uttapen
Create an API key

Solar Pro 3 API: toman pricing and code

upstage/solar-pro-3

toolsreasoningjson
Create a keyTry it
Input · per 1M tokens
43,519 toman
Output · per 1M tokens
174,075 toman
One 1,000-word request ≈
283 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 4,352 toman / 1M.Pricing and top-ups

Solar Pro 3 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="upstage/solar-pro-3", 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="upstage/solar-pro-3", messages=messages, tools=tools)
print(final.choices[0].message.content)

What is Solar Pro 3 good for?

The id for Solar Pro 3 in our API is "upstage/solar-pro-3", served from Upstage. Context is 131,072 tokens; past that you have to summarise the history yourself. A single response can run to 117,964 tokens. On price it sits in the "very cheap" band — cheaper than 95 and dearer than 311 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 283 toman per 1,000-word exchange (43,519 in, 174,075 out, per 1M tokens). It supports cached input: repeated context is billed at 4,352 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 Command R (08-2024): Solar Pro 3 works out roughly 1× 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 353 thousand-word requests on Solar Pro 3, and every 1,000 toman is about 3,534 words of round trip. A job with one million input tokens and one million output tokens comes to 217,594 toman in total. Filling this model's 131,072-token window costs 5,704 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "upstage/solar-pro4", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out 5× dearer (283 against 57 toman). The context windows differ too: 131,072 against 524,288 tokens. Maximum answer length differs as well: 117,964 against 131,072 tokens.

Upstage has 2 models in our catalogue; the cheapest is Solar Pro 4 at 57 toman per thousand words and the dearest Solar Pro 3 at 283. Among the less common parameters it accepts parallel_tool_calls — all through the standard request body. It does not support 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-2.0 at 131,072 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.

Provider's own description

Solar Pro 3 is Upstage's powerful Mixture-of-Experts (MoE) language model. With 102B total parameters and 12B active parameters per forward pass, it delivers exceptional performance while maintaining computational efficiency. Optimized...

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 out135 toman
Summarising a ten-page document4,000 in + 600 out279 toman
Classifying a thousand short rows120,000 in + 20,000 out8,704 toman

Frequently asked

How do I call Solar Pro 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 "upstage/solar-pro-3". Nothing else in your code changes.
What does Solar Pro 3 cost in toman?
43,519 toman per 1M input tokens and 174,075 toman per 1M output tokens; a 1,000-word request is around 283 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Solar Pro 3 take?
Up to 131,072 tokens per request, roughly 98k words. A single answer can reach 117,964 tokens.
Does Solar Pro 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.