Which is cheaper, DeepSeek V4 Flash 0731 (batch) or Qwen3 32B?

Qwen3 32B is the cheaper of the two: one 1,000-word request costs about 136 toman, about 1.2× cheaper than DeepSeek V4 Flash 0731 (batch).

Which one has the larger context window?

DeepSeek V4 Flash 0731 (batch), with 1,048,576 tokens.

DeepSeek V4 Flash 0731 (batch) vs Qwen3 32B

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 32B comes out about 1.2× cheaper.

Feature	DeepSeek V4 Flash 0731 (batch)	Qwen3 32B
Provider	DeepSeek	Qwen (Alibaba)
Model id	deepseek/deepseek-v4-flash-0731:batch	qwen/qwen3-32b
Context	1,048,576 tokens	131,072 tokens
Max output	943,718 tokens	16,384 tokens
Input / 1M tokens	40,618 toman	23,210 toman
Output / 1M tokens	81,235 toman	81,235 toman
≈ one 1,000-word request	158 toman	136 toman
Input caching	8,704 toman / 1M	No
Image input	No	No
File input	No	No
Tool calling	Yes	Yes
JSON output	Yes	Yes
Reasoning mode	Yes	Yes

Which one for what?

DeepSeek V4 Flash 0731 (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
High-volume work where cost per call decides

158 toman per 1,000 words · Model page

Qwen3 32B

Multi-step problems, maths, and tracking down a bug
Agents that call your own APIs and database
High-volume work where cost per call decides

136 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": "deepseek/deepseek-v4-flash-0731: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="deepseek/deepseek-v4-flash-0731: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: "deepseek/deepseek-v4-flash-0731: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' => 'deepseek/deepseek-v4-flash-0731: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:    "deepseek/deepseek-v4-flash-0731:batch",
		Messages: []openai.ChatCompletionMessageParamUnion{openai.UserMessage("Introduce yourself in one sentence.")},
	})
	if err != nil {
		panic(err)
	}
	fmt.Println(resp.Choices[0].Message.Content)
}

model = "qwen/qwen3-32b"

More comparisons: all pairs · model rankings · full catalogue

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