Inkling (batch) API pricing in toman

Inkling (batch) 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:batch",
    "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:batch",
    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:batch",
  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 (batch) good for?

The id for Inkling (batch) in our API is "thinkingmachines/inkling:batch", served from Thinking Machines. Context is 524,288 tokens; past that you have to summarise the history yourself. A single response can run to 471,859 tokens. On price it sits in the "mid-range" band — cheaper than 273 and dearer than 133 of the other paid models in the catalogue.

What it can do beyond plain text: 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.

Pricing is 290,125 toman per 1M input tokens and 1,175,006 per 1M output tokens. A 1,000-word round trip on Inkling (batch) lands near 1,905 toman. It supports cached input: repeated context is billed at 49,321 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 Kimi K2.6: Inkling (batch) works out roughly 1× more expensive, 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 52 thousand-word requests on Inkling (batch), and every 1,000 toman is about 525 words of round trip. A job with one million input tokens and one million output tokens comes to 1,465,131 toman in total. Filling this model's 524,288-token window costs 152,109 toman on the input side alone, which is the real reason to keep conversation history short.

This id gets confused with "thinkingmachines/inkling", because the underlying model is the same. The difference is the suffix: "batch" against the standard variant. Both land at nearly the same price on a thousand-word request (1,905 and 1,905 toman). The context windows differ too: 524,288 against 1,048,576 tokens. On parameters the other takes response_format, seed.

Thinking Machines has 6 models in our catalogue; the cheapest is Inkling Small at 622 toman per thousand words and the dearest Inkling (batch) at 1,905. Among the less common parameters it accepts logit_bias, min_p, 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 MiniMax M3 (batch) at 524,288 tokens.

Good fits: 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,...

Job	Tokens	Cost
One chat turn with a medium history	1,500 in + 400 out	905 toman
Summarising a ten-page document	4,000 in + 600 out	1,866 toman
Classifying a thousand short rows	120,000 in + 20,000 out	58,315 toman

Job

Tokens

Cost

One chat turn with a medium history

1,500 in + 400 out

905 toman

Summarising a ten-page document

4,000 in + 600 out

1,866 toman

Classifying a thousand short rows

120,000 in + 20,000 out

58,315 toman

Other variants of this model

Same core model, different execution terms and different price. This page is the batch (cheaper, slower) variant.

Variant	Model id	Output / 1M	Context
standard	thinkingmachines/inkling	1,175,006	1,048,576
free	thinkingmachines/inkling:free	0	1,048,576

Put the variant's id verbatim in the model field; nothing else in your code changes.

Frequently asked

How do I call Inkling (batch) 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:batch". Nothing else in your code changes.

What does Inkling (batch) cost in toman?

290,125 toman per 1M input tokens and 1,175,006 toman per 1M output tokens; a 1,000-word request is around 1,905 toman. You pay for the usage that request actually reported, and a failed request costs nothing.

How much input does Inkling (batch) take?

Up to 524,288 tokens per request, roughly 393k words. A single answer can reach 471,859 tokens.

Does Inkling (batch) 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.

Inkling (batch) API: toman pricing and code

Inkling (batch) example: reading an image into JSON

What is Inkling (batch) good for?

Three real jobs, priced on this model

Other variants of this model

Frequently asked