GLM 5.3 FlashX vs GPT-5.4 Nano
Pricing, context window and real answers (September 2026)
GLM 5.3 FlashX (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 5.3 FlashX |  GPT-5.4 Nano | |
|---|---|---|
| Provider | Z.ai | OpenAI |
| Input (per 1M tokens) | ¥66.12 | ¥35.74 |
| Output (per 1M tokens) | ¥223.38 | ¥223.38 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | ¥178 | ¥147 |
| Context window | 1,048,576 tokens | 400,000 tokens |
| Release date | 9/18/2026 | 3/17/2026 |
| Input modalities | text, image, video | file, image, text |
| Throughput (p50) | — | — |
| Arena · overall | Unranked | #146 · ELO 1,402 |
| Arena · Japanese | Unranked | #107 · ELO 1,368 |
| Arena · coding | Unranked | #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?
- Output pricing is identical (¥223.38 per 1M tokens).
- For long documents, GLM 5.3 FlashX: 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 5.3 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-5.4 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 5.3 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-5.4 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 5.3 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-5.4 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 5.3 FlashX or GPT-5.4 Nano?
- Both are ¥223.38 per 1M output tokens on FastMetal (yen, before tax).
- How do the context windows of GLM 5.3 FlashX and GPT-5.4 Nano compare?
- GLM 5.3 FlashX takes 1,048,576 tokens; GPT-5.4 Nano takes 400,000.
- Can I use GLM 5.3 FlashX and GPT-5.4 Nano with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "glm-5.3-flashx" 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 5.3 FlashX and GPT-5.4 Nano from the browser chat and the API. No monthly fee.