Inkling Small API: toman pricing and code
thinkingmachines/inkling-small
You are billed for the usage the request actually reported. Prices follow the market. Cached input: 34,622 toman per 1M tokens.Pricing and top-ups
Inkling Small 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-small",
"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-small",
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-small",
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 Small good for?
The id for Inkling Small in our API is "thinkingmachines/
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 155,799 toman per 1M input tokens and 415,463 per 1M output tokens. A 1,000-word round trip on Inkling Small lands near 743 toman. It supports cached input: repeated context is billed at 34,622 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 Muse Glimmer 30B: Inkling Small works out about 10% 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 135 thousand-word requests on Inkling Small, and every 1,000 toman is about 1,346 words of round trip. A job with one million input tokens and one million output tokens comes to 571,262 toman in total. Filling this model's 524,288-token window costs 81,683 toman on the input side alone, which is the real reason to keep conversation history short.
Its nearest relative in the catalogue is "thinkingmachines/
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, 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.
Good fits: high-volume work such as classification, tagging and bulk summarising, 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.
Inkling Small is an open-weight multimodal mixture-of-experts model from Thinking Machines Lab, with 12B active parameters out of 276B total. It is positioned as the smaller, more efficient member of...
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 | 400 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 872 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 27,005 toman |
Frequently asked
- How do I call Inkling Small 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-small". Nothing else in your code changes. - What does Inkling Small cost in toman?
- 155,799 toman per 1M input tokens and 415,463 toman per 1M output tokens; a 1,000-word request is around 743 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Inkling Small take?
- Up to 524,288 tokens per request, roughly 393k words. A single answer can reach 262,144 tokens.
- Does Inkling Small 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.