Beam 1.0 501B A23B API: toman pricing and code
reflection/beam-1.0-501b-a23b
You are billed for the usage the request actually reported. Prices follow the market. Cached input: 17,311 toman per 1M tokens.Pricing and top-ups
Beam 1.0 501B A23B example: tool calling
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-…")
tools = [{
"type": "function",
"function": {
"name": "check_stock",
"description": "Returns the stock level of a product",
"parameters": {
"type": "object",
"properties": {"sku": {"type": "string"}},
"required": ["sku"],
},
},
}]
messages = [{"role": "user", "content": "How many of the Nike NK-42 shoe are in stock?"}]
first = client.chat.completions.create(model="reflection/beam-1.0-501b-a23b", messages=messages, tools=tools)
call = first.choices[0].message.tool_calls[0]
# you run the function yourself — the model never touches the database
args = json.loads(call.function.arguments)
result = {"sku": args["sku"], "qty": 7}
messages += [first.choices[0].message, {"role": "tool", "tool_call_id": call.id, "content": json.dumps(result)}]
final = client.chat.completions.create(model="reflection/beam-1.0-501b-a23b", messages=messages, tools=tools)
print(final.choices[0].message.content)import OpenAI from "openai";
const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });
const tools = [{
type: "function",
function: {
name: "check_stock",
description: "Returns the stock level of a product",
parameters: { type: "object", properties: { sku: { type: "string" } }, required: ["sku"] },
},
}];
const messages = [{ role: "user", content: "How many of the Nike NK-42 shoe are in stock?" }];
const first = await client.chat.completions.create({ model: "reflection/beam-1.0-501b-a23b", messages, tools });
const call = first.choices[0].message.tool_calls[0];
const args = JSON.parse(call.function.arguments);
const result = { sku: args.sku, qty: 7 };
messages.push(first.choices[0].message, { role: "tool", tool_call_id: call.id, content: JSON.stringify(result) });
const final = await client.chat.completions.create({ model: "reflection/beam-1.0-501b-a23b", messages, tools });
console.log(final.choices[0].message.content);curl https://api.uttapen.ir/v1/chat/completions \
-H "Authorization: Bearer sk-up-…" \
-H "Content-Type: application/json" \
-d '{
"model": "reflection/beam-1.0-501b-a23b",
"messages": [{"role": "user", "content": "How many of the Nike NK-42 shoe are in stock?"}],
"tools": [{
"type": "function",
"function": {
"name": "check_stock",
"parameters": {"type": "object", "properties": {"sku": {"type": "string"}}, "required": ["sku"]}
}
}]
}'What is Beam 1.0 501B A23B good for?
Beam 1.0 501B A23B comes from Reflection; in uttapen you reach it with the model id "reflection/
Capabilities available on this id: 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.
For a back-of-envelope figure: about 563 toman per 1,000-word exchange (86,555 in, 346,219 out, per 1M tokens). It supports cached input: repeated context is billed at 17,311 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 DeepSeek V3.1 Terminus, and Beam 1.0 501B A23B costs about the same, 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 178 thousand-word requests on Beam 1.0 501B A23B, and every 1,000 toman is about 1,776 words of round trip. A job with one million input tokens and one million output tokens comes to 432,774 toman in total. Filling this model's 1,048,576-token window costs 90,759 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, presence_penalty, reasoning_effort, repetition_penalty — all through the standard request body. By context size the nearest option from another provider is DeepSeek V4 Flash 0423 at 1,048,576 tokens.
What to use it for: 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, analysing a long document or codebase in one request.
Beam 1.0 is Reflection's open-weight reasoning model for coding and long-horizon agentic workflows. It uses a sparse mixture-of-experts architecture with 501B total and 23B active parameters per token, and a...
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 | 268 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 554 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 17,311 toman |
Price history
| Date | Input (toman/1M) | Output (toman/1M) |
|---|---|---|
| 22 Oct 2026 | 86,555 | 346,219 |
| 20 Oct 2026 | 103,866 | 346,219 |
| 17 Oct 2026 | 346,219 | 1,038,658 |
| 16 Oct 2026 | 86,555 | 346,219 |
Every price change for this model is recorded. Toman figures use today's rate.
Frequently asked
- How do I call Beam 1.0 501B A23B 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 "reflection/ beam-1.0-501b-a23b". Nothing else in your code changes. - What does Beam 1.0 501B A23B cost in toman?
- 86,555 toman per 1M input tokens and 346,219 toman per 1M output tokens; a 1,000-word request is around 563 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Beam 1.0 501B A23B take?
- Up to 1,048,576 tokens per request, roughly 786k words. A single answer can reach 131,072 tokens.
- Does Beam 1.0 501B A23B 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.