Which is cheaper, Nemotron 3 Nano 30B A3B or UI-TARS 7B ?

Nemotron 3 Nano 30B A3B 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?

Nemotron 3 Nano 30B A3B, with 262,144 tokens.

Nemotron 3 Nano 30B A3B vs UI-TARS 7B

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, Nemotron 3 Nano 30B A3B comes out about 1.2× cheaper.

Feature	Nemotron 3 Nano 30B A3B	UI-TARS 7B
Provider	NVIDIA	ByteDance
Model id	nvidia/nemotron-3-nano-30b-a3b	bytedance/ui-tars-1.5-7b
Context	262,144 tokens	128,000 tokens
Max output	235,929 tokens	2,048 tokens
Input / 1M tokens	14,506 toman	29,013 toman
Output / 1M tokens	58,025 toman	58,025 toman
≈ one 1,000-word request	94 toman	113 toman
Input caching	8,704 toman / 1M	29,013 toman / 1M
Image input	No	Yes
File input	No	No
Tool calling	Yes	No
JSON output	Yes	Yes
Reasoning mode	Yes	No

Which one for what?

Nemotron 3 Nano 30B A3B

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

94 toman per 1,000 words · Model page

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

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": "nvidia/nemotron-3-nano-30b-a3b",
    "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="nvidia/nemotron-3-nano-30b-a3b",
    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: "nvidia/nemotron-3-nano-30b-a3b",
  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' => 'nvidia/nemotron-3-nano-30b-a3b',
    '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:    "nvidia/nemotron-3-nano-30b-a3b",
		Messages: []openai.ChatCompletionMessageParamUnion{openai.UserMessage("Introduce yourself in one sentence.")},
	})
	if err != nil {
		panic(err)
	}
	fmt.Println(resp.Choices[0].Message.Content)
}

model = "bytedance/ui-tars-1.5-7b"

More comparisons: all pairs · model rankings · full catalogue

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