Claude Haiku 4.5 (batch) API: toman pricing and code
anthropic/claude-haiku-4.5:batch
visiontoolsreasoningjsonfilesYou are billed for the usage the request actually reported. Prices follow the market. Cached input: 14,506 toman / 1M. Web search: 2,901.25 toman / request.Pricing and top-ups
Claude Haiku 4.5 (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": "anthropic/claude-haiku-4.5: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="anthropic/claude-haiku-4.5: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: "anthropic/claude-haiku-4.5: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 Claude Haiku 4.5 (batch) good for?
Anthropic publishes this model; we expose it under the id "anthropic/claude-haiku-4.5:batch". Its context window is 200,000 tokens, roughly 150k English words in one request. A single response can run to 64,000 tokens. On price it sits in the "mid-range" band — cheaper than 228 and dearer than 178 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 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 145,063 toman per 1M tokens and output at 725,313 — output costs 5× input, so trimming the answer saves more than trimming the prompt. It supports cached input: repeated context is billed at 14,506 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 Nova 2 Lite: Claude Haiku 4.5 (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 88 thousand-word requests on Claude Haiku 4.5 (batch), and every 1,000 toman is about 884 words of round trip. A job with one million input tokens and one million output tokens comes to 870,375 toman in total. Filling this model's 200,000-token window costs 29,013 toman on the input side alone, which is the real reason to keep conversation history short.
This id gets confused with "anthropic/claude-haiku-4.5", because the underlying model is the same. The difference is the suffix: "batch" against the standard variant. On a thousand-word request this variant works out 2× cheaper (1,131 against 2,263 toman). On parameters the other takes max_completion_tokens.
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 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 o1 at 200,000 tokens.
Where it makes sense: 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.
Claude Haiku 4.5 is Anthropic’s fastest and most efficient model, delivering near-frontier intelligence at a fraction of the cost and latency of larger Claude models. Matching Claude Sonnet 4’s performance...
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 | 508 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 1,015 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 31,914 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 | anthropic/claude-haiku-4.5 | 1,450,625 | 200,000 |
Put the variant's id verbatim in the model field; nothing else in your code changes.
Frequently asked
- How do I call Claude Haiku 4.5 (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 "anthropic/claude-haiku-4.5:batch". Nothing else in your code changes.
- What does Claude Haiku 4.5 (batch) cost in toman?
- 145,063 toman per 1M input tokens and 725,313 toman per 1M output tokens; a 1,000-word request is around 1,131 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Claude Haiku 4.5 (batch) take?
- Up to 200,000 tokens per request, roughly 150k words. A single answer can reach 64,000 tokens.
- Does Claude Haiku 4.5 (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. Structured output through response_format works too.