Seed 1.6 API: toman pricing and code
bytedance-seed/seed-1.6
visiontoolsreasoningjsonYou are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups
Seed 1.6 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": "bytedance-seed/seed-1.6",
"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="bytedance-seed/seed-1.6",
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: "bytedance-seed/seed-1.6",
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 Seed 1.6 good for?
The id for Seed 1.6 in our API is "bytedance-seed/seed-1.6", served from ByteDance Seed. Context is 262,144 tokens; past that you have to summarise the history yourself. A single response can run to 32,768 tokens. On price it sits in the "cheap" band — cheaper than 200 and dearer than 206 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 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 (72,531 in, 580,250 out, per 1M tokens). 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 Mistral Medium 3: Seed 1.6 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 118 thousand-word requests on Seed 1.6, 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 262,144-token window costs 19,014 toman on the input side alone, which is the real reason to keep conversation history short.
Its nearest relative in the catalogue is "bytedance-seed/seed-2.0-lite", and choosing between those two is where most people hesitate. Both land at nearly the same price on a thousand-word request (849 and 849 toman). Maximum answer length differs as well: 32,768 against 131,072 tokens. On parameters the other takes reasoning_effort.
ByteDance Seed has 6 models in our catalogue; the cheapest is Seed 1.6 Flash at 141 toman per thousand words and the dearest Seed-2.0-Code at 1,320. It does not support seed, 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 Trinity Large Thinking at 262,144 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.
Seed 1.6 is a general-purpose model released by the ByteDance Seed team. It incorporates multimodal capabilities and adaptive deep thinking with a 256K context window.
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 | 341 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 638 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 20,309 toman |
Frequently asked
- How do I call Seed 1.6 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 "bytedance-seed/seed-1.6". Nothing else in your code changes.
- What does Seed 1.6 cost in toman?
- 72,531 toman per 1M input tokens and 580,250 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 Seed 1.6 take?
- Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 32,768 tokens.
- Does Seed 1.6 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.