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Ling 3.0 Tiny API: toman pricing and code

inclusionai/ling-3.0-tiny

Tool callingReasoningJSON output
Create a keyTry it
Input · per 1M tokens
5,193 toman
Output · per 1M tokens
15,580 toman
One 1,000-word request ≈
27 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 1,039 toman per 1M tokens.Pricing and top-ups

Ling 3.0 Tiny 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="inclusionai/ling-3.0-tiny", 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="inclusionai/ling-3.0-tiny", 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: "inclusionai/ling-3.0-tiny", 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: "inclusionai/ling-3.0-tiny", 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": "inclusionai/ling-3.0-tiny",
    "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 Ling 3.0 Tiny good for?

inclusionAI publishes this model; we expose it under the id "inclusionai/ling-3.0-tiny". Its context window is 262,144 tokens, roughly 197k English words in one request. A single response can run to 250,000 tokens. On price it sits in the "very cheap" band — cheaper than 392 and dearer than 6 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 27 toman per 1,000-word exchange (5,193 in, 15,580 out, per 1M tokens). It supports cached input: repeated context is billed at 1,039 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 Mistral Nemo, and Ling 3.0 Tiny 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 3,704 thousand-word requests on Ling 3.0 Tiny, and every 1,000 toman is about 37,037 words of round trip. A job with one million input tokens and one million output tokens comes to 20,773 toman in total. Filling this model's 262,144-token window costs 1,361 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "inclusionai/ling-3.0-flash", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out about 29% cheaper (27 against 38 toman). Maximum answer length differs as well: 250,000 against 32,768 tokens. On parameters, this one takes structured_outputs while the other takes logit_bias, min_p, repetition_penalty, top_k.

inclusionAI has 4 models in our catalogue; the cheapest is Ling 3.0 Tiny at 27 toman per thousand words and the dearest Ling 3.1 Flash at 135. Among the less common parameters it accepts logprobs, presence_penalty, top_logprobs — all through the standard request body. By context size the nearest option from another provider is Aion 3.5 at 262,144 tokens.

What to use it for: 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, analysing a long document or codebase in one request.

Provider's own description

Ling 3.0 Tiny is a mixture-of-experts model from InclusionAI, with 1.3B active parameters out of 7.9B total. It is designed for responsive agents, instruction following, and multi-turn conversations, with switchable...

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 out14 toman
Summarising a ten-page document4,000 in + 600 out30 toman
Classifying a thousand short rows120,000 in + 20,000 out935 toman

Frequently asked

How do I call Ling 3.0 Tiny 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 "inclusionai/ling-3.0-tiny". Nothing else in your code changes.
What does Ling 3.0 Tiny cost in toman?
5,193 toman per 1M input tokens and 15,580 toman per 1M output tokens; a 1,000-word request is around 27 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Ling 3.0 Tiny take?
Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 250,000 tokens.
Does Ling 3.0 Tiny 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.