The Directory

AI Tools

New launches tracked daily — what they cost, who they're for, how to get the most out of them

All tools

Codingby Poolside

Laguna S 2.1

Name: Laguna S 2.1
Author: Poolside

$0.1/mofree tier available

118B open-weight coding MoE model designed for agentic software engineering

Laguna S 2.1 is a 118-billion-parameter Mixture-of-Experts (MoE) model with 8 billion activated parameters per token, developed by Poolside for long-horizon agentic coding tasks. It features a 1,048,576-token context window, native reasoning/thinking capabilities, and a mixed attention architecture combining global and sliding-window attention. The model is open-weight under the OpenMDW-1.1 license and can be self-hosted on a single NVIDIA DGX Spark. On benchmark tasks like Terminal-Bench 2.1 (70.2%), SWE-Bench Pro (59.4%), and DeepSWE (40.4%), it matches or exceeds models several times its size, including DeepSeek-V4-Pro-Max.

Who it's for

DevelopersSoftware engineersDevOps teamsGovernment agenciesDefense contractorsEnterprise teamsCode-focused organizations

Pricing · freemium

checked Jul 22, 2026

Plan	Price	Includes
Free (Limited Context)	Free	262K token context window · 32K max output tokens · Free usage with model improvement clause
Paid via OpenRouter (1M Context)	Free	$0.10 per million input tokens · $0.20 per million output tokens · 1M token context window · 131K max output tokens
Featherless Flat-Rate	$10 /mo	Flat-rate pricing from $10/month · OpenAI-compatible API · 250K context access

AI-researched pricing — verify on the official site before subscribing.

Use it for

— Agentic coding and automation
— Long-horizon software engineering tasks
— Complex codebase analysis and modification
— Terminal-based coding agents
— Multi-step problem solving with tool integration
— Self-hosted deployment for compliance and sovereignty
— Building coding assistants and copilots

Get the most out of it

01Use pool, Poolside's native agent harness, for tighter integration with the model's 1M context window and native thinking capabilities—it offers superior ergonomics compared to third-party wrappers
02Enable thinking/reasoning mode (enabled by default) to improve benchmark performance significantly; Terminal-Bench improves from 60.4% to 70.2% and DeepSWE from 16.5% to 40.4%
03Self-host for long-running agentic work: the 236GB BF16 checkpoint fits on a single DGX Spark, allowing you to move high-volume coding tasks off metered APIs onto controlled hardware
04Leverage the 1M token context window for analyzing entire codebases and long task histories without chunking, enabling better reasoning and fewer round-trips
05For consumer hardware deployment, use the smaller Laguna XS 2.1 variant (33B, 3B active parameters) which runs comfortably on 24GB VRAM machines

Visit Laguna S 2.1

Was this useful?