Weaver (alpha) API: toman pricing and code
mancer/weaver
jsonYou are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups
Weaver (alpha) 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="mancer/weaver",
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: "mancer/weaver",
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": "mancer/weaver",
"messages": [{"role": "user", "content": "Classify the ticket and give it a priority from 1 to 5."}],
"response_format": {"type": "json_object"}
}'What is Weaver (alpha) good for?
Weaver (alpha) comes from Mancer; in uttapen you reach it with the model id "mancer/weaver". It accepts up to 8,000 tokens of input per call, so a long document or several source files fit in a single request. A single response can run to 6,000 tokens. On price it sits in the "cheap" band — cheaper than 113 and dearer than 293 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 434 toman per 1,000-word exchange (116,050 in, 217,594 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 Llama 3.3 Euryale 70B: Weaver (alpha) works out roughly 1.2× 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 230 thousand-word requests on Weaver (alpha), and every 1,000 toman is about 2,304 words of round trip. A job with one million input tokens and one million output tokens comes to 333,644 toman in total. Filling this model's 8,000-token window costs 928 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 GPT-4 at 8,191 tokens.
What to use it for: 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).
An attempt to recreate Claude-style verbosity, but don't expect the same level of coherence or memory. Meant for use in roleplay/narrative situations.
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 | 261 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 595 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 18,278 toman |
Frequently asked
- How do I call Weaver (alpha) 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 "mancer/weaver". Nothing else in your code changes.
- What does Weaver (alpha) cost in toman?
- 116,050 toman per 1M input tokens and 217,594 toman per 1M output tokens; a 1,000-word request is around 434 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Weaver (alpha) take?
- Up to 8,000 tokens per request, roughly 6k words. A single answer can reach 6,000 tokens.
- Can I stream Weaver (alpha)'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.