About
Laguna S 2.1 is the latest coding agent model from Poolside.
Pricing
| Input | $0.13 | per 1M prompt tokens |
|---|---|---|
| Output | $0.25 | per 1M generated tokens, reasoning included |
| Cached input | $0.01 | per 1M tokens read back from the prompt cache |
10% off right now. The provider is running a promotion, and the developer API bills what the provider actually charged for your call plus our flat markup, so it reaches your bill with nothing to claim. The “from” figure is the cheapest endpoint serving the model; which one answers is the provider’s routing decision, so treat it as a floor. Promotions can end at any time.
What a request costs
| Typical request | Each | Per 1,000 |
|---|---|---|
| Short chat reply1,500 in · 400 out | $0.0003 | $0.290 |
| Question over a long document40,000 in · 800 out | $0.0052 | $5.24 |
| Agent tool step20,000 in · 1,200 out | $0.0028 | $2.82 |
At the developer-API rate, before any provider promotion or prompt-cache saving. In the app, the model picker shows what a message costs on your plan before you send it.
Call it from code
OpenAI-compatible: point any OpenAI SDK at https://api.privateer.pro/v1 and set
model. Keys are created in the app under Settings.
curl https://api.privateer.pro/v1/chat/completions \
-H "Authorization: Bearer $PRIVATEER_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "poolside/laguna-s-2.1",
"messages": [{ "role": "user", "content": "Hello" }]
}'import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.privateer.pro/v1",
api_key=os.environ["PRIVATEER_API_KEY"],
)
reply = client.chat.completions.create(
model="poolside/laguna-s-2.1",
messages=[{"role": "user", "content": "Hello"}],
)
print(reply.choices[0].message.content)import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://api.privateer.pro/v1",
apiKey: process.env.PRIVATEER_API_KEY,
});
const reply = await client.chat.completions.create({
model: "poolside/laguna-s-2.1",
messages: [{ role: "user", content: "Hello" }],
});
console.log(reply.choices[0].message.content);Parameters this model accepts
include_reasoningmax_tokensreasoningtemperaturetool_choicetools