GLM 5.3 vs GPT-5.6 Terra
Pricing, context window and real answers (August 2026)
GLM 5.3 (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 5.3 |  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 | 8/18/2026 | 7/9/2026 |
| Input modalities | text | file, image, text |
| Arena · overall | Unranked | #34 · ELO 1,468 |
| Arena · Japanese | Unranked | Unranked |
| Arena · coding | Unranked | #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 5.3: ¥786.28 per 1M output tokens, 63% less than GPT-5.6 Terra.
- For long documents, GPT-5.6 Terra: a 1,050,000-token context window against 1,048,576 for GLM 5.3.
- 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 5.3
# Counting 'r's in "strawberry"
**Step 1: Spell out the word letter by letter.**
s - t - r - a - w - b - e - r - r - y
**Step 2: Go through each letter and mark the 'r's.**
| Position | Letter | Is it 'r'? |
|----------|--------|------------|
| 1 | s | No |
| 2 | t | No |
| 3 | **r** | ✅ Yes (1st) |
| 4 | a | No |
| 5 | w | No |
| 6 | b | No |
| 7 | e | No |
| 8 | **r** | ✅ Yes (2nd) |
| 9 | **r** | ✅ Yes (3rd) |
| 10 | y | No |
**Step 3: Tally the results.**
- 1st 'r' in "st**r**awberry"
- 2nd 'r' in "strawbe**r**ry"
- 3rd 'r' in "strawber**r**y"
## Answer: There are **3** 'r's in "strawberry."
(Note: two of them appear together in the "rr" near the end, which is easy to miss when c…
5.8s493 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 5.3
## What's happening
The error means `users` is `undefined` when the component renders. `undefined` doesn't have a `.map()` method, so JavaScript throws a `TypeError`. Since destructured props that aren't passed default to `undefined`, something upstream isn't providing the value you expect.
## Common causes
1. **The parent doesn't pass the prop, or uses a different name:**
```jsx
<UserList /> {/* missing users */}
<UserList userList={users} /> {/* prop name mismatch */}
```
2. **Async data hasn't loaded yet** — the component renders before an API call completes, and the initial value is `undefined`:
```jsx
const [users, setUsers] = useState(); // undefin…
17.1s1345 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 5.3
Here's a breakdown of the issues, followed by a refactored version.
## Readability
- **Move imports to module level.** PEP 8 requires top-level imports; importing inside a function hides dependencies and adds overhead on every call (small, but pointless).
- **`item["active"] == True`** → just `item.get("active")`. Comparing to `True` with `==` is flagged by PEP 8, and `.get()` avoids a `KeyError` if the key is missing.
- **Use a list comprehension** instead of initializing a list and appending in a loop.
- **Rename things.** `get_data` doesn't say what it does (`fetch_active_items`?), and `response` beats `r`. Use `_` for the unused loop counter.
- **Magic numbers** (`2`, `10`) should be n…
75.0s6434 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 5.3 or GPT-5.6 Terra?
- Per 1M output tokens, GLM 5.3 is ¥786.28 and GPT-5.6 Terra is ¥2,144.4 on FastMetal (yen, before tax), so GLM 5.3 is cheaper.
- How do the context windows of GLM 5.3 and GPT-5.6 Terra compare?
- GLM 5.3 takes 1,048,576 tokens; GPT-5.6 Terra takes 1,050,000.
- Can I use GLM 5.3 and GPT-5.6 Terra with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "glm-5.3" 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 5.3 and GPT-5.6 Terra from the browser chat and the API. No monthly fee.