Hermes 4 405B API: toman pricing and code
nousresearch/hermes-4-405b
You are billed for the usage the request actually reported. Prices follow the market. Cached input: 69,244 toman per 1M tokens.Pricing and top-ups
Hermes 4 405B example: JSON output against a schema
The example is picked from this model's own capabilities. Drop in your key and it runs as is.
from openai import OpenAI
import json
client = OpenAI(base_url="https://api.uttapen.ir/v1", api_key="sk-up-…")
schema = {
"name": "ticket",
"schema": {
"type": "object",
"properties": {
"category": {"type": "string", "enum": ["fani", "mali", "forush"]},
"priority": {"type": "integer", "minimum": 1, "maximum": 5},
"summary": {"type": "string"},
},
"required": ["category", "priority", "summary"],
"additionalProperties": False,
},
"strict": True,
}
resp = client.chat.completions.create(
model="nousresearch/hermes-4-405b",
messages=[{"role": "user", "content": "Ticket: "For two days I cannot download my invoice and I was charged twice.""}],
response_format={"type": "json_schema", "json_schema": schema},
)
print(json.loads(resp.choices[0].message.content))import OpenAI from "openai";
const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });
const resp = await client.chat.completions.create({
model: "nousresearch/hermes-4-405b",
messages: [{ role: "user", content: "Ticket: "For two days I cannot download my invoice and I was charged twice."" }],
response_format: {
type: "json_schema",
json_schema: {
name: "ticket",
strict: true,
schema: {
type: "object",
properties: {
category: { type: "string", enum: ["fani", "mali", "forush"] },
priority: { type: "integer", minimum: 1, maximum: 5 },
summary: { type: "string" },
},
required: ["category", "priority", "summary"],
additionalProperties: false,
},
},
},
});
console.log(JSON.parse(resp.choices[0].message.content));curl https://api.uttapen.ir/v1/chat/completions \
-H "Authorization: Bearer sk-up-…" \
-H "Content-Type: application/json" \
-d '{
"model": "nousresearch/hermes-4-405b",
"messages": [{"role": "user", "content": "Classify the ticket and give it a priority from 1 to 5."}],
"response_format": {"type": "json_object"}
}'What is Hermes 4 405B good for?
Hermes 4 405B is one of Nous Research's models. Put "nousresearch/
What you get on top of text in, text out: 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 346,219 toman per 1M tokens and output at 1,038,658 — output costs 3× input, so trimming the answer saves more than trimming the prompt. It supports cached input: repeated context is billed at 69,244 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 Morph V3 Large: Hermes 4 405B works out about 40% 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 56 thousand-word requests on Hermes 4 405B, and every 1,000 toman is about 556 words of round trip. A job with one million input tokens and one million output tokens comes to 1,384,877 toman in total. Filling this model's 131,072-token window costs 45,380 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, presence_penalty, repetition_penalty, top_k, top_logprobs — all through the standard request body. It does not support tool_choice, tools, 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-2.0 at 131,072 tokens.
Where it makes sense: product chatbots and internal assistants, where cost and quality have to balance, logic puzzles and code review, extracting data against a fixed schema.
Hermes 4 is a large-scale reasoning model built on Meta-Llama-3.1-405B and released by Nous Research. It introduces a hybrid reasoning mode, where the model can choose to deliberate internally with...
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 | 935 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 2,008 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 62,319 toman |
Price history
| Date | Input (toman/1M) | Output (toman/1M) |
|---|---|---|
| 27 Oct 2026 | 346,219 | 1,038,658 |
| 7 Sep 2026 | 346,219 | 1,038,658 |
Every price change for this model is recorded. Toman figures use today's rate.
Frequently asked
- How do I call Hermes 4 405B 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 "nousresearch/ hermes-4-405b". Nothing else in your code changes. - What does Hermes 4 405B cost in toman?
- 346,219 toman per 1M input tokens and 1,038,658 toman per 1M output tokens; a 1,000-word request is around 1,800 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Hermes 4 405B take?
- Up to 131,072 tokens per request, roughly 98k words. A single answer can reach 117,964 tokens.
- Can I stream Hermes 4 405B's output?
- Yes — with stream=true you get SSE events as the tokens are produced. This model has no tool calling and no image input, so pick a different one if you need either.