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Qwen3 14B API: toman pricing and code

qwen/qwen3-14b

Tool callingReasoningJSON output
Create a keyTry it
Input · per 1M tokens
41,546 toman
Output · per 1M tokens
83,093 toman
One 1,000-word request ≈
162 toman

You are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups

Qwen3 14B 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="qwen/qwen3-14b",
    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": "qwen/qwen3-14b",
    "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: "qwen/qwen3-14b",
  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 Qwen3 14B good for?

Qwen3 14B is one of Qwen (Alibaba)'s models. Put "qwen/qwen3-14b" in the model field and the rest of your code stays as it is. Qwen3 14B keeps 40,960 tokens in view at once, and that number is what caps the conversation history in your product. A single response can run to 16,384 tokens. On price it sits in the "very cheap" band — cheaper than 349 and dearer than 47 of the other paid models in the catalogue.

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 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 41,546 toman per 1M tokens and output at 83,093 — output costs 2× input, so trimming the answer saves more than trimming the prompt. 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 Nemotron 3 Nano 30B A3B: Qwen3 14B works out about 20% more expensive, and its context window is smaller. 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 617 thousand-word requests on Qwen3 14B, and every 1,000 toman is about 6,173 words of round trip. A job with one million input tokens and one million output tokens comes to 124,639 toman in total. Filling this model's 40,960-token window costs 1,702 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "qwen/qwen3-30b-a3b", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out about 41% cheaper (162 against 279 toman). On parameters, this one takes logprobs, top_logprobs.

Qwen (Alibaba) has 42 models in our catalogue; the cheapest is Qwen3.7 Flash at 72 toman per thousand words and the dearest Qwen3.8 Max Prime at 7,201. Among the less common parameters it accepts logit_bias, logprobs, min_p, presence_penalty, repetition_penalty, top_k — all through the standard request body. By context size the nearest option from another provider is Perceptron Mk1.5 at 36,864 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, extracting data against a fixed schema.

Provider's own description

Qwen3-14B is a dense 14.8B parameter causal language model from the Qwen3 series, designed for both complex reasoning and efficient dialogue. It supports seamless switching between a "thinking" mode for...

Three real jobs, priced on this model

Each figure is derived from the prices above and moves when they do.

JobTokensCost
One chat turn with a medium history1,500 in + 400 out96 toman
Summarising a ten-page document4,000 in + 600 out216 toman
Classifying a thousand short rows120,000 in + 20,000 out6,647 toman

Price history

DateInput (toman/1M)Output (toman/1M)
11 Oct 202641,54683,093
11 Oct 202634,62276,168
10 Oct 202641,54683,093
10 Oct 202634,62276,168
28 Sep 202641,54683,093
28 Sep 202678,765315,060
14 Sep 202641,54683,093
8 Sep 202678,765315,060
7 Sep 202641,54683,093

Every price change for this model is recorded. Toman figures use today's rate.

Frequently asked

How do I call Qwen3 14B 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 "qwen/qwen3-14b". Nothing else in your code changes.
What does Qwen3 14B cost in toman?
41,546 toman per 1M input tokens and 83,093 toman per 1M output tokens; a 1,000-word request is around 162 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Qwen3 14B take?
Up to 40,960 tokens per request, roughly 31k words. A single answer can reach 16,384 tokens.
Does Qwen3 14B 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.