Which is cheaper, o3 Mini (batch) or o4 Mini (batch)?

o3 Mini (batch) is the cheaper of the two: one 1,000-word request costs about 1,037 toman.

Which one has the larger context window?

o3 Mini (batch), with 200,000 tokens.

o3 Mini (batch) vs o4 Mini (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.

Feature	o3 Mini (batch)	o4 Mini (batch)
Provider	OpenAI	OpenAI
Model id	openai/o3-mini:batch	openai/o4-mini:batch
Context	200,000 tokens	200,000 tokens
Max output	100,000 tokens	100,000 tokens
Input / 1M tokens	159,569 toman	159,569 toman
Output / 1M tokens	638,275 toman	638,275 toman
≈ one 1,000-word request	1,037 toman	1,037 toman
Input caching	79,784 toman / 1M	39,892 toman / 1M
Image input	No	Yes
File input	Yes	Yes
Tool calling	Yes	Yes
JSON output	Yes	Yes
Reasoning mode	Yes	Yes

Which one for what?

o3 Mini (batch)

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

1,037 toman per 1,000 words · Model page

o4 Mini (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

1,037 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/o3-mini:batch",
    "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/o3-mini:batch",
    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/o3-mini:batch",
  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/o3-mini:batch',
    '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/o3-mini:batch",
		Messages: []openai.ChatCompletionMessageParamUnion{openai.UserMessage("Introduce yourself in one sentence.")},
	})
	if err != nil {
		panic(err)
	}
	fmt.Println(resp.Choices[0].Message.Content)
}

model = "openai/o4-mini:batch"

More comparisons: all pairs · model rankings · full catalogue

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