GLM 5.2 vs GPT-5.4 Nano
Pricing, context window and real answers (September 2026)
GLM 5.2 (Z.ai) and GPT-5.4 Nano (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-5.4 Nano | |
|---|---|---|
| Provider | Z.ai | OpenAI |
| Input (per 1M tokens) | ¥235.2 | ¥35.74 |
| Output (per 1M tokens) | ¥739.2 | ¥223.38 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | ¥605 | ¥147 |
| Context window | 1,048,576 tokens | 400,000 tokens |
| Release date | 6/16/2026 | 3/17/2026 |
| Input modalities | text | file, image, text |
| Throughput (p50) | — | — |
| Arena · overall | #38 · ELO 1,472 | #146 · ELO 1,402 |
| Arena · Japanese | #28 · ELO 1,452 | #107 · ELO 1,368 |
| Arena · coding | #48 · ELO 1,510 | #128 · ELO 1,460 |
Prices are FastMetal's yen rates before tax. Arena ranks are as of the latest leaderboard scrape.
Which should you pick?
- On price, GPT-5.4 Nano: ¥223.38 per 1M output tokens, 70% less than GLM 5.2.
- On the Overall arena board, GLM 5.2 ranks higher (#38; GPT-5.4 Nano is #146).
- On the Japanese arena board, GLM 5.2 ranks higher (#28; GPT-5.4 Nano is #107).
- On the Coding arena board, GLM 5.2 ranks higher (#48; GPT-5.4 Nano is #128).
- For long documents, GLM 5.2: a 1,048,576-token context window against 400,000 for GPT-5.4 Nano.
- 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-5.4 Nano
**Step 1:** Write the word: **strawberry**
**Step 2:** Count each letter **r** in the word:
- s **t r** a w b e **r r** y
There are **3** **r**’s in total (**one** after the **t**, and **two** near the end).
✅ **Answer: 3**
1.5s86 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-5.4 Nano
This error means that **`users` is `undefined` (or otherwise not an array)** when your component renders, so calling `users.map(...)` throws:
```js
users.map(...)
^^^^ reading 'map' of undefined
```
### Why it happens
Common causes:
- You render `<UserList />` before data has loaded (e.g., `users` comes from an async fetch).
- The parent component passes the wrong prop name (e.g., `user` instead of `users`).
- The API/parent state initializes `users` as `undefined` rather than `[]`.
- You’re destructuring a prop incorrectly upstream.
### Fix options
#### 1) Ensure `users` is always an array (recommended)
Initialize/default it in the component:
```js
function UserList({ users = []…
3.3s383 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-5.4 Nano
Here are key improvements (readability, performance, robustness, best practices) and a revised version of your function.
## What to improve
1. **Avoid bare `except:`**
Catch specific exceptions from `requests` (e.g., `requests.exceptions.RequestException`) to avoid hiding bugs.
2. **Prefer `raise_for_status()` or check status codes clearly**
`r.raise_for_status()` is a common pattern.
3. **Use exponential backoff (or at least a clear retry delay)**
Instead of sleeping the same amount every time, consider increasing delay.
4. **Reuse a `requests.Session()` (optional but best practice)**
If this function is called repeatedly, passing a session or creating one outside i…
4.4s718 output tokens
Frequently asked questions
- Which is cheaper, GLM 5.2 or GPT-5.4 Nano?
- Per 1M output tokens, GLM 5.2 is ¥739.2 and GPT-5.4 Nano is ¥223.38 on FastMetal (yen, before tax), so GPT-5.4 Nano is cheaper.
- How do the context windows of GLM 5.2 and GPT-5.4 Nano compare?
- GLM 5.2 takes 1,048,576 tokens; GPT-5.4 Nano takes 400,000.
- Which ranks higher, GLM 5.2 or GPT-5.4 Nano?
- GLM 5.2 ranks higher on the public arena (GLM 5.2 #28, GPT-5.4 Nano #107). Ranks move as the leaderboard updates.
- Can I use GLM 5.2 and GPT-5.4 Nano with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "glm-5.2" or "gpt-5.4-nano" 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-5.4 Nano from the browser chat and the API. No monthly fee.