Glimpse API: toman pricing and code

elorian/glimpse

Image inputTool callingReasoningJSON output
Input · per 1M tokens
207,732 toman
Output · per 1M tokens
1,038,658 toman
One 1,000-word request ≈
1,620 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 20,773 toman per 1M tokens.Pricing and top-ups

Glimpse 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": "elorian/glimpse",
    "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="elorian/glimpse",
    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: "elorian/glimpse",
  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 Glimpse good for?

Elorian publishes this model; we expose it under the id "elorian/glimpse". Its context window is 262,144 tokens, roughly 197k English words in one request. A single response can run to 256,000 tokens. On price it sits in the "mid-range" band — cheaper than 149 and dearer than 243 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 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.

Pricing is 207,732 toman per 1M input tokens and 1,038,658 per 1M output tokens. A 1,000-word round trip on Glimpse lands near 1,620 toman. It supports cached input: repeated context is billed at 20,773 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 Seed-2.0-Code, and Glimpse costs about the same, and its context window is the same size. 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 62 thousand-word requests on Glimpse, and every 1,000 toman is about 617 words of round trip. A job with one million input tokens and one million output tokens comes to 1,246,390 toman in total. Filling this model's 262,144-token window costs 54,456 toman on the input side alone, which is the real reason to keep conversation history short.

Among the less common parameters it accepts logit_bias, logprobs, min_p, presence_penalty, repetition_penalty, top_k — all through the standard request body. It does not support seed, 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 Aion 3.5 at 262,144 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, extracting data against a fixed schema, analysing a long document or codebase in one request.

Provider's own description

Glimpse is a multimodal visual reasoning model from Elorian, built for visual understanding and complex visual reasoning in the physical world. It reasons directly over images and video rather than...

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 out727 toman
Summarising a ten-page document4,000 in + 600 out1,454 toman
Classifying a thousand short rows120,000 in + 20,000 out45,701 toman

Frequently asked

How do I call Glimpse 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 "elorian/glimpse". Nothing else in your code changes.
What does Glimpse cost in toman?
207,732 toman per 1M input tokens and 1,038,658 toman per 1M output tokens; a 1,000-word request is around 1,620 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Glimpse take?
Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 256,000 tokens.
Does Glimpse 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.