Which is cheaper, Morph V3 Large or Qwen3 VL 235B A22B Instruct?

Qwen3 VL 235B A22B Instruct is the cheaper of the two: one 1,000-word request costs about 796 toman, about 1.3× cheaper than Morph V3 Large.

Which one has the larger context window?

Morph V3 Large, with 262,144 tokens.

Morph V3 Large vs Qwen3 VL 235B A22B 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, Qwen3 VL 235B A22B Instruct comes out about 1.3× cheaper.

Feature	Morph V3 Large	Qwen3 VL 235B A22B Instruct
Provider	Morph	Qwen (Alibaba)
Model id	morph/morph-v3-large	qwen/qwen3-vl-235b-a22b-instruct
Context	262,144 tokens	262,144 tokens
Max output	131,072 tokens	32,768 tokens
Input / 1M tokens	261,113 toman	60,926 toman
Output / 1M tokens	551,238 toman	551,238 toman
≈ one 1,000-word request	1,056 toman	796 toman
Input caching	No	29,013 toman / 1M
Image input	No	Yes
File input	No	No
Tool calling	No	Yes
JSON output	Yes	Yes
Reasoning mode	No	No

Which one for what?

Morph V3 Large

Whole documents or a codebase in a single request

1,056 toman per 1,000 words · Model page

Qwen3 VL 235B A22B Instruct

Whole documents or a codebase in a single request
Reading screenshots, invoices, and scanned forms
Agents that call your own APIs and database

796 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": "morph/morph-v3-large",
    "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="morph/morph-v3-large",
    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: "morph/morph-v3-large",
  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' => 'morph/morph-v3-large',
    '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:    "morph/morph-v3-large",
		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-vl-235b-a22b-instruct"

More comparisons: all pairs · model rankings · full catalogue

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