Morph V3 Large API: toman pricing and code
morph/morph-v3-large
You are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups
Morph V3 Large 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="morph/morph-v3-large",
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: "morph/morph-v3-large",
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": "morph/morph-v3-large",
"messages": [{"role": "user", "content": "Classify the ticket and give it a priority from 1 to 5."}],
"response_format": {"type": "json_object"}
}'What is Morph V3 Large good for?
The id for Morph V3 Large in our API is "morph/
What you get on top of text in, text out: it returns schema-valid JSON through response_format, ready to hand to your code. All of it works through the standard parameters of the official OpenAI SDK — no custom client, no wrapper.
Input runs at 311,597 toman per 1M tokens and output at 657,817 — output costs 2.1× input, so trimming the answer saves more than trimming the prompt. 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 Aion-RP 1.0 (8B): Morph V3 Large works out about 20% more expensive, and its context window is larger. 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 79 thousand-word requests on Morph V3 Large, and every 1,000 toman is about 794 words of round trip. A job with one million input tokens and one million output tokens comes to 969,414 toman in total. Filling this model's 262,144-token window costs 81,683 toman on the input side alone, which is the real reason to keep conversation history short.
Its nearest relative in the catalogue is "morph/
Morph has 2 models in our catalogue; the cheapest is Morph V3 Fast at 900 toman per thousand words and the dearest Morph V3 Large at 1,260. Among the less common parameters it accepts logprobs, top_logprobs — all through the standard request body. It does not support include_reasoning, reasoning, 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 3.5 at 262,144 tokens.
Where it makes sense: high-volume work such as classification, tagging and bulk summarising, extracting data against a fixed schema, analysing a long document or codebase in one request.
Morph's high-accuracy apply model for complex code edits. ~4,500 tokens/sec with 98% accuracy for precise code transformations. The model requires the prompt to be in the following format: <instruction>{instruction}</instruction> <code>{initial_code}</code>...
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 | 731 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 1,641 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 50,548 toman |
Frequently asked
- How do I call Morph V3 Large 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 "morph/ morph-v3-large". Nothing else in your code changes. - What does Morph V3 Large cost in toman?
- 311,597 toman per 1M input tokens and 657,817 toman per 1M output tokens; a 1,000-word request is around 1,260 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Morph V3 Large take?
- Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 131,072 tokens.
- Can I stream Morph V3 Large'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.