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Ling 3.1 Flash API: toman pricing and code

inclusionai/ling-3.1-flash

Tool callingReasoningJSON output
Create a keyTry it
Input · per 1M tokens
25,966 toman
Output · per 1M tokens
77,899 toman
One 1,000-word request ≈
135 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 5,193 toman per 1M tokens.Pricing and top-ups

Ling 3.1 Flash 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-3.1-flash", 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-3.1-flash", 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-3.1-flash", 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-3.1-flash", 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-3.1-flash",
    "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 3.1 Flash good for?

The id for Ling 3.1 Flash in our API is "inclusionai/ling-3.1-flash", served from inclusionAI. Context is 1,000,000 tokens; past that you have to summarise the history yourself. A single response can run to 262,144 tokens. On price it sits in the "very cheap" band — cheaper than 351 and dearer than 45 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 135 toman per 1,000-word exchange (25,966 in, 77,899 out, per 1M tokens). It supports cached input: repeated context is billed at 5,193 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 Nemotron 3 Nano 30B A3B, and Ling 3.1 Flash 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 741 thousand-word requests on Ling 3.1 Flash, and every 1,000 toman is about 7,407 words of round trip. A job with one million input tokens and one million output tokens comes to 103,866 toman in total. Filling this model's 1,000,000-token window costs 25,966 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 3.6× more expensive (135 against 38 toman). The context windows differ too: 1,000,000 against 262,144 tokens. Maximum answer length differs as well: 262,144 against 32,768 tokens. On parameters, this one takes structured_outputs.

inclusionAI has 3 models in our catalogue; the cheapest is Ling 3.0 Flash at 38 toman per thousand words and the dearest Ling 3.1 Flash at 135. 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 Nova 2 Lite at 1,000,000 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 3.1 Flash is a hybrid reasoning mixture-of-experts model from inclusionAI, with 25B active parameters out of 560B total.

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 out70 toman
Summarising a ten-page document4,000 in + 600 out151 toman
Classifying a thousand short rows120,000 in + 20,000 out4,674 toman

Price history

DateInput (toman/1M)Output (toman/1M)
13 Oct 202625,96677,899
2 Oct 202600

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

Frequently asked

How do I call Ling 3.1 Flash 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-3.1-flash". Nothing else in your code changes.
What does Ling 3.1 Flash cost in toman?
25,966 toman per 1M input tokens and 77,899 toman per 1M output tokens; a 1,000-word request is around 135 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Ling 3.1 Flash take?
Up to 1,000,000 tokens per request, roughly 750k words. A single answer can reach 262,144 tokens.
Does Ling 3.1 Flash 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.