Gemini 3.5 Flash API: toman pricing and code
google/gemini-3.5-flash
visiontoolsreasoningjsonfilesaudioYou are billed for the usage the request actually reported. Prices follow the market. Cached input: 43,519 toman / 1M. Reasoning tokens: 2,611,125 toman / 1M. Per input image: 0.44 toman. Web search: 4,061.75 toman / request.Pricing and top-ups
Gemini 3.5 Flash 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-3.5-flash",
"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-3.5-flash",
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-3.5-flash",
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 3.5 Flash good for?
The id for Gemini 3.5 Flash in our API is "google/gemini-3.5-flash", served from Google. Context is 1,048,576 tokens; past that you have to summarise the history yourself. A single response can run to 65,536 tokens. On price it sits in the "mid-range" band — cheaper than 321 and dearer than 85 of the other paid models in the catalogue.
What you get on top of text in, text out: 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.
Input runs at 435,188 toman per 1M tokens and output at 2,611,125 — output costs 6× input, so trimming the answer saves more than trimming the prompt. It supports cached input: repeated context is billed at 43,519 toman per 1M, which matters a lot if your system prompt is long. Each input image is billed separately at about 0.44 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 Claude Sonnet 5: Gemini 3.5 Flash works out roughly 1.1× cheaper, 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 25 thousand-word requests on Gemini 3.5 Flash, and every 1,000 toman is about 253 words of round trip. A job with one million input tokens and one million output tokens comes to 3,046,313 toman in total. Filling this model's 1,048,576-token window costs 456,327 toman on the input side alone, which is the real reason to keep conversation history short.
This id gets confused with "google/gemini-3.5-flash:batch", because the underlying model is the same. The difference is the suffix: no suffix (the standard variant) against "batch". On a thousand-word request this variant works out 2× dearer (3,960 against 1,980 toman).
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. By context size the nearest option from another provider is DeepSeek V4 Flash 0423 at 1,048,576 tokens.
Where it makes sense: product chatbots and internal assistants, where cost and quality have to balance, 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 3.5 Flash is Google's high-efficiency multimodal model, bringing near-Pro level coding and reasoning at Flash-tier cost and speed. It is highly optimized for coding proficiency and parallel agentic execution...
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 | 1,697 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 3,307 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 104,445 toman |
Other variants of this model
Same core model, different execution terms and different price. This page is the standard variant.
| Variant | Model id | Output / 1M | Context |
|---|---|---|---|
| batch (cheaper, slower) | google/gemini-3.5-flash:batch | 1,305,563 | 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 3.5 Flash 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-3.5-flash". Nothing else in your code changes.
- What does Gemini 3.5 Flash cost in toman?
- 435,188 toman per 1M input tokens and 2,611,125 toman per 1M output tokens; a 1,000-word request is around 3,960 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Gemini 3.5 Flash take?
- Up to 1,048,576 tokens per request, roughly 786k words. A single answer can reach 65,536 tokens.
- Does Gemini 3.5 Flash 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.