GLM 5.2 vs Kimi K3
Pricing, context window and real answers (August 2026)
GLM 5.2 (Z.ai) and Kimi K3 (Moonshot), 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 |  Kimi K3 | |
|---|---|---|
| Provider | Z.ai | Moonshot |
| Input (per 1M tokens) | ¥250.18 | ¥536.1 |
| Output (per 1M tokens) | ¥786.28 | ¥2,680.5 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | ¥643 | ¥1,876 |
| Context window | 1,048,576 tokens | 1,048,576 tokens |
| Release date | 6/16/2026 | 7/16/2026 |
| Input modalities | text | text, image |
| Arena · overall | #31 · ELO 1,469 | #11 · ELO 1,486 |
| Arena · Japanese | #13 · ELO 1,472 | Unranked |
| Arena · coding | #46 · ELO 1,506 | #8 · ELO 1,530 |
Prices are FastMetal's yen rates before tax. Arena ranks are as of the latest leaderboard scrape.
Which should you pick?
- On price, GLM 5.2: ¥786.28 per 1M output tokens, 71% less than Kimi K3.
- On the Overall arena board, Kimi K3 ranks higher (#11; GLM 5.2 is #31).
- On the Coding arena board, Kimi K3 ranks higher (#8; GLM 5.2 is #46).
- 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
Kimi K3
# Counting the 'r's in "strawberry"
**Step 1: Write out each letter with its position**
| Position | Letter |
|----------|--------|
| 1 | s |
| 2 | t |
| 3 | **r** ✓ |
| 4 | a |
| 5 | w |
| 6 | b |
| 7 | e |
| 8 | **r** ✓ |
| 9 | **r** ✓ |
| 10 | y |
**Step 2: Identify each 'r'**
- Position 3: the 'r' in "st**r**"
- Position 8: the first 'r' in "be**r**ry"
- Position 9: the second 'r' in "ber**r**y"
**Step 3: Verify by splitting the word**
"Strawberry" = "straw" + "berry"
- "straw" contains **1** r
- "berry" contains **2** r's
**Answer: There are 3 r's in "strawberry."** 🍓
12.2s594 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
Kimi K3
## What's causing it
The error means `users` is `undefined` when the component renders — `.map()` only exists on arrays, so calling it on `undefined` throws. Your component code is fine; the problem is **upstream**: whatever renders `<UserList />` isn't passing a valid array.
The most common culprit is **async data fetching with uninitialized state** in the parent:
```jsx
function App() {
const [users, setUsers] = useState(); // ← undefined!
useEffect(() => {
fetch('/api/users')
.then(res => res.json())
.then(setUsers);
}, []);
return <UserList users={users} />; // undefined on first render
}
```
The component renders *before* the fetch resolves, so `users` is `…
47.5s1756 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
Kimi K3
This works, but there are several issues worth addressing. Here's a breakdown, followed by an improved version.
## Issues
### Correctness & best practices
1. **Bare `except:`** — catches *everything*, including `KeyboardInterrupt` and programming bugs like `KeyError` on a malformed item (which then get pointlessly retried). Catch `requests.RequestException` specifically.
2. **Silent failures** — errors are swallowed with no logging, and returning `[]` makes "API is down" indistinguishable from "no active items." At minimum, log; ideally, raise after the final attempt.
3. **Retrying non-retryable errors** — a 400/401/404 will never succeed on retry, but the code sleeps and retries anyway. O…
57.6s8060 output tokens
Frequently asked questions
- Which is cheaper, GLM 5.2 or Kimi K3?
- Per 1M output tokens, GLM 5.2 is ¥786.28 and Kimi K3 is ¥2,680.5 on FastMetal (yen, before tax), so GLM 5.2 is cheaper.
- How do the context windows of GLM 5.2 and Kimi K3 compare?
- GLM 5.2 takes 1,048,576 tokens; Kimi K3 takes 1,048,576.
- Which ranks higher, GLM 5.2 or Kimi K3?
- Kimi K3 ranks higher on the public arena (GLM 5.2 #13, Kimi K3 #11). Ranks move as the leaderboard updates.
- Can I use GLM 5.2 and Kimi K3 with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "glm-5.2" or "kimi-k3" 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 Kimi K3 from the browser chat and the API. No monthly fee.