Which is cheaper, GPT-4 or GPT-5 Pro (batch)?

GPT-5 Pro (batch) is the cheaper of the two: one 1,000-word request costs about 25,458 toman, about 1.3× cheaper than GPT-4.

Which one has the larger context window?

GPT-5 Pro (batch), with 400,000 tokens.

GPT-4 vs GPT-5 Pro (batch)

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, GPT-5 Pro (batch) comes out about 1.3× cheaper.

Feature	GPT-4	GPT-5 Pro (batch)
Provider	OpenAI	OpenAI
Model id	openai/gpt-4	openai/gpt-5-pro:batch
Context	8,191 tokens	400,000 tokens
Max output	4,096 tokens	128,000 tokens
Input / 1M tokens	8,703,750 toman	2,175,938 toman
Output / 1M tokens	17,407,500 toman	17,407,500 toman
≈ one 1,000-word request	33,945 toman	25,458 toman
Input caching	No	No
Image input	No	Yes
File input	No	Yes
Tool calling	Yes	Yes
JSON output	Yes	Yes
Reasoning mode	No	Yes

Which one for what?

GPT-4

Agents that call your own APIs and database

33,945 toman per 1,000 words · Model page

GPT-5 Pro (batch)

Multi-step problems, maths, and tracking down a bug
Whole documents or a codebase in a single request
Reading screenshots, invoices, and scanned forms
Agents that call your own APIs and database

25,458 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": "openai/gpt-4",
    "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="openai/gpt-4",
    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: "openai/gpt-4",
  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' => 'openai/gpt-4',
    '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:    "openai/gpt-4",
		Messages: []openai.ChatCompletionMessageParamUnion{openai.UserMessage("Introduce yourself in one sentence.")},
	})
	if err != nil {
		panic(err)
	}
	fmt.Println(resp.Choices[0].Message.Content)
}

model = "openai/gpt-5-pro:batch"

More comparisons: all pairs · model rankings · full catalogue

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