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Laguna S 2.1 API: toman pricing and code

poolside/laguna-s-2.1

toolsreasoning
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Input · per 1M tokens
26,111 toman
Output · per 1M tokens
52,223 toman
One 1,000-word request ≈
102 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 2,611 toman / 1M.Pricing and top-ups

Laguna S 2.1 example: a multi-step problem with reasoning

The example is picked from this model's own capabilities. Drop in your key and it runs as is.

from openai import OpenAI

client = OpenAI(base_url="https://api.uttapen.ir/v1", api_key="sk-up-…")

resp = client.chat.completions.create(
    model="poolside/laguna-s-2.1",
    messages=[{"role": "user", "content": (
        "A shop has 3 warehouses. A ships 120 orders a day, B ships 85 and C ships 40. "
        "If C closes and its load is split between A and B in proportion to their capacity, how many does each ship a day? "
        "Work through it step by step and give just the two numbers at the end."
    )}],
    reasoning_effort="medium",   # low | medium | high
)
print(resp.choices[0].message.content)
# reasoning tokens count as output tokens too:
print(resp.usage.completion_tokens_details)

What is Laguna S 2.1 good for?

Laguna S 2.1 is one of Poolside's models. Put "poolside/laguna-s-2.1" in the model field and the rest of your code stays as it is. Laguna S 2.1 keeps 1,048,576 tokens in view at once, and that number is what caps the conversation history in your product. A single response can run to 131,072 tokens. On price it sits in the "very cheap" band — cheaper than 29 and dearer than 377 of the other paid models in the catalogue.

What you get on top of text in, text out: it supports tool calling, so it can invoke your own functions with valid arguments; it has a reasoning mode that pays off on multi-step problems, maths and debugging. All of it works through the standard parameters of the official OpenAI SDK — no custom client, no wrapper. Keep in mind that reasoning tokens are output tokens and do appear on the bill.

Input runs at 26,111 toman per 1M tokens and output at 52,223 — output costs 2× input, so trimming the answer saves more than trimming the prompt. It supports cached input: repeated context is billed at 2,611 toman per 1M, which matters a lot if your system prompt is long. You are always charged for the usage the request actually reported, never for the estimate, and a failed request costs nothing.

The closest alternative with the same capabilities from a different provider is Ling 3.0 Flash Fin: Laguna S 2.1 works out roughly 1.1× more expensive, and its context window is larger. Both run on the same key and the same code, so trying the other one is a single string change.

To make the figure concrete: 100,000 toman of credit buys roughly 980 thousand-word requests on Laguna S 2.1, and every 1,000 toman is about 9,804 words of round trip. A job with one million input tokens and one million output tokens comes to 78,334 toman in total. Filling this model's 1,048,576-token window costs 27,380 toman on the input side alone, which is the real reason to keep conversation history short.

This id gets confused with "poolside/laguna-s-2.1:free", because the underlying model is the same. The difference is the suffix: no suffix (the standard variant) against "free". The context windows differ too: 1,048,576 against 262,144 tokens. Maximum answer length differs as well: 131,072 against 32,768 tokens.

Poolside has 4 models in our catalogue; the cheapest is Laguna XS 2.1 at 68 toman per thousand words and the dearest Laguna S 2.1 at 102. It does not support response_format, top_p, seed, structured_outputs, which most models here do, so test before switching if your code relies on them. By context size the nearest option from another provider is DeepSeek V4 Flash 0423 at 1,048,576 tokens.

Where it makes sense: high-volume work such as classification, tagging and bulk summarising, logic puzzles and code review, agents that reach out to APIs and databases, analysing a long document or codebase in one request.

Provider's own description

Laguna S 2.1 is the latest coding agent model from [Poolside](<https://poolside.ai/>). Laguna S 2.1 is a 118B total parameter model with 8B active parameters, scoring 70.2% on Terminal-Bench 2.1 and...

Three real jobs, priced on this model

Each figure is derived from the prices above and moves when they do.

JobTokensCost
One chat turn with a medium history1,500 in + 400 out60 toman
Summarising a ten-page document4,000 in + 600 out136 toman
Classifying a thousand short rows120,000 in + 20,000 out4,178 toman

Other variants of this model

Same core model, different execution terms and different price. This page is the standard variant.

VariantModel idOutput / 1MContext
freepoolside/laguna-s-2.1:free0262,144

Put the variant's id verbatim in the model field; nothing else in your code changes.

Frequently asked

How do I call Laguna S 2.1 from Iran?
Sign up with your mobile number, top the wallet up in toman, create an API key, then in the official OpenAI SDK point base_url at https://api.uttapen.ir/v1 and set model to "poolside/laguna-s-2.1". Nothing else in your code changes.
What does Laguna S 2.1 cost in toman?
26,111 toman per 1M input tokens and 52,223 toman per 1M output tokens; a 1,000-word request is around 102 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Laguna S 2.1 take?
Up to 1,048,576 tokens per request, roughly 786k words. A single answer can reach 131,072 tokens.
Does Laguna S 2.1 support streaming and tool calling?
Streaming (stream=true) works on every model here. This one supports tool calling in the standard OpenAI shape.