GLM 5.1 vs Muse Spark 1.3
Pricing, context window and real answers (September 2026)
GLM 5.1 (Z.ai) and Muse Spark 1.3 (Meta), 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 5.1 |  Muse Spark 1.3 | |
|---|---|---|
| Provider | Z.ai | Meta |
| Input (per 1M tokens) | ¥250.18 | ¥219.9 |
| Output (per 1M tokens) | ¥786.28 | ¥747.65 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | ¥643 | ¥594 |
| Context window | 204,800 tokens | 1,048,576 tokens |
| Release date | 4/7/2026 | 9/2/2026 |
| Input modalities | text | text, image, video, file, audio |
| Arena · overall | #44 · ELO 1,466 | Unranked |
| Arena · Japanese | #45 · ELO 1,434 | Unranked |
| Arena · coding | #37 · ELO 1,514 | Unranked |
Prices are FastMetal's yen rates before tax. Arena ranks are as of the latest leaderboard scrape.
Which should you pick?
- On price, Muse Spark 1.3: ¥747.65 per 1M output tokens, 5% less than GLM 5.1.
- For long documents, Muse Spark 1.3: a 1,048,576-token context window against 204,800 for GLM 5.1.
- 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 5.1
There are **3** 'r's in the word "strawberry".
Here is the step-by-step reasoning:
1. Let's break down the word "strawberry" letter by letter: s - t - r - a - w - b - e - r - r - y
2. Now, let's check each letter one by one to see if it is an 'r':
- **s**: not an 'r'
- **t**: not an 'r'
- **r**: this is the 1st 'r'
- **a**: not an 'r'
- **w**: not an 'r'
- **b**: not an 'r'
- **e**: not an 'r'
- **r**: this is the 2nd 'r'
- **r**: this is the 3rd 'r'
- **y**: not an 'r'
3. Counting them up, there are three 'r's in total (in the 3rd, 8th, and 9th positions).
7.3s565 output tokens
Muse Spark 1.3
The word is **strawberry** — there are **3** 'r's in it.
Here's my reasoning step by step:
1. **Write out the word:**
strawberry
2. **Break it into individual letters:**
s - t - r - a - w - b - e - r - r - y
3. **Go through each letter one by one and check if it is 'r':**
1. s → not r, count = 0
2. t → not r, count = 0
3. r → is r, count = 1
4. a → not r, count = 1
5. w → not r, count = 1
6. b → not r, count = 1
7. e → not r, count = 1
8. r → is r, count = 2
9. r → is r, count = 3
10. y → not r, count = 3
4. **Total up the matches:**
The 'r' appears at position 3, 8, and 9.
**Final answer: 3**
10.5s1063 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 5.1
### What is causing the error?
The error `TypeError: Cannot read properties of undefined (reading 'map')` means you are trying to call the `.map()` method on a variable that is `undefined`.
In your code, the variable is `users`. When the `UserList` component renders, the `users` prop is `undefined`, so `undefined.map()` throws an error.
### Why is `users` undefined?
This usually happens for one of two reasons:
1. **You forgot to pass the prop:** The parent component rendering `<UserList />` is not passing the `users` attribute (e.g., `<UserList />` instead of `<UserList users={myUsers} />`).
2. **Async Data Fetching:** You are fetching the users from an API. On the initial render, befor…
45.0s1680 output tokens
Muse Spark 1.3
This error means `users` is `undefined` when your component tries to render.
`users.map(...)` only works if `users` is an array. On the first render, if you haven't passed the prop yet, or your data hasn't loaded from an API yet, `users` is `undefined`, so JavaScript throws:
> Cannot read properties of undefined (reading 'map')
This almost always happens in one of these 2 cases:
1. You forgot to pass the prop:
```js
<UserList /> // users is undefined!
```
vs.
```js
<UserList users={myUsers} />
```
2. Your data is async and is `undefined` on the first render:
```js
const [users, setUsers] = useState(); // undefined at first!
// ...
<UserList users={users} />
```
### How to fix it
**1.…
16.7s1242 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 5.1
Here is a review of your function, broken down by areas for improvement, followed by a refactored version of the code.
### Key Areas for Improvement
1. **Imports Inside the Function:**
Placing `import` statements inside a function is generally discouraged in Python. It slows down the function execution every time it is called, and it violates PEP 8 (the Python style guide). Imports should always be at the top of the module.
2. **Bare `except` Clause:**
Using a bare `except:` catches *everything*, including `KeyboardInterrupt` and `SystemExit`, which makes it hard to terminate a hanging program. You should catch specific exceptions (like `requests.exceptions.RequestException`).
3. *…
70.3s2854 output tokens
Muse Spark 1.3
Here's a friendly review of your `get_data` function — it works, but there are a few readability, robustness, and performance issues worth fixing:
### 1. Readability Issues
**a) Imports inside the function**
```python
import requests
import time
```
This runs on every call (though Python caches it). Move to the top of the file per PEP 8.
**b) Poor variable names & magic numbers**
`r`, `i`, `data`, `2`, `10`, `200` don't tell the reader much.
**c) `== True` anti-pattern**
```python
if item["active"] == True:
```
Just use `if item.get("active"):` or `is True`. The current version will also crash with `KeyError` if `active` is missing.
**d) No docstring / type hints**
No one knows what `ur…
26.9s2141 output tokens
Frequently asked questions
- Which is cheaper, GLM 5.1 or Muse Spark 1.3?
- Per 1M output tokens, GLM 5.1 is ¥786.28 and Muse Spark 1.3 is ¥747.65 on FastMetal (yen, before tax), so Muse Spark 1.3 is cheaper.
- How do the context windows of GLM 5.1 and Muse Spark 1.3 compare?
- GLM 5.1 takes 204,800 tokens; Muse Spark 1.3 takes 1,048,576.
- Can I use GLM 5.1 and Muse Spark 1.3 with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "glm-5.1" or "muse-spark-1.3" 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 5.1 and Muse Spark 1.3 from the browser chat and the API. No monthly fee.