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Ling 3.0 Flash Fin API: toman pricing and code

inclusionai/ling-3.0-flash-fin

toolsreasoningjson
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Input · per 1M tokens
17,408 toman
Output · per 1M tokens
52,223 toman
One 1,000-word request ≈
91 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 3,482 toman / 1M.Pricing and top-ups

Ling 3.0 Flash Fin example: a multi-step problem with reasoning

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-…")

resp = client.chat.completions.create(
    model="inclusionai/ling-3.0-flash-fin",
    messages=[{"role": "user", "content": (
        "A shop has 3 warehouses. A ships 120 orders a day, B ships 85 and C ships 40. "
        "If C closes and its load is split between A and B in proportion to their capacity, how many does each ship a day? "
        "Work through it step by step and give just the two numbers at the end."
    )}],
    reasoning_effort="medium",   # low | medium | high
)
print(resp.choices[0].message.content)
# reasoning tokens count as output tokens too:
print(resp.usage.completion_tokens_details)

What is Ling 3.0 Flash Fin good for?

Ling 3.0 Flash Fin is one of inclusionAI's models. Put "inclusionai/ling-3.0-flash-fin" in the model field and the rest of your code stays as it is. Ling 3.0 Flash Fin 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 235,929 tokens. On price it sits in the "very cheap" band — cheaper than 29 and dearer than 377 of the other paid models in the catalogue.

What you get on top of text in, text out: 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.

Input runs at 17,408 toman per 1M tokens and output at 52,223 — output costs 3× input, so trimming the answer saves more than trimming the prompt. It supports cached input: repeated context is billed at 3,482 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 Laguna S 2.1: Ling 3.0 Flash Fin works out roughly 1.1× cheaper, and its context window is smaller. 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 1,099 thousand-word requests on Ling 3.0 Flash Fin, and every 1,000 toman is about 10,989 words of round trip. A job with one million input tokens and one million output tokens comes to 69,630 toman in total. Filling this model's 262,144-token window costs 4,563 toman on the input side alone, which is the real reason to keep conversation history short.

This id gets confused with "inclusionai/ling-3.0-flash-fin:free", because the underlying model is the same. The difference is the suffix: no suffix (the standard variant) against "free". Maximum answer length differs as well: 235,929 against 32,768 tokens. On parameters, this one takes logit_bias, min_p, response_format, structured_outputs while the other takes logprobs, top_logprobs.

inclusionAI has 4 models in our catalogue; the cheapest is Ling 3.0 Flash at 32 toman per thousand words and the dearest Ling 3.0 Flash Fin at 91. Among the less common parameters it accepts logit_bias, min_p, repetition_penalty, top_k — all through the standard request body. By context size the nearest option from another provider is Trinity Large Thinking at 262,144 tokens.

Where it makes sense: 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

Ling 3.0 Flash Fin is a finance-focused mixture-of-experts model from InclusionAI, built on Ling 3.0 Flash with 5.1B active parameters out of 124B total. It is designed for real-world investment...

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 out47 toman
Summarising a ten-page document4,000 in + 600 out101 toman
Classifying a thousand short rows120,000 in + 20,000 out3,133 toman

Other variants of this model

Same core model, different execution terms and different price. This page is the standard variant.

VariantModel idOutput / 1MContext
freeinclusionai/ling-3.0-flash-fin:free0262,144

Put the variant's id verbatim in the model field; nothing else in your code changes.

Frequently asked

How do I call Ling 3.0 Flash Fin 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 "inclusionai/ling-3.0-flash-fin". Nothing else in your code changes.
What does Ling 3.0 Flash Fin cost in toman?
17,408 toman per 1M input tokens and 52,223 toman per 1M output tokens; a 1,000-word request is around 91 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Ling 3.0 Flash Fin take?
Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 235,929 tokens.
Does Ling 3.0 Flash Fin 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.