Which is cheaper, Granite 4.0 Micro or Laguna XS 2.1?

Granite 4.0 Micro is the cheaper of the two: one 1,000-word request costs about 49 toman, about 1.4× cheaper than Laguna XS 2.1.

Which one has the larger context window?

Laguna XS 2.1, with 262,144 tokens.

Granite 4.0 Micro vs Laguna XS 2.1

Both sit behind the same key and the same code on uttapen — the only thing that changes is the model string, so you can run each of them against your own workload without touching anything else. On cost, Granite 4.0 Micro comes out about 1.4× cheaper.

Feature	Granite 4.0 Micro	Laguna XS 2.1
Provider	IBM Granite	Poolside
Model id	ibm-granite/granite-4.0-h-micro	poolside/laguna-xs-2.1
Context	131,000 tokens	262,144 tokens
Max output	117,900 tokens	32,768 tokens
Input / 1M tokens	4,932 toman	17,408 toman
Output / 1M tokens	32,494 toman	34,815 toman
≈ one 1,000-word request	49 toman	68 toman
Input caching	No	8,704 toman / 1M
Image input	No	No
File input	No	No
Tool calling	No	Yes
JSON output	Yes	No
Reasoning mode	No	Yes

Which one for what?

Granite 4.0 Micro

High-volume work where cost per call decides

49 toman per 1,000 words · Model page

Laguna XS 2.1

Multi-step problems, maths, and tracking down a bug
Whole documents or a codebase in a single request
Agents that call your own APIs and database
High-volume work where cost per call decides

68 toman per 1,000 words · Model page

Run both with the same code

Swap the model value between the two ids and send the same request twice; what each answer cost comes back in the X-Uttapen-Cost-Toman header.

curl https://api.uttapen.ir/v1/chat/completions \
  -H "Authorization: Bearer sk-up-…" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "ibm-granite/granite-4.0-h-micro",
    "messages": [{"role": "user", "content": "Introduce yourself in one sentence."}],
    "stream": true
  }'

from openai import OpenAI

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

stream = client.chat.completions.create(
    model="ibm-granite/granite-4.0-h-micro",
    messages=[{"role": "user", "content": "Introduce yourself in one sentence."}],
    stream=True,
)
for chunk in stream:
    print(chunk.choices[0].delta.content or "", end="", flush=True)

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.uttapen.ir/v1",
  apiKey: "sk-up-…",
});

const stream = await client.chat.completions.create({
  model: "ibm-granite/granite-4.0-h-micro",
  messages: [{ role: "user", content: "Introduce yourself in one sentence." }],
  stream: true,
});
for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content ?? "");
}

<?php
// composer require openai-php/client guzzlehttp/guzzle
$client = OpenAI::factory()
    ->withBaseUri('https://api.uttapen.ir/v1')
    ->withApiKey('sk-up-…')
    ->make();

$result = $client->chat()->create([
    'model' => 'ibm-granite/granite-4.0-h-micro',
    'messages' => [['role' => 'user', 'content' => 'Introduce yourself in one sentence.']],
]);
echo $result->choices[0]->message->content;

package main

import (
	"context"
	"fmt"

	"github.com/openai/openai-go"
	"github.com/openai/openai-go/option"
)

func main() {
	client := openai.NewClient(
		option.WithBaseURL("https://api.uttapen.ir/v1"),
		option.WithAPIKey("sk-up-…"),
	)
	resp, err := client.Chat.Completions.New(context.Background(), openai.ChatCompletionNewParams{
		Model:    "ibm-granite/granite-4.0-h-micro",
		Messages: []openai.ChatCompletionMessageParamUnion{openai.UserMessage("Introduce yourself in one sentence.")},
	})
	if err != nil {
		panic(err)
	}
	fmt.Println(resp.Choices[0].Message.Content)
}

model = "poolside/laguna-xs-2.1"

More comparisons: all pairs · model rankings · full catalogue

base_url = https://api.uttapen.ir/v1