Gemini 3.8 Flash (batch) API: toman pricing and code
google/gemini-3.8-flash:batch
visiontoolsreasoningjsonfilesaudioYou are billed for the usage the request actually reported. Prices follow the market. Cached input: 10,880 toman / 1M. Reasoning tokens: 543,984 toman / 1M. Per input image: 0.11 toman. Web search: 4,061.75 toman / request.Pricing and top-ups
Gemini 3.8 Flash (batch) example: reading an image into JSON
The example is picked from this model's own capabilities. Drop in your key and it runs as is.
# tip: a data URI works too — base64 the file and prefix it with data:image/jpeg;base64,
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:batch",
"messages": [{
"role": "user",
"content": [
{"type": "text", "text": "Return only the invoice number and the total, as JSON."},
{"type": "image_url", "image_url": {"url": "https://example.com/factor.jpg"}}
]
}]
}'import base64, json
from openai import OpenAI
client = OpenAI(base_url="https://api.uttapen.ir/v1", api_key="sk-up-…")
img = base64.b64encode(open("factor.jpg", "rb").read()).decode()
resp = client.chat.completions.create(
model="google/gemini-3.8-flash:batch",
messages=[{
"role": "user",
"content": [
{"type": "text", "text": "Return the invoice number, the date and the total as JSON."},
{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{img}"}},
],
}],
response_format={"type": "json_object"},
)
print(json.loads(resp.choices[0].message.content))import OpenAI from "openai";
import { readFileSync } from "node:fs";
const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });
const img = readFileSync("factor.jpg").toString("base64");
const resp = await client.chat.completions.create({
model: "google/gemini-3.8-flash:batch",
messages: [{
role: "user",
content: [
{ type: "text", text: "Return the invoice number, the date and the total as JSON." },
{ type: "image_url", image_url: { url: `data:image/jpeg;base64,${img}` } },
],
}],
response_format: { type: "json_object" },
});
console.log(JSON.parse(resp.choices[0].message.content));What is Gemini 3.8 Flash (batch) good for?
Google publishes this model; we expose it under the id "google/gemini-3.8-flash:batch". Its context window is 1,048,576 tokens, roughly 786k English words in one request. A single response can run to 65,536 tokens. On price it sits in the "cheap" band — cheaper than 192 and dearer than 214 of the other paid models in the catalogue.
Capabilities available on this id: it reads images directly, which makes it a real option for invoices, forms and screenshots; it takes files such as PDFs as input; it accepts audio input; it supports tool calling, so it can invoke your own functions with valid arguments; it returns schema-valid JSON through response_format, ready to hand to your code; it has a reasoning mode that pays off on multi-step problems, maths and debugging. All of it works through the standard parameters of the official OpenAI SDK — no custom client, no wrapper. Keep in mind that reasoning tokens are output tokens and do appear on the bill.
For a back-of-envelope figure: about 849 toman per 1,000-word exchange (108,797 in, 543,984 out, per 1M tokens). It supports cached input: repeated context is billed at 10,880 toman per 1M, which matters a lot if your system prompt is long. Each input image is billed separately at about 0.11 toman. You are always charged for the usage the request actually reported, never for the estimate, and a failed request costs nothing.
The closest alternative with the same capabilities from a different provider is Qwen3 VL 235B A22B Instruct: Gemini 3.8 Flash (batch) works out roughly 1.1× more expensive, and its context window is larger. Both run on the same key and the same code, so trying the other one is a single string change.
To make the figure concrete: 100,000 toman of credit buys roughly 118 thousand-word requests on Gemini 3.8 Flash (batch), and every 1,000 toman is about 1,178 words of round trip. A job with one million input tokens and one million output tokens comes to 652,781 toman in total. Filling this model's 1,048,576-token window costs 114,082 toman on the input side alone, which is the real reason to keep conversation history short.
This id gets confused with "google/gemini-3.8-flash", because the underlying model is the same. The difference is the suffix: "batch" against the standard variant. On a thousand-word request this variant works out 2× cheaper (849 against 1,697 toman). On parameters the other takes temperature, top_p.
Google has 45 models in our catalogue; the cheapest is Gemma 3 4B at 57 toman per thousand words and the dearest Google Gemini Pro Latest at 5,280. Among the less common parameters it accepts reasoning_effort — all through the standard request body. It does not support temperature, top_p, which most models here do, so test before switching if your code relies on them. By context size the nearest option from another provider is DeepSeek V4 Flash 0423 at 1,048,576 tokens.
What to use it for: high-volume work such as classification, tagging and bulk summarising, logic puzzles and code review, pulling text and fields out of images, agents that reach out to APIs and databases, extracting data against a fixed schema, analysing a long document or codebase in one request.
Gemini 3.8 Flash is Google's most intelligent Flash model with significant gains from 3.7 Flash across software engineering, agentic tasks, and multi-step reasoning.
Three real jobs, priced on this model
Each figure is derived from the prices above and moves when they do.
| Job | Tokens | Cost |
|---|---|---|
| One chat turn with a medium history | 1,500 in + 400 out | 381 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 762 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 23,935 toman |
Other variants of this model
Same core model, different execution terms and different price. This page is the batch (cheaper, slower) variant.
| Variant | Model id | Output / 1M | Context |
|---|---|---|---|
| standard | google/gemini-3.8-flash | 1,087,969 | 1,048,576 |
Put the variant's id verbatim in the model field; nothing else in your code changes.
Frequently asked
- How do I call Gemini 3.8 Flash (batch) from Iran?
- Sign up with your mobile number, top the wallet up in toman, create an API key, then in the official OpenAI SDK point base_url at https://api.uttapen.ir/v1 and set model to "google/gemini-3.8-flash:batch". Nothing else in your code changes.
- What does Gemini 3.8 Flash (batch) cost in toman?
- 108,797 toman per 1M input tokens and 543,984 toman per 1M output tokens; a 1,000-word request is around 849 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Gemini 3.8 Flash (batch) take?
- Up to 1,048,576 tokens per request, roughly 786k words. A single answer can reach 65,536 tokens.
- Does Gemini 3.8 Flash (batch) support streaming and tool calling?
- Streaming (stream=true) works on every model here. This one supports tool calling in the standard OpenAI shape. Image input is accepted through image_url, as a data URI or a public URL. Structured output through response_format works too.