Hy3 preview API: toman pricing and code
tencent/hy3-preview
You are billed for the usage the request actually reported. Prices follow the market. Cached input: 20,773 toman per 1M tokens.Pricing and top-ups
Hy3 preview example: tool calling
The example is picked from this model's own capabilities. Drop in your key and it runs as is.
from openai import OpenAI
import json
client = OpenAI(base_url="https://api.uttapen.ir/v1", api_key="sk-up-…")
tools = [{
"type": "function",
"function": {
"name": "check_stock",
"description": "Returns the stock level of a product",
"parameters": {
"type": "object",
"properties": {"sku": {"type": "string"}},
"required": ["sku"],
},
},
}]
messages = [{"role": "user", "content": "How many of the Nike NK-42 shoe are in stock?"}]
first = client.chat.completions.create(model="tencent/hy3-preview", messages=messages, tools=tools)
call = first.choices[0].message.tool_calls[0]
# you run the function yourself — the model never touches the database
args = json.loads(call.function.arguments)
result = {"sku": args["sku"], "qty": 7}
messages += [first.choices[0].message, {"role": "tool", "tool_call_id": call.id, "content": json.dumps(result)}]
final = client.chat.completions.create(model="tencent/hy3-preview", messages=messages, tools=tools)
print(final.choices[0].message.content)import OpenAI from "openai";
const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });
const tools = [{
type: "function",
function: {
name: "check_stock",
description: "Returns the stock level of a product",
parameters: { type: "object", properties: { sku: { type: "string" } }, required: ["sku"] },
},
}];
const messages = [{ role: "user", content: "How many of the Nike NK-42 shoe are in stock?" }];
const first = await client.chat.completions.create({ model: "tencent/hy3-preview", messages, tools });
const call = first.choices[0].message.tool_calls[0];
const args = JSON.parse(call.function.arguments);
const result = { sku: args.sku, qty: 7 };
messages.push(first.choices[0].message, { role: "tool", tool_call_id: call.id, content: JSON.stringify(result) });
const final = await client.chat.completions.create({ model: "tencent/hy3-preview", messages, tools });
console.log(final.choices[0].message.content);curl https://api.uttapen.ir/v1/chat/completions \
-H "Authorization: Bearer sk-up-…" \
-H "Content-Type: application/json" \
-d '{
"model": "tencent/hy3-preview",
"messages": [{"role": "user", "content": "How many of the Nike NK-42 shoe are in stock?"}],
"tools": [{
"type": "function",
"function": {
"name": "check_stock",
"parameters": {"type": "object", "properties": {"sku": {"type": "string"}}, "required": ["sku"]}
}
}]
}'What is Hy3 preview good for?
Hy3 preview comes from Tencent; in uttapen you reach it with the model id "tencent/
What you get on top of text in, text out: it supports tool calling, so it can invoke your own functions with valid arguments; 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 62,319 toman per 1M tokens and output at 207,732 — output costs 3.3× input, so trimming the answer saves more than trimming the prompt. It supports cached input: repeated context is billed at 20,773 toman per 1M, which matters a lot if your system prompt is long. 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 Command R (08-2024), and Hy3 preview costs about the same, 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 285 thousand-word requests on Hy3 preview, and every 1,000 toman is about 2,849 words of round trip. A job with one million input tokens and one million output tokens comes to 270,051 toman in total. Filling this model's 262,144-token window costs 16,337 toman on the input side alone, which is the real reason to keep conversation history short.
Tencent has 8 models in our catalogue; the cheapest is Hy-MT2-1.8B at 99 toman per thousand words and the dearest Hy4 preview at 1,351. Among the less common parameters it accepts reasoning_effort — all through the standard request body. It does not support response_format, structured_outputs, 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 Aion 3.5 at 262,144 tokens.
Where it makes sense: high-volume work such as classification, tagging and bulk summarising, logic puzzles and code review, agents that reach out to APIs and databases, analysing a long document or codebase in one request.
Hy3 preview is a high-efficiency Mixture-of-Experts model from Tencent designed for agentic workflows and production use. It supports configurable reasoning levels across disabled, low, and high modes, allowing it to...
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 | 177 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 374 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 11,633 toman |
Frequently asked
- How do I call Hy3 preview 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 "tencent/ hy3-preview". Nothing else in your code changes. - What does Hy3 preview cost in toman?
- 62,319 toman per 1M input tokens and 207,732 toman per 1M output tokens; a 1,000-word request is around 351 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Hy3 preview take?
- Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 235,929 tokens.
- Does Hy3 preview support streaming and tool calling?
- Streaming (stream=true) works on every model here. This one supports tool calling in the standard OpenAI shape.