LFM2.5-24B-A2B API: toman pricing and code
liquid/lfm-2.5-24b-a2b
You are billed for the usage the request actually reported. Prices follow the market. Cached input: 8,655 toman per 1M tokens.Pricing and top-ups
LFM2.5-24B-A2B 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="liquid/lfm-2.5-24b-a2b",
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: "liquid/lfm-2.5-24b-a2b",
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": "liquid/lfm-2.5-24b-a2b",
"messages": [{"role": "user", "content": "Classify the ticket and give it a priority from 1 to 5."}],
"response_format": {"type": "json_object"}
}'What is LFM2.5-24B-A2B good for?
The id for LFM2.5-24B-A2B in our API is "liquid/
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 17,311 toman per 1M input tokens and 242,354 per 1M output tokens. A 1,000-word round trip on LFM2.5-24B-A2B lands near 338 toman. It supports cached input: repeated context is billed at 8,655 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 Mercury 2: LFM2.5-24B-A2B works out about 23% cheaper, and its context window is the same size. 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 296 thousand-word requests on LFM2.5-24B-A2B, and every 1,000 toman is about 2,959 words of round trip. A job with one million input tokens and one million output tokens comes to 259,665 toman in total. Filling this model's 128,000-token window costs 2,216 toman on the input side alone, which is the real reason to keep conversation history short.
Its nearest relative in the catalogue is "liquid/
Liquid has 2 models in our catalogue. Among the less common parameters it accepts logprobs, max_completion_tokens, min_p, presence_penalty, repetition_penalty, top_k — all through the standard request body. By context size the nearest option from another provider is Nova Micro 1.0 at 128,000 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.
LFM2.5-24B-A2B is a mixture-of-experts reasoning model from Liquid AI with 24B total and 2.45B active parameters. It always reasons before answering and is built for agents that call tools. Liquid...
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 | 123 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 215 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 6,924 toman |
Price history
| Date | Input (toman/1M) | Output (toman/1M) |
|---|---|---|
| 13 Oct 2026 | 17,311 | 242,354 |
| 13 Oct 2026 | 34,622 | 346,219 |
Every price change for this model is recorded. Toman figures use today's rate.
Frequently asked
- How do I call LFM2.5-24B-A2B 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 "liquid/ lfm-2.5-24b-a2b". Nothing else in your code changes. - What does LFM2.5-24B-A2B cost in toman?
- 17,311 toman per 1M input tokens and 242,354 toman per 1M output tokens; a 1,000-word request is around 338 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does LFM2.5-24B-A2B take?
- Up to 128,000 tokens per request, roughly 96k words. A single answer can reach 8,192 tokens.
- Does LFM2.5-24B-A2B 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.