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Mercury 2.5 API: toman pricing and code

inception/mercury-2.5

Tool callingReasoningJSON output
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Input · per 1M tokens
13,849 toman
Output · per 1M tokens
51,933 toman
One 1,000-word request ≈
86 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 1,385 toman per 1M tokens.Pricing and top-ups

Mercury 2.5 example: tool calling

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-…")

tools = [{
    "type": "function",
    "function": {
        "name": "check_stock",
        "description": "Returns the stock level of a product",
        "parameters": {
            "type": "object",
            "properties": {"sku": {"type": "string"}},
            "required": ["sku"],
        },
    },
}]

messages = [{"role": "user", "content": "How many of the Nike NK-42 shoe are in stock?"}]
first = client.chat.completions.create(model="inception/mercury-2.5", messages=messages, tools=tools)
call = first.choices[0].message.tool_calls[0]

# you run the function yourself — the model never touches the database
args = json.loads(call.function.arguments)
result = {"sku": args["sku"], "qty": 7}

messages += [first.choices[0].message, {"role": "tool", "tool_call_id": call.id, "content": json.dumps(result)}]
final = client.chat.completions.create(model="inception/mercury-2.5", messages=messages, tools=tools)
print(final.choices[0].message.content)
import OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });

const tools = [{
  type: "function",
  function: {
    name: "check_stock",
    description: "Returns the stock level of a product",
    parameters: { type: "object", properties: { sku: { type: "string" } }, required: ["sku"] },
  },
}];

const messages = [{ role: "user", content: "How many of the Nike NK-42 shoe are in stock?" }];
const first = await client.chat.completions.create({ model: "inception/mercury-2.5", messages, tools });
const call = first.choices[0].message.tool_calls[0];

const args = JSON.parse(call.function.arguments);
const result = { sku: args.sku, qty: 7 };

messages.push(first.choices[0].message, { role: "tool", tool_call_id: call.id, content: JSON.stringify(result) });
const final = await client.chat.completions.create({ model: "inception/mercury-2.5", messages, tools });
console.log(final.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-2.5",
    "messages": [{"role": "user", "content": "How many of the Nike NK-42 shoe are in stock?"}],
    "tools": [{
      "type": "function",
      "function": {
        "name": "check_stock",
        "parameters": {"type": "object", "properties": {"sku": {"type": "string"}}, "required": ["sku"]}
      }
    }]
  }'

What is Mercury 2.5 good for?

Mercury 2.5 comes from Inception; in uttapen you reach it with the model id "inception/mercury-2.5". It accepts up to 260,000 tokens of input per call, so a long document or several source files fit in a single request. A single response can run to 65,536 tokens. On price it sits in the "very cheap" band — cheaper than 369 and dearer than 25 of the other paid models in the catalogue.

Capabilities available on this id: 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.

For a back-of-envelope figure: about 86 toman per 1,000-word exchange (13,849 in, 51,933 out, per 1M tokens). It supports cached input: repeated context is billed at 1,385 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 Nova Micro 1.0: Mercury 2.5 works out about 10% 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 1,163 thousand-word requests on Mercury 2.5, and every 1,000 toman is about 11,628 words of round trip. A job with one million input tokens and one million output tokens comes to 65,782 toman in total. Filling this model's 260,000-token window costs 3,601 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "inception/mercury-3", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out about 2.4× cheaper (86 against 203 toman). The context windows differ too: 260,000 against 1,000,000 tokens. Maximum answer length differs as well: 65,536 against 131,072 tokens.

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 Aion 3.5 at 262,144 tokens.

What to use it for: 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.

Provider's own description

Mercury 2.5 is the fastest reasoning LLM, and the latest diffusion LLM (dLLM) from Inception. Instead of generating tokens sequentially, Mercury 2.5 produces and refines multiple tokens in parallel, achieving...

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 out42 toman
Summarising a ten-page document4,000 in + 600 out87 toman
Classifying a thousand short rows120,000 in + 20,000 out2,701 toman

Frequently asked

How do I call Mercury 2.5 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-2.5". Nothing else in your code changes.
What does Mercury 2.5 cost in toman?
13,849 toman per 1M input tokens and 51,933 toman per 1M output tokens; a 1,000-word request is around 86 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Mercury 2.5 take?
Up to 260,000 tokens per request, roughly 195k words. A single answer can reach 65,536 tokens.
Does Mercury 2.5 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.