GLM 4.7 vs Qwen3.8 27B
Pricing, context window and real answers (August 2026)
GLM 4.7 (Z.ai) and Qwen3.8 27B (Qwen), 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 4.7 |  Qwen3.8 27B | |
|---|---|---|
| Provider | Z.ai | Qwen |
| Input (per 1M tokens) | ¥107.22 | ¥80.41 |
| Output (per 1M tokens) | ¥393.14 | ¥571.84 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | ¥304 | ¥366 |
| Context window | 202,752 tokens | 1,000,000 tokens |
| Release date | 12/22/2025 | 8/14/2026 |
| Input modalities | text | text, image, video |
| Arena · overall | #79 · ELO 1,442 | #87 · ELO 1,436 |
| Arena · Japanese | Unranked | Unranked |
| Arena · coding | #87 · ELO 1,485 | #67 · ELO 1,498 |
Prices are FastMetal's yen rates before tax. Arena ranks are as of the latest leaderboard scrape.
Which should you pick?
- On price, GLM 4.7: ¥393.14 per 1M output tokens, 31% less than Qwen3.8 27B.
- On the Overall arena board, GLM 4.7 ranks higher (#79; Qwen3.8 27B is #87).
- On the Coding arena board, Qwen3.8 27B ranks higher (#67; GLM 4.7 is #87).
- For long documents, Qwen3.8 27B: a 1,000,000-token context window against 202,752 for GLM 4.7.
- 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 4.7
There are **3** "r"s in "strawberry".
Here is the step-by-step reasoning:
1. **Break down the word into individual letters:**
s - t - r - a - w - b - e - r - r - y
2. **Identify the position of each 'r':**
* The 3rd letter is **r**.
* The 8th letter is **r**.
* The 9th letter is **r**.
3. **Count the total occurrences:**
1 + 1 + 1 = 3.
23.3s685 output tokens
Qwen3.8 27B
The word is: **strawberry**
Break it into letters:
1. s
2. t
3. r
4. a
5. w
6. b
7. e
8. r
9. r
10. y
Now count the **r**’s:
- 3rd letter: **r**
- 8th letter: **r**
- 9th letter: **r**
So, there are **3** r’s in **strawberry**.
3.6s239 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 4.7
### The Cause
The error `TypeError: Cannot read properties of undefined (reading 'map')` means that the variable `users` is `undefined` when the component tries to render.
In React, this typically happens for one of two reasons:
1. The **Parent Component** is not passing the `users` prop to `UserList`.
2. The **Parent Component** passed the prop, but the data hasn't loaded yet (e.g., an API call is still pending), so the value is initially `undefined`.
Since `undefined` does not have a `.map()` method, the application crashes.
---
### How to Fix It
There are two main ways to fix this: handling it inside the `UserList` component (defensive coding) or fixing the parent component passing…
42.0s1384 output tokens
Qwen3.8 27B
The error means that `users` is `undefined` when `UserList` renders:
```jsx
{users.map(...)}
```
JavaScript cannot call `.map()` on `undefined`.
For example, this would cause the error:
```jsx
<UserList />
```
because `users` is not being passed in.
## Simple fix: give `users` a default value
```jsx
function UserList({ users = [] }) {
return (
<ul>
{users.map(user => <li key={user.id}>{user.name}</li>)}
</ul>
);
}
```
Now if `users` is not passed in, it defaults to an empty array.
## Better fix: make sure the parent passes an array
Wherever you use `UserList`, pass `users`:
```jsx
<UserList users={users} />
```
If `users` comes from an API or state, make sure it…
9.5s601 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 4.7
Here is a review of your function with suggestions for improving readability, performance, and best practices, followed by a refactored version of the code.
### Code Review
1. **Import Placement:** You import `requests` and `time` inside the function. Imports should generally be placed at the top of the file so they are loaded once when the module starts, rather than every time the function is called.
2. **Bare `except:`:** You used `except:` without specifying an exception type. This is dangerous because it catches *everything*, including `KeyboardInterrupt` (Ctrl+C) and `SystemExit`, preventing your program from terminating when needed. You should specifically catch `requests.exception…
24.5s2951 output tokens
Qwen3.8 27B
The function works at a basic level, but it has several readability, reliability, and best-practice issues.
## Main issues
1. **Imports inside the function**
```python
import requests
import time
```
Imports should normally be at the top of the module unless there is a specific reason to lazy-load them.
2. **Bare `except` is too broad**
```python
except:
time.sleep(2)
```
This catches almost everything, including `KeyboardInterrupt`, `SystemExit`, `KeyError`, `TypeError`, and other programming errors. It makes debugging harder and can retry failures that should not be retried.
3. **It retries on every non-`200` status**
Retrying `401 Unauthorized`, `4…
205.9s13103 output tokens
Frequently asked questions
- Which is cheaper, GLM 4.7 or Qwen3.8 27B?
- Per 1M output tokens, GLM 4.7 is ¥393.14 and Qwen3.8 27B is ¥571.84 on FastMetal (yen, before tax), so GLM 4.7 is cheaper.
- How do the context windows of GLM 4.7 and Qwen3.8 27B compare?
- GLM 4.7 takes 202,752 tokens; Qwen3.8 27B takes 1,000,000.
- Which ranks higher, GLM 4.7 or Qwen3.8 27B?
- GLM 4.7 ranks higher on the public arena (GLM 4.7 #79, Qwen3.8 27B #87). Ranks move as the leaderboard updates.
- Can I use GLM 4.7 and Qwen3.8 27B with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "glm-4.7" or "qwen3.8-27b" 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 4.7 and Qwen3.8 27B from the browser chat and the API. No monthly fee.