Which is cheaper, Llama 3.1 70B Instruct or Llama 3.2 3B Instruct?

Llama 3.2 3B Instruct is the cheaper of the two: one 1,000-word request costs about 143 toman, about 2.1× cheaper than Llama 3.1 70B Instruct.

Which one has the larger context window?

Llama 3.1 70B Instruct, with 131,072 tokens.

Llama 3.1 70B Instruct vs Llama 3.2 3B Instruct

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, Llama 3.2 3B Instruct comes out about 2.1× cheaper.

Feature	Llama 3.1 70B Instruct	Llama 3.2 3B Instruct
Provider	Meta	Meta
Model id	meta-llama/llama-3.1-70b-instruct	meta-llama/llama-3.2-3b-instruct
Context	131,072 tokens	131,072 tokens
Max output	16,384 tokens	117,964 tokens
Input / 1M tokens	116,050 toman	14,506 toman
Output / 1M tokens	116,050 toman	95,741 toman
≈ one 1,000-word request	302 toman	143 toman
Input caching	No	No
Image input	No	No
File input	No	No
Tool calling	Yes	No
JSON output	Yes	Yes
Reasoning mode	No	No

Which one for what?

Llama 3.1 70B Instruct

Agents that call your own APIs and database
High-volume work where cost per call decides

302 toman per 1,000 words · Model page

Llama 3.2 3B Instruct

High-volume work where cost per call decides

143 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-3.1-70b-instruct",
    "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-3.1-70b-instruct",
    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-3.1-70b-instruct",
  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-3.1-70b-instruct',
    '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-3.1-70b-instruct",
		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-3.2-3b-instruct"

More comparisons: all pairs · model rankings · full catalogue

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