Laguna S 2.1
Open Weights • Released 2026-07-13 • Last Verified 2026-09-21
Laguna S 2.1 is a versatile open-weight language model developed by Poolside. Built on modern Transformer architecture with Grouped-Query Attention (GQA) and Rotary Position Embeddings (RoPE), Laguna S 2.1 delivers strong performance in instruction following, multi-turn dialogue, creative synthesis, and structured JSON output generation.
Plain English Summary (What is this model & who is it for?)
Think of Laguna S 2.1 as a versatile AI assistant for writing, research, and brainstorming. It helps you draft emails, write essays, summarize long articles, and generate creative ideas on any topic.
💡 Real-World Use Cases & Practical Examples
🚀 How to Run & Use This Model (Step-by-Step Guide)
Simple setup instructions for everyday users and developers.
Sign Up & Get API Access
Create a account on Poolside's official developer portal and obtain your API Key.
Try the Interactive Playground
Click the "Try Playground" button at the top of this page to test prompts instantly inside your web browser.
Send Your First Request
Use standard HTTP cURL requests or official Python/Node.js SDKs to send prompts to the endpoint.
Integrate Into Your App
Pass the model ID "poolside-laguna-s-2-1" into your code payload to power chatbots, workflows, and web applications.
Benchmark Performance
Hardware Requirements for Local Running
Consumer GPU / CPU compatible
Strengths
- •Strong Instruction Following & Alignment
- •Multi-Turn Dialogue Context Stability
- •Low-Latency Batch Inference Execution
- •Support for Structured JSON & Schema Enforcing
Limitations & Weaknesses
- •Requires local GPU hardware for self-hosting
import openai
client = openai.OpenAI()
response = client.chat.completions.create(
model="poolside-laguna-s-2-1",
messages=[
{"role": "system", "content": "You are an expert AI assistant."},
{"role": "user", "content": "Explain quantum computing in 2 sentences."}
]
)
print(response.choices[0].message.content)Pricing Overview
Prices subject to provider tiers and volume discounts. Check documentation for current token rates.
Model Tags
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