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Ternary Bonsai 2 27B API: toman pricing and code

prism-ml/ternary-bonsai-2-27b

Image inputTool callingReasoningJSON output
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Input · per 1M tokens
25,966 toman
Output · per 1M tokens
173,110 toman
One 1,000-word request ≈
259 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 12,983 toman per 1M tokens.Pricing and top-ups

Ternary Bonsai 2 27B 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": "prism-ml/ternary-bonsai-2-27b",
    "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="prism-ml/ternary-bonsai-2-27b",
    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: "prism-ml/ternary-bonsai-2-27b",
  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 Ternary Bonsai 2 27B good for?

Ternary Bonsai 2 27B comes from Prism Ml; in uttapen you reach it with the model id "prism-ml/ternary-bonsai-2-27b". It accepts up to 262,144 tokens of input per call, so a long document or several source files fit in a single request. A single response can run to 32,768 tokens. On price it sits in the "very cheap" band — cheaper than 296 and dearer than 88 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 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 25,966 toman per 1M tokens and output at 173,110 — output costs 6.7× input, so trimming the answer saves more than trimming the prompt. It supports cached input: repeated context is billed at 12,983 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 Claude Haiku 5.5, and Ternary Bonsai 2 27B costs about the same, 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 386 thousand-word requests on Ternary Bonsai 2 27B, and every 1,000 toman is about 3,861 words of round trip. A job with one million input tokens and one million output tokens comes to 199,076 toman in total. Filling this model's 262,144-token window costs 6,807 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "prism-ml/ternary-bonsai-2-9b", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out about 2.7× more expensive (259 against 95 toman). Maximum answer length differs as well: 32,768 against 65,536 tokens. On parameters, this one takes reasoning_effort.

Prism Ml has 2 models in our catalogue; the cheapest is Ternary Bonsai 2 9B at 95 toman per thousand words and the dearest Ternary Bonsai 2 27B at 259. Among the less common parameters it accepts logprobs, presence_penalty, reasoning_effort, repetition_penalty, top_k, top_logprobs — all through the standard request body. By context size the nearest option from another provider is Aion 3.5 at 262,144 tokens.

Where it makes sense: 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, extracting data against a fixed schema, analysing a long document or codebase in one request.

Provider's own description

Bonsai 2 27B is a 27B-parameter reasoning model from PrismML derived from Qwen3.8-27B. It supports coding, mathematics, tool calling, and image understanding with a 262K-token context window. Ternary compression shrinks...

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 out108 toman
Summarising a ten-page document4,000 in + 600 out208 toman
Classifying a thousand short rows120,000 in + 20,000 out6,578 toman

Price history

DateInput (toman/1M)Output (toman/1M)
29 Sep 202625,966173,110
18 Sep 202625,966173,110

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

Frequently asked

How do I call Ternary Bonsai 2 27B 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 "prism-ml/ternary-bonsai-2-27b". Nothing else in your code changes.
What does Ternary Bonsai 2 27B cost in toman?
25,966 toman per 1M input tokens and 173,110 toman per 1M output tokens; a 1,000-word request is around 259 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Ternary Bonsai 2 27B take?
Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 32,768 tokens.
Does Ternary Bonsai 2 27B 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.