Hunyuan A13B Instruct API: toman pricing and code
tencent/hunyuan-a13b-instruct
reasoningjsonYou are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups
Hunyuan A13B Instruct example: a multi-step problem with reasoning
The example is picked from this model's own capabilities. Drop in your key and it runs as is.
from openai import OpenAI
client = OpenAI(base_url="https://api.uttapen.ir/v1", api_key="sk-up-…")
resp = client.chat.completions.create(
model="tencent/hunyuan-a13b-instruct",
messages=[{"role": "user", "content": (
"A shop has 3 warehouses. A ships 120 orders a day, B ships 85 and C ships 40. "
"If C closes and its load is split between A and B in proportion to their capacity, how many does each ship a day? "
"Work through it step by step and give just the two numbers at the end."
)}],
reasoning_effort="medium", # low | medium | high
)
print(resp.choices[0].message.content)
# reasoning tokens count as output tokens too:
print(resp.usage.completion_tokens_details)curl https://api.uttapen.ir/v1/chat/completions \
-H "Authorization: Bearer sk-up-…" \
-H "Content-Type: application/json" \
-d '{
"model": "tencent/hunyuan-a13b-instruct",
"messages": [{"role": "user", "content": "120 and 85 daily orders split across two warehouses; work through it step by step."}],
"reasoning": {"effort": "medium"}
}'import OpenAI from "openai";
const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });
const resp = await client.chat.completions.create({
model: "tencent/hunyuan-a13b-instruct",
messages: [{ role: "user", content: "120 and 85 daily orders are split between two warehouses; work through it step by step and give just the two numbers at the end." }],
reasoning_effort: "medium",
});
console.log(resp.choices[0].message.content);
console.log(resp.usage.completion_tokens_details);What is Hunyuan A13B Instruct good for?
The id for Hunyuan A13B Instruct in our API is "tencent/hunyuan-a13b-instruct", served from Tencent. Context is 131,072 tokens; past that you have to summarise the history yourself. A single response can run to 117,964 tokens. On price it sits in the "very cheap" band — cheaper than 94 and dearer than 312 of the other paid models in the catalogue.
What it can do beyond plain text: 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.
Pricing is 40,618 toman per 1M input tokens and 165,371 per 1M output tokens. A 1,000-word round trip on Hunyuan A13B Instruct lands near 268 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 WizardLM-2 8x22B: Hunyuan A13B Instruct works out roughly 1.7× 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 373 thousand-word requests on Hunyuan A13B Instruct, and every 1,000 toman is about 3,731 words of round trip. A job with one million input tokens and one million output tokens comes to 205,989 toman in total. Filling this model's 131,072-token window costs 5,324 toman on the input side alone, which is the real reason to keep conversation history short.
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 top_k — all through the standard request body. It does not support tool_choice, tools, 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 Aion-2.0 at 131,072 tokens.
Good fits: high-volume work such as classification, tagging and bulk summarising, logic puzzles and code review, extracting data against a fixed schema.
Hunyuan-A13B is a 13B active parameter Mixture-of-Experts (MoE) language model developed by Tencent, with a total parameter count of 80B and support for reasoning via Chain-of-Thought. It offers competitive benchmark...
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 | 127 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 262 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 8,182 toman |
Frequently asked
- How do I call Hunyuan A13B Instruct 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/hunyuan-a13b-instruct". Nothing else in your code changes.
- What does Hunyuan A13B Instruct cost in toman?
- 40,618 toman per 1M input tokens and 165,371 toman per 1M output tokens; a 1,000-word request is around 268 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Hunyuan A13B Instruct take?
- Up to 131,072 tokens per request, roughly 98k words. A single answer can reach 117,964 tokens.
- Can I stream Hunyuan A13B Instruct's output?
- Yes — with stream=true you get SSE events as the tokens are produced. This model has no tool calling and no image input, so pick a different one if you need either.