Which is cheaper, Llama Guard 4 12B or Ling 3.0 Flash Fin?

Ling 3.0 Flash Fin is the cheaper of the two: one 1,000-word request costs about 91 toman, about 1.5× cheaper than Llama Guard 4 12B.

Which one has the larger context window?

Ling 3.0 Flash Fin, with 262,144 tokens.

Llama Guard 4 12B 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. On cost, Ling 3.0 Flash Fin comes out about 1.5× cheaper.

Feature	Llama Guard 4 12B	Ling 3.0 Flash Fin
Provider	Meta	inclusionAI
Model id	meta-llama/llama-guard-4-12b	inclusionai/ling-3.0-flash-fin
Context	163,840 tokens	262,144 tokens
Max output	16,384 tokens	235,929 tokens
Input / 1M tokens	52,223 toman	17,408 toman
Output / 1M tokens	52,223 toman	52,223 toman
≈ one 1,000-word request	136 toman	91 toman
Input caching	No	3,482 toman / 1M
Image input	Yes	No
File input	No	No
Tool calling	No	Yes
JSON output	Yes	Yes
Reasoning mode	No	Yes

Which one for what?

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

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": "meta-llama/llama-guard-4-12b",
    "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="meta-llama/llama-guard-4-12b",
    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: "meta-llama/llama-guard-4-12b",
  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' => 'meta-llama/llama-guard-4-12b',
    '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:    "meta-llama/llama-guard-4-12b",
		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

Llama Guard 4 12B vs Ling 3.0 Flash Fin

Which one for what?

Llama Guard 4 12B

ILing 3.0 Flash Fin

Run both with the same code

Ling 3.0 Flash Fin