Mercury 3 API: toman pricing and code
inception/mercury-3
You are billed for the usage the request actually reported. Prices follow the market. Cached input: 3,462 toman per 1M tokens.Pricing and top-ups
Mercury 3 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="inception/mercury-3",
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: "inception/mercury-3",
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": "inception/mercury-3",
"messages": [{"role": "user", "content": "Classify the ticket and give it a priority from 1 to 5."}],
"response_format": {"type": "json_object"}
}'What is Mercury 3 good for?
The id for Mercury 3 in our API is "inception/
What it can do beyond plain text: 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.
Pricing is 34,622 toman per 1M input tokens and 121,177 per 1M output tokens. A 1,000-word round trip on Mercury 3 lands near 203 toman. It supports cached input: repeated context is billed at 3,462 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 DeepSeek V3.2: Mercury 3 works out about 29% cheaper, 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 493 thousand-word requests on Mercury 3, and every 1,000 toman is about 4,926 words of round trip. A job with one million input tokens and one million output tokens comes to 155,799 toman in total. Filling this model's 1,000,000-token window costs 34,622 toman on the input side alone, which is the real reason to keep conversation history short.
Its nearest relative in the catalogue is "inception/
Inception has 3 models in our catalogue; the cheapest is Mercury 2.5 at 86 toman per thousand words and the dearest Mercury 2 at 450. Among the less common parameters it accepts reasoning_effort — all through the standard request body. It does not support top_p, 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 Nova 2 Lite at 1,000,000 tokens.
Good fits: high-volume work such as classification, tagging and bulk summarising, logic puzzles and code review, agents that reach out to APIs and databases, extracting data against a fixed schema, analysing a long document or codebase in one request.
Mercury 3 is a diffusion-based language model from Inception, and the lab's most capable model to date. It is suited for agentic workflows and real-time applications where latency compounds, such...
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 | 100 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 211 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 6,578 toman |
Frequently asked
- How do I call Mercury 3 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 "inception/ mercury-3". Nothing else in your code changes. - What does Mercury 3 cost in toman?
- 34,622 toman per 1M input tokens and 121,177 toman per 1M output tokens; a 1,000-word request is around 203 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Mercury 3 take?
- Up to 1,000,000 tokens per request, roughly 750k words. A single answer can reach 131,072 tokens.
- Does Mercury 3 support streaming and tool calling?
- Streaming (stream=true) works on every model here. This one supports tool calling in the standard OpenAI shape. Structured output through response_format works too.