Which is cheaper, Llama 4 Maverick or Llama 3.1 70B Instruct?

Llama 3.1 70B Instruct is the cheaper of the two: one 1,000-word request costs about 302 toman.

Which one has the larger context window?

Llama 4 Maverick, with 1,048,576 tokens.

Llama 4 Maverick vs Llama 3.1 70B 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.

Feature	Llama 4 Maverick	Llama 3.1 70B Instruct
Provider	Meta	Meta
Model id	meta-llama/llama-4-maverick	meta-llama/llama-3.1-70b-instruct
Context	1,048,576 tokens	131,072 tokens
Max output	115,200 tokens	16,384 tokens
Input / 1M tokens	58,025 toman	116,050 toman
Output / 1M tokens	201,927 toman	116,050 toman
≈ one 1,000-word request	338 toman	302 toman
Input caching	No	No
Image input	Yes	No
File input	No	No
Tool calling	Yes	Yes
JSON output	Yes	Yes
Reasoning mode	No	No

Which one for what?

Llama 4 Maverick

Whole documents or a codebase in a single request
Reading screenshots, invoices, and scanned forms
Agents that call your own APIs and database
High-volume work where cost per call decides

338 toman per 1,000 words · Model page

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

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-4-maverick",
    "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-4-maverick",
    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-4-maverick",
  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-4-maverick',
    '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-4-maverick",
		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-3.1-70b-instruct"

More comparisons: all pairs · model rankings · full catalogue

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