Relace Search API: toman pricing and code
relace/relace-search
toolsjsonYou are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups
Relace Search 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="relace/relace-search",
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: "relace/relace-search",
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": "relace/relace-search",
"messages": [{"role": "user", "content": "Classify the ticket and give it a priority from 1 to 5."}],
"response_format": {"type": "json_object"}
}'What is Relace Search good for?
Relace Search is one of Relace's models. Put "relace/relace-search" in the model field and the rest of your code stays as it is. Relace Search keeps 256,000 tokens in view at once, and that number is what caps the conversation history in your product. A single response can run to 128,000 tokens. On price it sits in the "mid-range" band — cheaper than 242 and dearer than 164 of the other paid models in the catalogue.
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. All of it works through the standard parameters of the official OpenAI SDK — no custom client, no wrapper.
Pricing is 290,125 toman per 1M input tokens and 870,375 per 1M output tokens. A 1,000-word round trip on Relace Search lands near 1,509 toman. 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 GLM 5.1: Relace Search works out roughly 1× more expensive, 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 66 thousand-word requests on Relace Search, and every 1,000 toman is about 663 words of round trip. A job with one million input tokens and one million output tokens comes to 1,160,500 toman in total. Filling this model's 256,000-token window costs 74,272 toman on the input side alone, which is the real reason to keep conversation history short.
Relace has 2 models in our catalogue; the cheapest is Relace Apply 3 at 792 toman per thousand words and the dearest Relace Search at 1,509. It does not support include_reasoning, reasoning, structured_outputs, 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 Command A at 256,000 tokens.
Good fits: product chatbots and internal assistants, where cost and quality have to balance, agents that reach out to APIs and databases, extracting data against a fixed schema, analysing a long document or codebase in one request.
The relace-search model uses 4-12 `view_file` and `grep` tools in parallel to explore a codebase and return relevant files to the user request. In contrast to RAG, relace-search performs agentic...
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 | 783 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 1,683 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 52,223 toman |
Frequently asked
- How do I call Relace Search 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 "relace/relace-search". Nothing else in your code changes.
- What does Relace Search cost in toman?
- 290,125 toman per 1M input tokens and 870,375 toman per 1M output tokens; a 1,000-word request is around 1,509 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Relace Search take?
- Up to 256,000 tokens per request, roughly 192k words. A single answer can reach 128,000 tokens.
- Does Relace Search 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.