Inkling API: toman pricing and code
thinkingmachines/inkling
visiontoolsreasoningjsonaudioYou are billed for the usage the request actually reported. Prices follow the market. Cached input: 49,321 toman / 1M.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 1,048,576 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 273 and dearer than 133 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 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 290,125 toman per 1M tokens and output at 1,175,006 — output costs 4.1× input, so trimming the answer saves more than trimming the prompt. 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 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, 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 1,048,576-token window costs 304,218 toman on the input side alone, which is the real reason to keep conversation history short.
This id gets confused with "thinkingmachines/inkling:batch", because the underlying model is the same. The difference is the suffix: no suffix (the standard variant) against "batch". Both land at nearly the same price on a thousand-word request (1,905 and 1,905 toman). The context windows differ too: 1,048,576 against 524,288 tokens. On parameters, this one 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 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 DeepSeek V4 Flash 0423 at 1,048,576 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, extracting data against a fixed schema, analysing a long document or codebase in one request.
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.
| 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 standard variant.
| Variant | Model id | Output / 1M | Context |
|---|---|---|---|
| batch (cheaper, slower) | thinkingmachines/inkling:batch | 1,175,006 | 524,288 |
| 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 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?
- 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 take?
- Up to 1,048,576 tokens per request, roughly 786k 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. Structured output through response_format works too.