Uncensored API: toman pricing and code
cognitivecomputations/dolphin-mistral-24b-venice-edition
jsonYou are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups
Uncensored 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="cognitivecomputations/dolphin-mistral-24b-venice-edition",
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: "cognitivecomputations/dolphin-mistral-24b-venice-edition",
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": "cognitivecomputations/dolphin-mistral-24b-venice-edition",
"messages": [{"role": "user", "content": "Classify the ticket and give it a priority from 1 to 5."}],
"response_format": {"type": "json_object"}
}'What is Uncensored good for?
Uncensored comes from Cognitive Computations; in uttapen you reach it with the model id "cognitivecomputations/dolphin-mistral-24b-venice-edition". It accepts up to 128,000 tokens of input per call, so a long document or several source files fit in a single request. A single response can run to 8,192 tokens. On price it sits in the "cheap" band — cheaper than 129 and dearer than 277 of the other paid models in the catalogue.
Capabilities available on this id: 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.
For a back-of-envelope figure: about 415 toman per 1,000-word exchange (58,025 in, 261,113 out, per 1M tokens). 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 R1 Distill Llama 70B: Uncensored works out roughly 1.5× cheaper, 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 241 thousand-word requests on Uncensored, and every 1,000 toman is about 2,410 words of round trip. A job with one million input tokens and one million output tokens comes to 319,138 toman in total. Filling this model's 128,000-token window costs 7,427 toman on the input side alone, which is the real reason to keep conversation history short.
Among the less common parameters it accepts 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 Nova Micro 1.0 at 128,000 tokens.
What to use it for: high-volume work such as classification, tagging and bulk summarising, extracting data against a fixed schema.
Venice Uncensored Dolphin Mistral 24B Venice Edition is a fine-tuned variant of Mistral-Small-24B-Instruct-2501, developed by dphn.ai in collaboration with Venice.ai. This model is designed as an “uncensored” instruct-tuned LLM, preserving...
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 | 191 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 389 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 12,185 toman |
Frequently asked
- How do I call Uncensored 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 "cognitivecomputations/dolphin-mistral-24b-venice-edition". Nothing else in your code changes.
- What does Uncensored cost in toman?
- 58,025 toman per 1M input tokens and 261,113 toman per 1M output tokens; a 1,000-word request is around 415 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Uncensored take?
- Up to 128,000 tokens per request, roughly 96k words. A single answer can reach 8,192 tokens.
- Can I stream Uncensored'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.