Ling 3.0 Flash Fin vs Llama Guard 4 12B
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, Ling 3.0 Flash Fin comes out about 1.5× cheaper.
| Feature | Ling 3.0 Flash Fin |  Llama Guard 4 12B |
|---|---|---|
| Provider | inclusionAI | Meta |
| Model id | inclusionai/ling-3.0-flash-fin | meta-llama/llama-guard-4-12b |
| Context | 262,144 tokens | 163,840 tokens |
| Max output | 235,929 tokens | 16,384 tokens |
| Input / 1M tokens | 17,408 toman | 52,223 toman |
| Output / 1M tokens | 52,223 toman | 52,223 toman |
| ≈ one 1,000-word request | 91 toman | 136 toman |
| Input caching | 3,482 toman / 1M | No |
| Image input | No | Yes |
| File input | No | No |
| Tool calling | Yes | No |
| JSON output | Yes | Yes |
| Reasoning mode | Yes | No |
Which one for what?
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
Llama Guard 4 12B
- Reading screenshots, invoices, and scanned forms
- High-volume work where cost per call decides
136 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": "inclusionai/ling-3.0-flash-fin",
"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="inclusionai/ling-3.0-flash-fin",
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: "inclusionai/ling-3.0-flash-fin",
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' => 'inclusionai/ling-3.0-flash-fin',
'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: "inclusionai/ling-3.0-flash-fin",
Messages: []openai.ChatCompletionMessageParamUnion{openai.UserMessage("Introduce yourself in one sentence.")},
})
if err != nil {
panic(err)
}
fmt.Println(resp.Choices[0].Message.Content)
}model = "meta-llama/llama-guard-4-12b"
More comparisons: all pairs · model rankings · full catalogue
base_url = https://api.uttapen.ir/v1