Inkling Small (batch) API: toman pricing and code
thinkingmachines/inkling-small:batch
visiontoolsreasoningaudioYou are billed for the usage the request actually reported. Prices follow the market. Cached input: 29,013 toman / 1M.Pricing and top-ups
Inkling Small (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-small: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-small: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-small: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 Small (batch) good for?
Inkling Small (batch) comes from Thinking Machines; in uttapen you reach it with the model id "thinkingmachines/inkling-small:batch". 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 "cheap" band — cheaper than 151 and dearer than 255 of the other paid models in the catalogue.
Capabilities available on this id: 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.
For a back-of-envelope figure: about 641 toman per 1,000-word exchange (145,063 in, 348,150 out, per 1M tokens). It supports cached input: repeated context is billed at 29,013 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 MiniMax M3: Inkling Small (batch) works out roughly 1.1× more expensive, and its context window is smaller. 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 156 thousand-word requests on Inkling Small (batch), and every 1,000 toman is about 1,560 words of round trip. A job with one million input tokens and one million output tokens comes to 493,213 toman in total. Filling this model's 524,288-token window costs 76,055 toman on the input side alone, which is the real reason to keep conversation history short.
This id gets confused with "thinkingmachines/inkling-small", 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 (641 and 622 toman). The context windows differ too: 524,288 against 1,048,576 tokens. Maximum answer length differs as well: 471,859 against 262,144 tokens. On parameters the other takes 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, logprobs, 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.
What to use it for: 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 | 357 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 789 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 24,371 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-small | 348,150 | 1,048,576 |
| free | thinkingmachines/inkling-small: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 Small (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-small:batch". Nothing else in your code changes.
- What does Inkling Small (batch) cost in toman?
- 145,063 toman per 1M input tokens and 348,150 toman per 1M output tokens; a 1,000-word request is around 641 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Inkling Small (batch) take?
- Up to 524,288 tokens per request, roughly 393k words. A single answer can reach 471,859 tokens.
- Does Inkling Small (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.