Which is cheaper, Gemini 3.8 Flash or Gemini 3.6 Flash?

Gemini 3.8 Flash is the cheaper of the two: one 1,000-word request costs about 1,697 toman.

Which one has the larger context window?

Gemini 3.8 Flash, with 1,048,576 tokens.

Gemini 3.8 Flash vs Gemini 3.6 Flash

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	Gemini 3.8 Flash	Gemini 3.6 Flash
Provider	Google	Google
Model id	google/gemini-3.8-flash	google/gemini-3.6-flash
Context	1,048,576 tokens	1,048,576 tokens
Max output	65,536 tokens	65,536 tokens
Input / 1M tokens	217,594 toman	217,594 toman
Output / 1M tokens	1,087,969 toman	1,087,969 toman
≈ one 1,000-word request	1,697 toman	1,697 toman
Input caching	21,759 toman / 1M	21,759 toman / 1M
Image input	Yes	Yes
File input	Yes	Yes
Tool calling	Yes	Yes
JSON output	Yes	Yes
Reasoning mode	Yes	Yes

Which one for what?

Gemini 3.8 Flash

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

1,697 toman per 1,000 words · Model page

Gemini 3.6 Flash

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

1,697 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-3.8-flash",
    "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-3.8-flash",
    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-3.8-flash",
  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-3.8-flash',
    '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-3.8-flash",
		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-3.6-flash"

More comparisons: all pairs · model rankings · full catalogue

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