GLM 5.2 vs GPT-5.6 Terra
Pricing, context window and real answers (August 2026)
GLM 5.2 (Z.ai) and GPT-5.6 Terra (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
 GLM 5.2 |  GPT-5.6 Terra | |
|---|---|---|
| Provider | Z.ai | OpenAI |
| Input (per 1M tokens) | ¥250.18 | ¥357.4 |
| Output (per 1M tokens) | ¥786.28 | ¥2,144.4 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | ¥643 | ¥1,430 |
| Context window | 1,048,576 tokens | 1,050,000 tokens |
| Release date | 6/16/2026 | 7/9/2026 |
| Input modalities | text | file, image, text |
| Arena · overall | #31 · ELO 1,469 | #34 · ELO 1,468 |
| Arena · Japanese | #13 · ELO 1,472 | Unranked |
| Arena · coding | #46 · ELO 1,506 | #22 · ELO 1,521 |
Prices are FastMetal's yen rates before tax. Arena ranks are as of the latest leaderboard scrape.
Which should you pick?
- On price, GLM 5.2: ¥786.28 per 1M output tokens, 63% less than GPT-5.6 Terra.
- On the Overall arena board, GLM 5.2 ranks higher (#31; GPT-5.6 Terra is #34).
- On the Coding arena board, GPT-5.6 Terra ranks higher (#22; GLM 5.2 is #46).
- For long documents, GPT-5.6 Terra: a 1,050,000-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
GPT-5.6 Terra
“strawberry” is spelled:
s t **r** a w b e **r** **r** y
There are **3** occurrences of the letter **r**.
3.6s71 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
GPT-5.6 Terra
The error means `users` is `undefined` when `UserList` renders, so React tries to evaluate:
```js
users.map(...)
```
But `.map()` only exists on arrays.
Common causes:
- The parent component does not pass a `users` prop.
- The user data is loaded asynchronously and has not arrived on the first render.
- The API response shape differs from what you expect, e.g. data is in `response.data.users` rather than `response.data`.
- The parent initializes its user state as `undefined` or `null`.
## Fix options
### 1. Initialize users as an empty array
In the parent component:
```jsx
const [users, setUsers] = useState([]);
```
Then pass it normally:
```jsx
<UserList users={users} />
```
An e…
5.8s559 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
GPT-5.6 Terra
Here are the main areas to improve:
### Issues in the original code
- **Imports inside the function**: Move `requests` and `time` imports to module scope.
- **Bare `except:`**: This catches everything, including `KeyboardInterrupt` and programming errors. Catch `requests.RequestException` (and possibly JSON decoding errors) instead.
- **Status code check**: Prefer `response.raise_for_status()` or `response.ok` over manually checking only for `200`. Successful responses can include other 2xx status codes.
- **`== True`**: Use `if item.get("active"):` instead.
- **Potential `KeyError`**: `item["name"]`, `item["id"]`, and `item["active"]` can fail if the API response is incomplete. Use `.get(…
10.6s939 output tokens
Frequently asked questions
- Which is cheaper, GLM 5.2 or GPT-5.6 Terra?
- Per 1M output tokens, GLM 5.2 is ¥786.28 and GPT-5.6 Terra is ¥2,144.4 on FastMetal (yen, before tax), so GLM 5.2 is cheaper.
- How do the context windows of GLM 5.2 and GPT-5.6 Terra compare?
- GLM 5.2 takes 1,048,576 tokens; GPT-5.6 Terra takes 1,050,000.
- Which ranks higher, GLM 5.2 or GPT-5.6 Terra?
- GLM 5.2 ranks higher on the public arena (GLM 5.2 #13, GPT-5.6 Terra #34). Ranks move as the leaderboard updates.
- Can I use GLM 5.2 and GPT-5.6 Terra with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "glm-5.2" or "gpt-5.6-terra" 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 GPT-5.6 Terra from the browser chat and the API. No monthly fee.