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ReMM SLERP 13B API: toman pricing and code

undi95/remm-slerp-l2-13b

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Input · per 1M tokens
101,544 toman
Output · per 1M tokens
188,581 toman
One 1,000-word request ≈
377 toman

You are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups

ReMM SLERP 13B 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="undi95/remm-slerp-l2-13b",
    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))

What is ReMM SLERP 13B good for?

ReMM SLERP 13B comes from Undi95; in uttapen you reach it with the model id "undi95/remm-slerp-l2-13b". It accepts up to 6,144 tokens of input per call, so a long document or several source files fit in a single request. A single response can run to 5,529 tokens. On price it sits in the "cheap" band — cheaper than 108 and dearer than 298 of the other paid models in the catalogue.

What you get on top of text in, text out: it returns schema-valid JSON through response_format, ready to hand to your code. All of it works through the standard parameters of the official OpenAI SDK — no custom client, no wrapper.

Input runs at 101,544 toman per 1M tokens and output at 188,581 — output costs 1.9× input, so trimming the answer saves more than trimming the prompt. 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 Gemma 2 27B: ReMM SLERP 13B works out roughly 1.3× 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 265 thousand-word requests on ReMM SLERP 13B, and every 1,000 toman is about 2,653 words of round trip. A job with one million input tokens and one million output tokens comes to 290,125 toman in total. Filling this model's 6,144-token window costs 624 toman on the input side alone, which is the real reason to keep conversation history short.

Among the less common parameters it accepts logit_bias, logprobs, min_p, repetition_penalty, top_a, top_k — all through the standard request body. It does not support include_reasoning, reasoning, tool_choice, tools, 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 Weaver (alpha) at 8,000 tokens.

Where it makes sense: high-volume work such as classification, tagging and bulk summarising, extracting data against a fixed schema, short single-turn requests (the context window is small).

Provider's own description

A recreation trial of the original MythoMax-L2-B13 but with updated models. #merge

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 out228 toman
Summarising a ten-page document4,000 in + 600 out519 toman
Classifying a thousand short rows120,000 in + 20,000 out15,957 toman

Frequently asked

How do I call ReMM SLERP 13B 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 "undi95/remm-slerp-l2-13b". Nothing else in your code changes.
What does ReMM SLERP 13B cost in toman?
101,544 toman per 1M input tokens and 188,581 toman per 1M output tokens; a 1,000-word request is around 377 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does ReMM SLERP 13B take?
Up to 6,144 tokens per request, roughly 5k words. A single answer can reach 5,529 tokens.
Can I stream ReMM SLERP 13B's output?
Yes — with stream=true you get SSE events as the tokens are produced. This model has no tool calling and no image input, so pick a different one if you need either.