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Inkling API: toman pricing and code

thinkingmachines/inkling

Image inputTool callingReasoningAudio input
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Input · per 1M tokens
346,219 toman
Output · per 1M tokens
1,402,188 toman
One 1,000-word request ≈
2,273 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 58,857 toman per 1M tokens.Pricing and top-ups

Inkling example: reading an image into JSON

The example is picked from this model's own capabilities. Drop in your key and it runs as is.

# tip: a data URI works too — base64 the file and prefix it with data:image/jpeg;base64,
curl https://api.uttapen.ir/v1/chat/completions \
  -H "Authorization: Bearer sk-up-…" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "thinkingmachines/inkling",
    "messages": [{
      "role": "user",
      "content": [
        {"type": "text", "text": "Return only the invoice number and the total, as JSON."},
        {"type": "image_url", "image_url": {"url": "https://example.com/factor.jpg"}}
      ]
    }]
  }'
import base64, json
from openai import OpenAI

client = OpenAI(base_url="https://api.uttapen.ir/v1", api_key="sk-up-…")

img = base64.b64encode(open("factor.jpg", "rb").read()).decode()

resp = client.chat.completions.create(
    model="thinkingmachines/inkling",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "Return the invoice number, the date and the total as JSON."},
            {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{img}"}},
        ],
    }],
    response_format={"type": "json_object"},
)
print(json.loads(resp.choices[0].message.content))
import OpenAI from "openai";
import { readFileSync } from "node:fs";

const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });
const img = readFileSync("factor.jpg").toString("base64");

const resp = await client.chat.completions.create({
  model: "thinkingmachines/inkling",
  messages: [{
    role: "user",
    content: [
      { type: "text", text: "Return the invoice number, the date and the total as JSON." },
      { type: "image_url", image_url: { url: `data:image/jpeg;base64,${img}` } },
    ],
  }],
  response_format: { type: "json_object" },
});
console.log(JSON.parse(resp.choices[0].message.content));

What is Inkling good for?

Inkling comes from Thinking Machines; in uttapen you reach it with the model id "thinkingmachines/inkling". It accepts up to 524,288 tokens of input per call, so a long document or several source files fit in a single request. A single response can run to 471,859 tokens. On price it sits in the "mid-range" band — cheaper than 131 and dearer than 267 of the other paid models in the catalogue.

What you get on top of text in, text out: it reads images directly, which makes it a real option for invoices, forms and screenshots; it accepts audio input; it supports tool calling, so it can invoke your own functions with valid arguments; 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 346,219 toman per 1M tokens and output at 1,402,188 — output costs 4.1× input, so trimming the answer saves more than trimming the prompt. It supports cached input: repeated context is billed at 58,857 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 GLM 5V Turbo, and Inkling 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 44 thousand-word requests on Inkling, and every 1,000 toman is about 440 words of round trip. A job with one million input tokens and one million output tokens comes to 1,748,408 toman in total. Filling this model's 524,288-token window costs 181,519 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "thinkingmachines/inkling-small", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out about 3.1× more expensive (2,273 against 743 toman). Maximum answer length differs as well: 471,859 against 262,144 tokens. On parameters the other takes seed.

Thinking Machines has 2 models in our catalogue; the cheapest is Inkling Small at 743 toman per thousand words and the dearest Inkling at 2,273. Among the less common parameters it accepts logit_bias, min_p, presence_penalty, reasoning_effort, repetition_penalty, top_k — all through the standard request body. It does not support response_format, seed, structured_outputs, 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 Llama 4 Maverick at 524,288 tokens.

Where it makes sense: product chatbots and internal assistants, where cost and quality have to balance, logic puzzles and code review, pulling text and fields out of images, agents that reach out to APIs and databases, analysing a long document or codebase in one request.

Provider's own description

Inkling is an open-weight multimodal mixture-of-experts model from Thinking Machines Lab, with 41B active parameters out of 975B total. It is designed for general-purpose reasoning, coding, agentic and tool-use systems,...

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 out1,080 toman
Summarising a ten-page document4,000 in + 600 out2,226 toman
Classifying a thousand short rows120,000 in + 20,000 out69,590 toman

Price history

DateInput (toman/1M)Output (toman/1M)
7 Oct 2026346,2191,402,188
2 Oct 2026328,9081,402,188
7 Sep 2026346,2191,402,188

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

Frequently asked

How do I call Inkling 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 "thinkingmachines/inkling". Nothing else in your code changes.
What does Inkling cost in toman?
346,219 toman per 1M input tokens and 1,402,188 toman per 1M output tokens; a 1,000-word request is around 2,273 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Inkling take?
Up to 524,288 tokens per request, roughly 393k words. A single answer can reach 471,859 tokens.
Does Inkling support streaming and tool calling?
Streaming (stream=true) works on every model here. This one supports tool calling in the standard OpenAI shape. Image input is accepted through image_url, as a data URI or a public URL.