GLM 5.2 vs GLM 5.3 Flash
Pricing, context window and real answers (August 2026)
GLM 5.2 (Z.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
 GLM 5.2 | GLM 5.3 Flash | |
|---|---|---|
| Provider | Z.ai | Z.ai |
| Input (per 1M tokens) | ¥250.18 | ¥13.19 |
| Output (per 1M tokens) | ¥786.28 | ¥43.98 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | ¥643 | ¥35 |
| Context window | 1,048,576 tokens | 1,310,720 tokens |
| Release date | 6/16/2026 | 8/26/2026 |
| Input modalities | text | text, image, video |
| Arena · overall | #33 · ELO 1,472 | #38 · ELO 1,469 |
| Arena · Japanese | #26 · ELO 1,458 | Unranked |
| Arena · coding | #48 · ELO 1,508 | #11 · ELO 1,531 |
Prices are FastMetal's yen rates before tax. Arena ranks are as of the latest leaderboard scrape.
Which should you pick?
- On price, GLM 5.3 Flash: ¥43.98 per 1M output tokens, 94% less than GLM 5.2.
- On the Overall arena board, GLM 5.2 ranks higher (#33; GLM 5.3 Flash is #38).
- On the Coding arena board, GLM 5.3 Flash ranks higher (#11; GLM 5.2 is #48).
- For long documents, GLM 5.3 Flash: a 1,310,720-token context window against 1,048,576 for GLM 5.2.
- 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.
GLM 5.2
To count the number of 'r's in the word 'strawberry', let's look at the word letter by letter:
1. **s** - no 'r'
2. **t** - no 'r'
3. **r** - this is the 1st 'r'
4. **a** - no 'r'
5. **w** - no 'r'
6. **b** - no 'r'
7. **e** - no 'r'
8. **r** - this is the 2nd 'r'
9. **r** - this is the 3rd 'r'
10. **y** - no 'r'
Total count: 3
There are **3** 'r's in the word 'strawberry'.
2.6s439 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…
GLM 5.2
The error `TypeError: Cannot read properties of undefined (reading 'map')` happens because the `users` prop being passed to your `UserList` component is currently `undefined`. When JavaScript tries to call `.map()` on `undefined`, it throws this error.
Here are the best ways to fix this:
### Fix 1: Provide a default value (Recommended)
You can provide a default empty array `[]` when destructuring the props. This ensures that even if `users` is not passed down, the `.map()` function will still have an array to iterate over (resulting in an empty list rather than a crash).
```jsx
function UserList({ users = [] }) {
return (
<ul>
{users.map(user => <li key={user.id}>{user.name}</…
8.3s1006 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):…
GLM 5.2
Here is a review of your Python function, broken down by readability, performance, and best practices, followed by a refactored version of the code.
### 1. Best Practices
* **Move imports to the top:** Importing modules inside a function is generally discouraged. While Python caches imports, putting `import requests` and `import time` at the top of your file is the standard convention and improves readability.
* **Avoid bare `except:` blocks:** Using a bare `except:` catches everything, including `KeyboardInterrupt` and `SystemExit`. This makes it hard to interrupt your program if it gets stuck in a loop. You should catch specific exceptions, such as `requests.exceptions.RequestException` (…
10.9s2061 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, GLM 5.2 or GLM 5.3 Flash?
- Per 1M output tokens, GLM 5.2 is ¥786.28 and GLM 5.3 Flash is ¥43.98 on FastMetal (yen, before tax), so GLM 5.3 Flash is cheaper.
- How do the context windows of GLM 5.2 and GLM 5.3 Flash compare?
- GLM 5.2 takes 1,048,576 tokens; GLM 5.3 Flash takes 1,310,720.
- Which ranks higher, GLM 5.2 or GLM 5.3 Flash?
- GLM 5.2 ranks higher on the public arena (GLM 5.2 #26, GLM 5.3 Flash #38). Ranks move as the leaderboard updates.
- Can I use GLM 5.2 and GLM 5.3 Flash with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "glm-5.2" 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 GLM 5.2 and GLM 5.3 Flash from the browser chat and the API. No monthly fee.