Hy4 preview API pricing in toman

Hy4 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/hy4-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/hy4-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/hy4-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/hy4-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/hy4-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 Hy4 preview good for?

Hy4 preview is one of Tencent's models. Put "tencent/hy4-preview" in the model field and the rest of your code stays as it is. Hy4 preview keeps 1,048,576 tokens in view at once, and that number is what caps the conversation history in your product. A single response can run to 64,000 tokens. On price it sits in the "mid-range" band — cheaper than 240 and dearer than 166 of the other paid models in the catalogue.

Capabilities available on this id: 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 1,258 toman per 1,000-word exchange (241,964 in, 725,603 out, per 1M tokens). It supports cached input: repeated context is billed at 12,185 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 R1: Hy4 preview works out roughly 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 79 thousand-word requests on Hy4 preview, and every 1,000 toman is about 795 words of round trip. A job with one million input tokens and one million output tokens comes to 967,567 toman in total. Filling this model's 1,048,576-token window costs 253,718 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "tencent/hy3", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out 5.1× dearer (1,258 against 249 toman). The context windows differ too: 1,048,576 against 262,144 tokens. Maximum answer length differs as well: 64,000 against 128,000 tokens. On parameters the other takes frequency_penalty, logit_bias, min_p, presence_penalty.

Tencent has 7 models in our catalogue; the cheapest is Hy-MT2-1.8B at 83 toman per thousand words and the dearest Hy4 preview at 1,258. Among the less common parameters it accepts max_completion_tokens, reasoning_effort — all through the standard request body. It does not support top_p, 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 DeepSeek V4 Flash 0423 at 1,048,576 tokens.

What to use it for: product chatbots and internal assistants, where cost and quality have to balance, logic puzzles and code review, agents that reach out to APIs and databases, extracting data against a fixed schema, analysing a long document or codebase in one request.

Provider's own description

Tencent: Hy4 preview is a mixture-of-experts model from Tencent, with 49B active parameters out of 770B total. It is designed for coding agents, complex tool-use workflows, and productivity tasks that...

Job	Tokens	Cost
One chat turn with a medium history	1,500 in + 400 out	653 toman
Summarising a ten-page document	4,000 in + 600 out	1,403 toman
Classifying a thousand short rows	120,000 in + 20,000 out	43,548 toman

Job

Tokens

Cost

One chat turn with a medium history

1,500 in + 400 out

653 toman

Summarising a ten-page document

4,000 in + 600 out

1,403 toman

Classifying a thousand short rows

120,000 in + 20,000 out

43,548 toman

Frequently asked

How do I call Hy4 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/hy4-preview". Nothing else in your code changes.

What does Hy4 preview cost in toman?

241,964 toman per 1M input tokens and 725,603 toman per 1M output tokens; a 1,000-word request is around 1,258 toman. You pay for the usage that request actually reported, and a failed request costs nothing.

How much input does Hy4 preview take?

Up to 1,048,576 tokens per request, roughly 786k words. A single answer can reach 64,000 tokens.

Does Hy4 preview support streaming and tool calling?

Streaming (stream=true) works on every model here. This one supports tool calling in the standard OpenAI shape. Structured output through response_format works too.

Hy4 preview API: toman pricing and code

Hy4 preview example: tool calling

What is Hy4 preview good for?

Three real jobs, priced on this model

Frequently asked