Claude Sonnet 4 API: toman pricing and code
anthropic/claude-sonnet-4
visiontoolsreasoningfilesYou are billed for the usage the request actually reported. Prices follow the market. Cached input: 87,038 toman / 1M. Web search: 2,901.25 toman / request.Pricing and top-ups
Claude Sonnet 4 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": "anthropic/claude-sonnet-4",
"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="anthropic/claude-sonnet-4",
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: "anthropic/claude-sonnet-4",
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 Claude Sonnet 4 good for?
Anthropic publishes this model; we expose it under the id "anthropic/claude-sonnet-4". Its context window is 1,000,000 tokens, roughly 750k English words in one request. A single response can run to 64,000 tokens. On price it sits in the "expensive" band — cheaper than 359 and dearer than 47 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 takes files such as PDFs as 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.
Input runs at 870,375 toman per 1M tokens and output at 4,351,875 — output costs 5× input, so trimming the answer saves more than trimming the prompt. It supports cached input: repeated context is billed at 87,038 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 K3: Claude Sonnet 4 works out roughly 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 15 thousand-word requests on Claude Sonnet 4, and every 1,000 toman is about 147 words of round trip. A job with one million input tokens and one million output tokens comes to 5,222,250 toman in total. Filling this model's 1,000,000-token window costs 870,375 toman on the input side alone, which is the real reason to keep conversation history short.
Its nearest relative in the catalogue is "anthropic/claude-opus-4", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out 5× cheaper (6,789 against 33,945 toman). The context windows differ too: 1,000,000 against 200,000 tokens. Maximum answer length differs as well: 64,000 against 32,000 tokens. On parameters, this one takes top_k.
Anthropic has 31 models in our catalogue; the cheapest is Claude 3 Haiku at 566 toman per thousand words and the dearest Claude Opus 4.1 at 33,945. Among the less common parameters it accepts 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 Nova 2 Lite at 1,000,000 tokens.
Where it makes sense: work where the quality of the answer matters more than its cost, 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.
Claude Sonnet 4 significantly enhances the capabilities of its predecessor, Sonnet 3.7, excelling in both coding and reasoning tasks with improved precision and controllability. Achieving state-of-the-art performance on SWE-bench (72.7%),...
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 | 3,046 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 6,093 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 191,483 toman |
Frequently asked
- How do I call Claude Sonnet 4 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 "anthropic/claude-sonnet-4". Nothing else in your code changes.
- What does Claude Sonnet 4 cost in toman?
- 870,375 toman per 1M input tokens and 4,351,875 toman per 1M output tokens; a 1,000-word request is around 6,789 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Claude Sonnet 4 take?
- Up to 1,000,000 tokens per request, roughly 750k words. A single answer can reach 64,000 tokens.
- Does Claude Sonnet 4 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.