Beam 1.0 501B A23B vs GPT-4o-mini
Pricing, context window and real answers (October 2026)
Beam 1.0 501B A23B (Reflection AI) and GPT-4o-mini (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 A23B |  GPT-4o-mini | |
|---|---|---|
| Provider | Reflection AI | OpenAI |
| Input (per 1M tokens) | $0.422 | $0.1583 |
| Output (per 1M tokens) | $1.27 | $0.633 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | $1.06 | $0.4748 |
| Context window | 1,048,576 tokens | 128,000 tokens |
| Release date | 10/16/2026 | 7/18/2024 |
| Input modalities | text | text, image, file |
| Throughput (p50) | — | 130 tokens/s (n=26) |
| Arena · overall | Unranked | #265 · ELO 1,318 |
| Arena · Japanese | Unranked | #199 · ELO 1,251 |
| Arena · coding | Unranked | #273 · ELO 1,348 |
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?
- On price, GPT-4o-mini: $0.633 per 1M output tokens, 50% less than Beam 1.0 501B A23B.
- For long documents, Beam 1.0 501B A23B: a 1,048,576-token context window against 128,000 for GPT-4o-mini.
- 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-4o-mini
To count the number of 'r's in the word "strawberry," we can follow these steps:
1. **Identify the word**: We have the word "strawberry."
2. **Break down the letters**: Let's write out the letters in the word:
- s
- t
- r
- a
- w
- b
- e
- r
- r
- y
3. **Locate the 'r's**: Now, we need to look for the letter 'r' among these letters.
- In the list of letters, we can see:
- The first 'r' is in the 3rd position.
- The second 'r' is in the 8th position.
- The third 'r' is in the 9th position.
4. **Count the occurrences**: We check each position we identified:
- There are three occurrences of 'r' in the word "strawberry."
5. **Conclusion**:…
2.1s239 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-4o-mini
The error you're encountering, `TypeError: Cannot read properties of undefined (reading 'map')`, indicates that the `users` prop is `undefined` when you try to call the `map` method on it.
In your `UserList` component, you're attempting to use `users.map(...)`, but if the `users` prop is not provided or is `undefined`, this will throw an error.
### Fixing the Error
1. **Default Props**: You can set a default value for the `users` prop to prevent the error when it is `undefined`.
```jsx
function UserList({ users = [] }) { // Default to an empty array
return (
<ul>
{users.map(user => <li key={user.id}>{user.name}</li>)}
</ul>
);
}
```…
5.0s511 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-4o-mini
Your Python function, `get_data`, essentially performs a GET request, handles retries on failure, and processes the response JSON to return a filtered list of dictionaries. Below, I've highlighted several improvements regarding readability, performance, and best practices:
1. **Avoid blanket exceptions**: It’s best practice to specify which exceptions you expect, allowing for better debugging and error handling.
2. **Use `requests` built-in exception handling**: Handle specific requests exceptions, such as `requests.exceptions.RequestException`.
3. **Improve condition checks**: Check for `item['active']` directly instead of comparing to `True`, which is more Pythonic.
4. **Use `time.sl…
8.8s628 output tokens
Frequently asked questions
- Which is cheaper, Beam 1.0 501B A23B or GPT-4o-mini?
- Per 1M output tokens, Beam 1.0 501B A23B is $1.27 and GPT-4o-mini is $0.633 on FastMetal (US dollars, before tax), so GPT-4o-mini is cheaper.
- How do the context windows of Beam 1.0 501B A23B and GPT-4o-mini compare?
- Beam 1.0 501B A23B takes 1,048,576 tokens; GPT-4o-mini takes 128,000.
- Can I use Beam 1.0 501B A23B and GPT-4o-mini with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "beam-1.0" or "gpt-4o-mini" 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-4o-mini from the browser chat and the API. No monthly fee.