GLM 5.3 FlashX vs GPT-5.6 Terra
Pricing, context window and real answers (September 2026)
GLM 5.3 FlashX (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 FlashX |  GPT-5.6 Terra | |
|---|---|---|
| Provider | Z.ai | OpenAI |
| Input (per 1M tokens) | ¥66.12 | ¥357.4 |
| Output (per 1M tokens) | ¥223.38 | ¥2,144.4 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | ¥178 | ¥1,430 |
| Context window | 1,048,576 tokens | 1,050,000 tokens |
| Release date | 9/18/2026 | 7/9/2026 |
| Input modalities | text, image, video | file, image, text |
| Throughput (p50) | — | — |
| Arena · overall | Unranked | #45 · ELO 1,466 |
| Arena · Japanese | Unranked | #20 · ELO 1,467 |
| Arena · coding | Unranked | #35 · ELO 1,518 |
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 FlashX: ¥223.38 per 1M output tokens, 90% 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 FlashX.
- 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 FlashX
# Counting 'r's in "strawberry"
**Step 1: Write out each letter with its position**
| Position | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
|----------|---|---|---|---|---|---|---|---|---|----|
| Letter | s | t | r | a | w | b | e | r | r | y |
**Step 2: Check each letter one by one**
- Position 1: **s** → not an 'r'
- Position 2: **t** → not an 'r'
- Position 3: **r** → ✅ 'r' found (count: 1)
- Position 4: **a** → not an 'r'
- Position 5: **w** → not an 'r'
- Position 6: **b** → not an 'r'
- Position 7: **e** → not an 'r'
- Position 8: **r** → ✅ 'r' found (count: 2)
- Position 9: **r** → ✅ 'r' found (count: 3)
- Position 10: **y** → not an 'r'
**Step 3: Tally the results**
The 'r's a…
4.1s549 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 FlashX
## What's Happening
The error means `users` is `undefined` at the time `UserList` renders. You're calling `.map()` on `undefined`, which JavaScript doesn't allow. This almost always happens for one of these reasons:
1. **The parent isn't passing the prop** — `<UserList />` with no `users` prop
2. **Data hasn't loaded yet** — you're fetching asynchronously, so the first render happens before the data arrives
3. **Prop name mismatch** — e.g., passing `user` but reading `users`
4. **Parent state initialized to `undefined`** — e.g., `useState()` instead of `useState([])`
## Fixes
### Option 1: Default parameter (simplest)
```jsx
function UserList({ users = [] }) {
return (
<ul>…
6.8s934 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 FlashX
# Code Review
The function works, but there are several issues worth addressing — including one subtle bug caused by the bare `except`.
## Correctness issues (most important)
1. **Bare `except:` is dangerous and masks a bug.** It catches *everything*, including `KeyboardInterrupt` and `SystemExit` (so Ctrl+C during a retry sleep is swallowed). Worse: because your data-processing loop sits inside the `try`, a `KeyError` from a malformed item is treated as a network failure — triggering a pointless re-download of the URL and silently discarding the real error. Catch only `requests.RequestException` (and `ValueError` for JSON decode failures).
2. **Silent failure.** Returning `[]` on error…
36.9s5836 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 FlashX or GPT-5.6 Terra?
- Per 1M output tokens, GLM 5.3 FlashX is ¥223.38 and GPT-5.6 Terra is ¥2,144.4 on FastMetal (yen, before tax), so GLM 5.3 FlashX is cheaper.
- How do the context windows of GLM 5.3 FlashX and GPT-5.6 Terra compare?
- GLM 5.3 FlashX takes 1,048,576 tokens; GPT-5.6 Terra takes 1,050,000.
- Can I use GLM 5.3 FlashX and GPT-5.6 Terra with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "glm-5.3-flashx" 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 FlashX and GPT-5.6 Terra from the browser chat and the API. No monthly fee.