.js project and start making calls with just a few lines of JavaScript — no backend and no configuration required. You can also use it with Python or cURL via Puter's OpenAI-compatible API.
Yes, it is free if you're using it through Puter.js. With the User-Pays Model, you can add Laguna M.1 to your app at no cost — your users pay for their own AI usage directly, making it completely free for you as a developer.
| Price per 1M tokens | |
|---|---|
| Input | $0 |
| Output | $0 |
Laguna M.1 was created by Poolside and released on Apr 28, 2026.
Laguna M.1 supports a context window of 131K tokens. For reference, that is roughly equivalent to 262 pages of text.
Laguna M.1 can generate up to 8K tokens in a single response.
Yes — the Laguna M.1 API works with any JavaScript framework, Node.js, or plain HTML through Puter.js. Just include the library and start building. See the documentation for more details.
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