FastMetal

Model comparison

Beam 1.0 501B A23B vs GPT-5.6 Luna

Pricing, context window and real answers (October 2026)

Beam 1.0 501B A23B (Reflection AI) and GPT-5.6 Luna (OpenAI), compared as you can actually call them on FastMetal. Both are served from the same OpenAI-compatible endpoint and API key; switching is a change to the model string.

Specs and pricing

Beam 1.0 501B A23Bopenai logoGPT-5.6 Luna
ProviderReflection AIOpenAI
Input (per 1M tokens)$0.422$0.211
Output (per 1M tokens)$1.27$1.27
Typical cost (1,000 in + 500 out tokens × 1,000 calls)$1.06$0.844
Context window1,048,576 tokens1,050,000 tokens
Release date10/16/20267/9/2026
Input modalitiestextfile, image, text
Throughput (p50)—179 tokens/s (n=26)
Arena · overallUnranked#75 · ELO 1,453
Arena · JapaneseUnranked#55 · ELO 1,431
Arena · codingUnranked#75 · ELO 1,501

Prices are FastMetal's rates, before tax.

Rankings: LMArena leaderboard dataset (lmarena-ai/leaderboard-dataset), CC BY 4.0, snapshot 2026-10-15; category names mapped and ratings rounded by FastMetal.

Which should you pick?

  • Output pricing is identical ($1.27 per 1M tokens).
  • For long documents, GPT-5.6 Luna: a 1,050,000-token context window against 1,048,576 for Beam 1.0 501B A23B.
  • If in doubt, try both on the same key. Switching is a change to the model string, and each is billed at its own rate.

Real answers to the same prompts

Exactly what the FastMetal gateway returned, side by side. Not benchmark scores: actual output.

Count the number of 'r's in 'strawberry'

Count the number of 'r's in 'strawberry'. Explain your reasoning step by step.

Beam 1.0 501B A23B

The word “strawberry” is spelled: s - t - r - a - w - b - e - r - r - y The letter “r” appears in these positions: - s**r**awberry (1st r) - strawbe**r**ry (2nd r) - strawber**r**y (3rd r) Therefore, there are **3** r's in “strawberry.”
7.6s278 output tokens

GPT-5.6 Luna

The word **“strawberry”** is spelled: **s – t – r – a – w – b – e – r – r – y** The letter **“r”** appears at positions **3, 8, and 9**. **Answer: 3**
3.0s120 output tokens

Debug This Error

I'm getting the following error in my Node.js application: TypeError: Cannot read properties of undefined (reading 'map') at UserList (/app/components/UserList.js:12:25) at renderWithHooks (/app/node_modules/rea…

Beam 1.0 501B A23B

The error means `users` is `undefined` when `UserList` renders, so calling `.map` on `undefined` throws. ### Common causes 1. Parent renders `<UserList />` without a `users` prop. 2. Parent passes `users={data}` where `data` is still `undefined` (e.g., fetch not resolved). 3. Destructured prop name mismatch. ### Fixes **1. Default the prop** ```js function UserList({ users = [] }) { return ( <ul> {users.map(user => <li key={user.id}>{user.name}</li>)} </ul> ); } ``` **2. Guard in JSX** ```js {Array.isArray(users) && users.map(user => <li key={user.id}>{user.name}</li>)} ``` **3. Optional chaining** ```js {users?.map(user => <li key={user.id}>{user.name}</li>)} ``` Note…
12.8s414 output tokens

GPT-5.6 Luna

`users` is `undefined` when `UserList` renders, so calling `.map()` on it fails. `.map()` only exists on arrays. Common causes include: - The parent does not pass a `users` prop. - The prop name differs, such as `userList` instead of `users`. - Users are loaded asynchronously and are initially undefined. - The API response has a different shape, such as `{ data: [...] }`. ### Provide a default value ```jsx function UserList({ users = [] }) { return ( <ul> {users.map(user => ( <li key={user.id}>{user.name}</li> ))} </ul> ); } ``` The parent should pass an array: ```jsx <UserList users={users} /> ``` If the list comes from state, initialize it as an empty…
4.8s507 output tokens

Code Review

Please review the following Python function and suggest improvements for readability, performance, and best practices: def get_data(url, retries=3): import requests import time for i in range(retries):…

Beam 1.0 501B A23B

Here are some key issues and improvements: ### Main issues in the original - **Imports inside the function** run on every call and hurt readability. - **Bare `except:`** catches `KeyboardInterrupt`, `SystemExit`, and programming errors. - **Retries every non-200 response**, including `404`/`400`, which usually should not be retried. - **`item["active"] == True`** can raise `KeyError` and is stricter than necessary unless you truly need exact boolean equality. - **Fixed sleep and unnecessary final sleep** before returning. - **Repeated `item["name"]` / `item["id"]`** lookups are slightly slower and less clean than a comprehension. - No handling/validation for malformed JSON or non-list respo…
16.8s1927 output tokens

GPT-5.6 Luna

### Main issues - Imports should be at module level, not inside the function. - A bare `except:` catches everything, including programming errors and `KeyboardInterrupt`. - The function sleeps even after the final failed attempt. - It retries all HTTP statuses, including permanent errors such as `404`. - `status_code == 200` excludes other successful `2xx` responses. - `if item["active"] == True` should generally be `if item.get("active") is True`. - A `requests.Session` can reuse connections and improve performance. - Returning `[]` for every failure makes it impossible to distinguish “no active items” from “request failed.” - Retries should generally use backoff rather than a fixed delay.…
23.3s1978 output tokens

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Frequently asked questions

Which is cheaper, Beam 1.0 501B A23B or GPT-5.6 Luna?
Both are $1.27 per 1M output tokens on FastMetal (US dollars, before tax).
How do the context windows of Beam 1.0 501B A23B and GPT-5.6 Luna compare?
Beam 1.0 501B A23B takes 1,048,576 tokens; GPT-5.6 Luna takes 1,050,000.
Can I use Beam 1.0 501B A23B and GPT-5.6 Luna with the same API key?
Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "beam-1.0" or "gpt-5.6-luna" as the model. Each is billed at its own rate from the same prepaid balance.

Try both on one API key

Create an account and add credit to call Beam 1.0 501B A23B and GPT-5.6 Luna from the browser chat and the API. No monthly fee.

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