Which is cheaper, Gemini 3.1 Flash Lite (batch) or Gemma 2 27B?

Gemini 3.1 Flash Lite (batch) is the cheaper of the two: one 1,000-word request costs about 330 toman, about 1.5× cheaper than Gemma 2 27B.

Which one has the larger context window?

Gemini 3.1 Flash Lite (batch), with 1,048,576 tokens.

Gemini 3.1 Flash Lite (batch) vs Gemma 2 27B

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 3.1 Flash Lite (batch) comes out about 1.5× cheaper.

Feature	Gemini 3.1 Flash Lite (batch)	Gemma 2 27B
Provider	Google	Google
Model id	google/gemini-3.1-flash-lite:batch	google/gemma-2-27b-it
Context	1,048,576 tokens	8,192 tokens
Max output	65,536 tokens	2,048 tokens
Input / 1M tokens	36,266 toman	188,581 toman
Output / 1M tokens	217,594 toman	188,581 toman
≈ one 1,000-word request	330 toman	490 toman
Input caching	3,627 toman / 1M	No
Image input	Yes	No
File input	Yes	No
Tool calling	Yes	No
JSON output	Yes	Yes
Reasoning mode	Yes	No

Which one for what?

Gemini 3.1 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

330 toman per 1,000 words · Model page

Gemma 2 27B

High-volume work where cost per call decides

490 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": "google/gemini-3.1-flash-lite: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="google/gemini-3.1-flash-lite: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: "google/gemini-3.1-flash-lite: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' => 'google/gemini-3.1-flash-lite: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:    "google/gemini-3.1-flash-lite:batch",
		Messages: []openai.ChatCompletionMessageParamUnion{openai.UserMessage("Introduce yourself in one sentence.")},
	})
	if err != nil {
		panic(err)
	}
	fmt.Println(resp.Choices[0].Message.Content)
}

model = "google/gemma-2-27b-it"

More comparisons: all pairs · model rankings · full catalogue

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