Create an API key

Morph V3 Fast API: toman pricing and code

morph/morph-v3-fast

JSON output
Create a keyTry it
Input · per 1M tokens
276,975 toman
Output · per 1M tokens
415,463 toman
One 1,000-word request ≈
900 toman

You are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups

Morph V3 Fast 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-fast",
    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-fast",
  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-fast",
    "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 Fast good for?

Morph V3 Fast is one of Morph's models. Put "morph/morph-v3-fast" in the model field and the rest of your code stays as it is. Morph V3 Fast keeps 262,144 tokens in view at once, and that number is what caps the conversation history in your product. A single response can run to 131,072 tokens. On price it sits in the "cheap" band — cheaper than 232 and dearer than 155 of the other paid models in the catalogue.

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 276,975 toman per 1M tokens and output at 415,463 — output costs 1.5× 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 MiniMax M2-her: Morph V3 Fast works out about 30% 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 111 thousand-word requests on Morph V3 Fast, and every 1,000 toman is about 1,111 words of round trip. A job with one million input tokens and one million output tokens comes to 692,439 toman in total. Filling this model's 262,144-token window costs 72,607 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-v3-large", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out about 29% cheaper (900 against 1,260 toman).

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.

Provider's own description

Morph's fastest apply model for code edits. ~10,500 tokens/sec with 96% accuracy for rapid code transformations. The model requires the prompt to be in the following format: <instruction>{instruction}</instruction> <code>{initial_code}</code> <update>{edit_snippet}</update>...

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 out582 toman
Summarising a ten-page document4,000 in + 600 out1,357 toman
Classifying a thousand short rows120,000 in + 20,000 out41,546 toman

Frequently asked

How do I call Morph V3 Fast 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-fast". Nothing else in your code changes.
What does Morph V3 Fast cost in toman?
276,975 toman per 1M input tokens and 415,463 toman per 1M output tokens; a 1,000-word request is around 900 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Morph V3 Fast take?
Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 131,072 tokens.
Can I stream Morph V3 Fast'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.