Beam 1.0 501B A23B vs GLM 5.3 Flash
Pricing, context window and real answers (October 2026)
Beam 1.0 501B A23B (Reflection AI) and GLM 5.3 Flash (Z.ai), 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 |  GLM 5.3 Flash | |
|---|---|---|
| Provider | Reflection AI | Z.ai |
| Input (per 1M tokens) | $0.422 | $0.1583 |
| Output (per 1M tokens) | $1.27 | $0.5275 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | $1.06 | $0.422 |
| Context window | 1,048,576 tokens | 1,310,720 tokens |
| Release date | 10/16/2026 | 8/26/2026 |
| Input modalities | text | text, image, video |
| Throughput (p50) | — | 98 tokens/s (n=14) |
| Arena · overall | Unranked | #41 · ELO 1,474 |
| Arena · Japanese | Unranked | #44 · ELO 1,443 |
| Arena · coding | Unranked | #27 · ELO 1,525 |
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, GLM 5.3 Flash: $0.5275 per 1M output tokens, 58% less than Beam 1.0 501B A23B.
- For long documents, GLM 5.3 Flash: a 1,310,720-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
GLM 5.3 Flash
# Counting 'r's in 'strawberry'
**Step 1: Write out the word letter by letter**
s - t - r - a - w - b - e - r - r - y
**Step 2: Check each letter one at a time**
| Position | Letter | Is it 'r'? |
|----------|--------|-----------|
| 1 | s | No |
| 2 | t | No |
| 3 | r | **Yes! (1)** |
| 4 | a | No |
| 5 | w | No |
| 6 | b | No |
| 7 | e | No |
| 8 | r | **Yes! (2)** |
| 9 | r | **Yes! (3)** |
| 10 | y | No |
**Step 3: Add up the matches**
The 'r's appear at positions 3, 8, and 9.
## Answer: There are **3** 'r's in "strawberry" 🍓
7.2s449 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
GLM 5.3 Flash
## What's happening
The error means the `users` prop is `undefined` at the time `UserList` renders. Since you can't call `.map()` on `undefined`, JavaScript throws the TypeError.
This usually happens for one of these reasons:
1. **The parent isn't passing the prop** — rendering `<UserList />` without `users`, or with a typo'd prop name like `user={users}`.
2. **Data hasn't loaded yet** — the parent fetches data asynchronously and its state starts as `undefined`:
```jsx
const [users, setUsers] = useState(); // undefined until fetch resolves!
return <UserList users={users} />;
```
3. **API response shape mismatch** — the array is nested, e.g., you need `response.data.users` inst…
31.3s1639 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
GLM 5.3 Flash
Solid foundation — you're already passing an explicit `timeout` and implementing retries, which many people skip. Here's a breakdown by category:
## Correctness & robustness (most important)
- **Bare `except:`** catches *everything*, including `KeyboardInterrupt` and `SystemExit`, and silently discards the error. Catch specific exceptions: `requests.RequestException` for network issues, `ValueError` for invalid JSON.
- **Retrying non-retryable failures**: a 401/403/404 will never succeed on retry, so you're sleeping 6 seconds for nothing. Only retry transient errors (5xx, 429, timeouts/connection errors).
- **`item["active"]` raises `KeyError`** on malformed data. Use `item.get("active")`…
116.1s6185 output tokens
Frequently asked questions
- Which is cheaper, Beam 1.0 501B A23B or GLM 5.3 Flash?
- Per 1M output tokens, Beam 1.0 501B A23B is $1.27 and GLM 5.3 Flash is $0.5275 on FastMetal (US dollars, before tax), so GLM 5.3 Flash is cheaper.
- How do the context windows of Beam 1.0 501B A23B and GLM 5.3 Flash compare?
- Beam 1.0 501B A23B takes 1,048,576 tokens; GLM 5.3 Flash takes 1,310,720.
- Can I use Beam 1.0 501B A23B and GLM 5.3 Flash with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "beam-1.0" or "glm-5.3-flash" 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 GLM 5.3 Flash from the browser chat and the API. No monthly fee.