Which is cheaper, Hy3 or Qwen3 235B A22B Instruct 2507?

Qwen3 235B A22B Instruct 2507 is the cheaper of the two: one 1,000-word request costs about 241 toman.

Hy3 vs Qwen3 235B A22B 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.

Feature	Hy3	Qwen3 235B A22B Instruct 2507
Provider	Tencent	Qwen (Alibaba)
Model id	tencent/hy3	qwen/qwen3-235b-a22b-2507
Context	262,144 tokens	262,144 tokens
Max output	128,000 tokens	16,384 tokens
Input / 1M tokens	38,297 toman	26,111 toman
Output / 1M tokens	153,186 toman	159,569 toman
≈ one 1,000-word request	249 toman	241 toman
Input caching	9,574 toman / 1M	No
Image input	No	No
File input	No	No
Tool calling	Yes	Yes
JSON output	Yes	Yes
Reasoning mode	Yes	No

Which one for what?

Hy3

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

249 toman per 1,000 words · Model page

Qwen3 235B A22B 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

241 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": "tencent/hy3",
    "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="tencent/hy3",
    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: "tencent/hy3",
  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' => 'tencent/hy3',
    '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:    "tencent/hy3",
		Messages: []openai.ChatCompletionMessageParamUnion{openai.UserMessage("Introduce yourself in one sentence.")},
	})
	if err != nil {
		panic(err)
	}
	fmt.Println(resp.Choices[0].Message.Content)
}

model = "qwen/qwen3-235b-a22b-2507"

More comparisons: all pairs · model rankings · full catalogue

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