Which is cheaper, GPT-3.5 Turbo (older v0613) or GPT-3.5 Turbo Instruct?

GPT-3.5 Turbo (older v0613) is the cheaper of the two: one 1,000-word request costs about 1,131 toman, about 1.2× cheaper than GPT-3.5 Turbo Instruct.

Which one has the larger context window?

GPT-3.5 Turbo (older v0613), with 4,095 tokens.

GPT-3.5 Turbo (older v0613) vs GPT-3.5 Turbo 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, GPT-3.5 Turbo (older v0613) comes out about 1.2× cheaper.

Feature	GPT-3.5 Turbo (older v0613)	GPT-3.5 Turbo Instruct
Provider	OpenAI	OpenAI
Model id	openai/gpt-3.5-turbo-0613	openai/gpt-3.5-turbo-instruct
Context	4,095 tokens	4,095 tokens
Max output	3,685 tokens	3,685 tokens
Input / 1M tokens	290,125 toman	435,188 toman
Output / 1M tokens	580,250 toman	580,250 toman
≈ one 1,000-word request	1,131 toman	1,320 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?

GPT-3.5 Turbo (older v0613)

Agents that call your own APIs and database

1,131 toman per 1,000 words · Model page

GPT-3.5 Turbo Instruct

Chat, summarising, and everyday text generation

1,320 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-3.5-turbo-0613",
    "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-3.5-turbo-0613",
    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-3.5-turbo-0613",
  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-3.5-turbo-0613',
    '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-3.5-turbo-0613",
		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-3.5-turbo-instruct"

More comparisons: all pairs · model rankings · full catalogue

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