Which is cheaper, UI-TARS 7B or Gemini 2.5 Flash Lite (batch)?

Gemini 2.5 Flash Lite (batch) is the cheaper of the two: one 1,000-word request costs about 94 toman, about 1.2× cheaper than UI-TARS 7B .

Which one has the larger context window?

Gemini 2.5 Flash Lite (batch), with 1,048,576 tokens.

UI-TARS 7B vs Gemini 2.5 Flash Lite (batch)

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, Gemini 2.5 Flash Lite (batch) comes out about 1.2× cheaper.

Feature	UI-TARS 7B	Gemini 2.5 Flash Lite (batch)
Provider	ByteDance	Google
Model id	bytedance/ui-tars-1.5-7b	google/gemini-2.5-flash-lite:batch
Context	128,000 tokens	1,048,576 tokens
Max output	2,048 tokens	65,535 tokens
Input / 1M tokens	29,013 toman	14,506 toman
Output / 1M tokens	58,025 toman	58,025 toman
≈ one 1,000-word request	113 toman	94 toman
Input caching	29,013 toman / 1M	2,901 toman / 1M
Image input	Yes	Yes
File input	No	Yes
Tool calling	No	Yes
JSON output	Yes	Yes
Reasoning mode	No	Yes

Which one for what?

UI-TARS 7B

Reading screenshots, invoices, and scanned forms
High-volume work where cost per call decides

113 toman per 1,000 words · Model page

Gemini 2.5 Flash Lite (batch)

Multi-step problems, maths, and tracking down a bug
Whole documents or a codebase in a single request
Reading screenshots, invoices, and scanned forms
Agents that call your own APIs and database
High-volume work where cost per call decides

94 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": "bytedance/ui-tars-1.5-7b",
    "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="bytedance/ui-tars-1.5-7b",
    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: "bytedance/ui-tars-1.5-7b",
  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' => 'bytedance/ui-tars-1.5-7b',
    '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:    "bytedance/ui-tars-1.5-7b",
		Messages: []openai.ChatCompletionMessageParamUnion{openai.UserMessage("Introduce yourself in one sentence.")},
	})
	if err != nil {
		panic(err)
	}
	fmt.Println(resp.Choices[0].Message.Content)
}

model = "google/gemini-2.5-flash-lite:batch"

More comparisons: all pairs · model rankings · full catalogue

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