GLM 5.3 vs GPT-5.6 Luna
Pricing, context window and real answers (August 2026)
GLM 5.3 (Z.ai) and GPT-5.6 Luna (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 Luna | |
|---|---|---|
| Provider | Z.ai | OpenAI |
| Input (per 1M tokens) | ¥250.18 | ¥35.74 |
| Output (per 1M tokens) | ¥786.28 | ¥214.44 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | ¥643 | ¥143 |
| 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 | #15 · ELO 1,484 | #63 · ELO 1,452 |
| Arena · Japanese | Unranked | #36 · ELO 1,444 |
| Arena · coding | #11 · ELO 1,531 | #69 · ELO 1,496 |
Prices are FastMetal's yen rates before tax. Arena ranks are as of the latest leaderboard scrape.
Which should you pick?
- On price, GPT-5.6 Luna: ¥214.44 per 1M output tokens, 73% less than GLM 5.3.
- On the Overall arena board, GLM 5.3 ranks higher (#15; GPT-5.6 Luna is #63).
- On the Coding arena board, GLM 5.3 ranks higher (#11; GPT-5.6 Luna is #69).
- For long documents, GPT-5.6 Luna: 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 Luna
The word **“strawberry”** is spelled:
**s – t – r – a – w – b – e – r – r – y**
The letter **“r”** appears at positions **3, 8, and 9**.
**Answer: 3**
3.0s120 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 Luna
`users` is `undefined` when `UserList` renders, so calling `.map()` on it fails. `.map()` only exists on arrays.
Common causes include:
- The parent does not pass a `users` prop.
- The prop name differs, such as `userList` instead of `users`.
- Users are loaded asynchronously and are initially undefined.
- The API response has a different shape, such as `{ data: [...] }`.
### Provide a default value
```jsx
function UserList({ users = [] }) {
return (
<ul>
{users.map(user => (
<li key={user.id}>{user.name}</li>
))}
</ul>
);
}
```
The parent should pass an array:
```jsx
<UserList users={users} />
```
If the list comes from state, initialize it as an empty…
4.8s507 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 Luna
### Main issues
- Imports should be at module level, not inside the function.
- A bare `except:` catches everything, including programming errors and `KeyboardInterrupt`.
- The function sleeps even after the final failed attempt.
- It retries all HTTP statuses, including permanent errors such as `404`.
- `status_code == 200` excludes other successful `2xx` responses.
- `if item["active"] == True` should generally be `if item.get("active") is True`.
- A `requests.Session` can reuse connections and improve performance.
- Returning `[]` for every failure makes it impossible to distinguish “no active items” from “request failed.”
- Retries should generally use backoff rather than a fixed delay.…
23.3s1978 output tokens
Frequently asked questions
- Which is cheaper, GLM 5.3 or GPT-5.6 Luna?
- Per 1M output tokens, GLM 5.3 is ¥786.28 and GPT-5.6 Luna is ¥214.44 on FastMetal (yen, before tax), so GPT-5.6 Luna is cheaper.
- How do the context windows of GLM 5.3 and GPT-5.6 Luna compare?
- GLM 5.3 takes 1,048,576 tokens; GPT-5.6 Luna takes 1,050,000.
- Which ranks higher, GLM 5.3 or GPT-5.6 Luna?
- GLM 5.3 ranks higher on the public arena (GLM 5.3 #15, GPT-5.6 Luna #36). Ranks move as the leaderboard updates.
- Can I use GLM 5.3 and GPT-5.6 Luna with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "glm-5.3" or "gpt-5.6-luna" 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 Luna from the browser chat and the API. No monthly fee.