Laguna S 2.1 vs Ling 3.0 Flash Fin
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.
| Feature |  Laguna S 2.1 | Ling 3.0 Flash Fin |
|---|---|---|
| Provider | Poolside | inclusionAI |
| Model id | poolside/laguna-s-2.1 | inclusionai/ling-3.0-flash-fin |
| Context | 1,048,576 tokens | 262,144 tokens |
| Max output | 131,072 tokens | 235,929 tokens |
| Input / 1M tokens | 26,111 toman | 17,408 toman |
| Output / 1M tokens | 52,223 toman | 52,223 toman |
| ≈ one 1,000-word request | 102 toman | 91 toman |
| Input caching | 2,611 toman / 1M | 3,482 toman / 1M |
| Image input | No | No |
| File input | No | No |
| Tool calling | Yes | Yes |
| JSON output | No | Yes |
| Reasoning mode | Yes | Yes |
Which one for what?
Laguna S 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
102 toman per 1,000 words · Model page
Ling 3.0 Flash Fin
- 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
91 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": "poolside/laguna-s-2.1",
"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="poolside/laguna-s-2.1",
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: "poolside/laguna-s-2.1",
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' => 'poolside/laguna-s-2.1',
'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: "poolside/laguna-s-2.1",
Messages: []openai.ChatCompletionMessageParamUnion{openai.UserMessage("Introduce yourself in one sentence.")},
})
if err != nil {
panic(err)
}
fmt.Println(resp.Choices[0].Message.Content)
}model = "inclusionai/ling-3.0-flash-fin"
More comparisons: all pairs · model rankings · full catalogue
base_url = https://api.uttapen.ir/v1