Morph V3 Fast API: toman pricing and code
morph/morph-v3-fast
You are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups
Morph V3 Fast example: translation between Persian and English
The example is picked from this model's own capabilities. Drop in your key and it runs as is.
from openai import OpenAI
client = OpenAI(base_url="https://api.uttapen.ir/v1", api_key="sk-up-…")
stream = client.chat.completions.create(
model="morph/morph-v3-fast",
messages=[{"role": "user", "content": "Translate into fluent Persian: \"Your order is confirmed and ships within 48 hours.\""}],
stream=True,
)
for chunk in stream:
print(chunk.choices[0].delta.content or "", end="", flush=True)import OpenAI from "openai";
const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });
const stream = await client.chat.completions.create({
model: "morph/morph-v3-fast",
messages: [{ role: "user", content: "Translate into fluent Persian: \"Your order is confirmed and ships within 48 hours.\"" }],
stream: true,
});
for await (const chunk of stream) process.stdout.write(chunk.choices[0]?.delta?.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": "Translate into fluent Persian: \"Your order is confirmed and ships within 48 hours.\""}],
"stream": true
}'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 81,920 tokens in view at once, and that number is what caps the conversation history in your product. A single response can run to 38,000 tokens. On price it sits in the "cheap" band — cheaper than 151 and dearer than 255 of the other paid models in the catalogue.
This one handles text only. If you need function calling or image understanding, pick another model from the same catalogue — swapping is a one-line change.
Input runs at 232,100 toman per 1M tokens and output at 348,150 — 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 roughly 1.3× 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 133 thousand-word requests on Morph V3 Fast, and every 1,000 toman is about 1,326 words of round trip. A job with one million input tokens and one million output tokens comes to 580,250 toman in total. Filling this model's 81,920-token window costs 19,014 toman on the input side alone, which is the real reason to keep conversation history short.
Morph has 2 models in our catalogue; the cheapest is Morph V3 Fast at 754 toman per thousand words and the dearest Morph V3 Large at 1,056. It does not support include_reasoning, reasoning, response_format, tool_choice, 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 Qwen3 30B A3B Thinking 2507 at 81,920 tokens.
Where it makes sense: high-volume work such as classification, tagging and bulk summarising.
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.
| Job | Tokens | Cost |
|---|---|---|
| One chat turn with a medium history | 1,500 in + 400 out | 487 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 1,137 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 34,815 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?
- 232,100 toman per 1M input tokens and 348,150 toman per 1M output tokens; a 1,000-word request is around 754 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 81,920 tokens per request, roughly 61k words. A single answer can reach 38,000 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.