Which is cheaper, Qwen2.5 7B Instruct or Qwen3 30B A3B Instruct 2507?

Qwen3 30B A3B Instruct 2507 is the cheaper of the two: one 1,000-word request costs about 91 toman, about 1.2× cheaper than Qwen2.5 7B Instruct.

Which one has the larger context window?

Qwen3 30B A3B Instruct 2507, with 262,144 tokens.

Qwen2.5 7B Instruct vs Qwen3 30B A3B Instruct 2507

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, Qwen3 30B A3B Instruct 2507 comes out about 1.2× cheaper.

Feature	Qwen2.5 7B Instruct	Qwen3 30B A3B Instruct 2507
Provider	Qwen (Alibaba)	Qwen (Alibaba)
Model id	qwen/qwen-2.5-7b-instruct	qwen/qwen3-30b-a3b-instruct-2507
Context	32,768 tokens	262,144 tokens
Max output	29,491 tokens	32,000 tokens
Input / 1M tokens	29,013 toman	13,970 toman
Output / 1M tokens	58,025 toman	56,009 toman
≈ one 1,000-word request	113 toman	91 toman
Input caching	No	No
Image input	No	No
File input	No	No
Tool calling	Yes	Yes
JSON output	Yes	Yes
Reasoning mode	No	No

Which one for what?

Qwen2.5 7B Instruct

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

113 toman per 1,000 words · Model page

Qwen3 30B A3B Instruct 2507

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

91 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": "qwen/qwen-2.5-7b-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="qwen/qwen-2.5-7b-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: "qwen/qwen-2.5-7b-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' => 'qwen/qwen-2.5-7b-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:    "qwen/qwen-2.5-7b-instruct",
		Messages: []openai.ChatCompletionMessageParamUnion{openai.UserMessage("Introduce yourself in one sentence.")},
	})
	if err != nil {
		panic(err)
	}
	fmt.Println(resp.Choices[0].Message.Content)
}

model = "qwen/qwen3-30b-a3b-instruct-2507"

More comparisons: all pairs · model rankings · full catalogue

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