GLM 5.3 vs Kimi K2.6
Pricing, context window and real answers (August 2026)
GLM 5.3 (Z.ai) and Kimi K2.6 (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 5.3 |  Kimi K2.6 | |
|---|---|---|
| Provider | Z.ai | Moonshot |
| Input (per 1M tokens) | ¥250.18 | ¥169.77 |
| Output (per 1M tokens) | ¥786.28 | ¥714.8 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | ¥643 | ¥527 |
| Context window | 1,048,576 tokens | 262,144 tokens |
| Release date | 8/18/2026 | 4/20/2026 |
| Input modalities | text | text, image |
| Arena · overall | Unranked | #41 · ELO 1,461 |
| Arena · Japanese | Unranked | #25 · ELO 1,449 |
| Arena · coding | Unranked | #30 · ELO 1,515 |
Prices are FastMetal's yen rates before tax. Arena ranks are as of the latest leaderboard scrape.
Which should you pick?
- On price, Kimi K2.6: ¥714.8 per 1M output tokens, 9% less than GLM 5.3.
- For long documents, GLM 5.3: a 1,048,576-token context window against 262,144 for Kimi K2.6.
- 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
# Counting 'r's in "strawberry"
**Step 1: Spell out the word letter by letter.**
s - t - r - a - w - b - e - r - r - y
**Step 2: Go through each letter and mark the 'r's.**
| Position | Letter | Is it 'r'? |
|----------|--------|------------|
| 1 | s | No |
| 2 | t | No |
| 3 | **r** | ✅ Yes (1st) |
| 4 | a | No |
| 5 | w | No |
| 6 | b | No |
| 7 | e | No |
| 8 | **r** | ✅ Yes (2nd) |
| 9 | **r** | ✅ Yes (3rd) |
| 10 | y | No |
**Step 3: Tally the results.**
- 1st 'r' in "st**r**awberry"
- 2nd 'r' in "strawbe**r**ry"
- 3rd 'r' in "strawber**r**y"
## Answer: There are **3** 'r's in "strawberry."
(Note: two of them appear together in the "rr" near the end, which is easy to miss when c…
5.8s493 output tokens
Kimi K2.6
Here is the step-by-step count:
**Step 1:** Write out the word and number each letter.
1. s
2. t
3. **r** ← 1st 'r'
4. a
5. w
6. b
7. e
8. **r** ← 2nd 'r'
9. **r** ← 3rd 'r'
10. y
**Step 2:** Identify the positions containing the letter 'r'.
- The 3rd letter is **r**
- The 8th letter is **r**
- The 9th letter is **r**
**Step 3:** Tally the total.
There are **3** letter 'r's in "strawberry".
4.5s352 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
## What's happening
The error means `users` is `undefined` when the component renders. `undefined` doesn't have a `.map()` method, so JavaScript throws a `TypeError`. Since destructured props that aren't passed default to `undefined`, something upstream isn't providing the value you expect.
## Common causes
1. **The parent doesn't pass the prop, or uses a different name:**
```jsx
<UserList /> {/* missing users */}
<UserList userList={users} /> {/* prop name mismatch */}
```
2. **Async data hasn't loaded yet** — the component renders before an API call completes, and the initial value is `undefined`:
```jsx
const [users, setUsers] = useState(); // undefin…
17.1s1345 output tokens
Kimi K2.6
The error occurs because `users` is `undefined` when your component renders. The `.map()` method only works on arrays, so calling it on `undefined` throws this error.
This usually happens when:
- The parent component hasn't finished fetching data yet
- The `users` prop isn't being passed at all
- The prop is misspelled in the parent component
## Quick Fix
Set a **default value** for the `users` prop:
```jsx
function UserList({ users = [] }) {
return (
<ul>
{users.map(user => <li key={user.id}>{user.name}</li>)}
</ul>
);
}
```
## Alternative Fixes
**Optional chaining** (renders nothing if undefined):
```jsx
{users?.map(user => <li key={user.id}>{user.name}</li>)}
```…
71.8s864 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
Here's a breakdown of the issues, followed by a refactored version.
## Readability
- **Move imports to module level.** PEP 8 requires top-level imports; importing inside a function hides dependencies and adds overhead on every call (small, but pointless).
- **`item["active"] == True`** → just `item.get("active")`. Comparing to `True` with `==` is flagged by PEP 8, and `.get()` avoids a `KeyError` if the key is missing.
- **Use a list comprehension** instead of initializing a list and appending in a loop.
- **Rename things.** `get_data` doesn't say what it does (`fetch_active_items`?), and `response` beats `r`. Use `_` for the unused loop counter.
- **Magic numbers** (`2`, `10`) should be n…
75.0s6434 output tokens
Kimi K2.6
Here is a refactored version followed by a breakdown of the improvements.
### Refactored Function
```python
import logging
import time
from typing import Any
import requests
logger = logging.getLogger(__name__)
def fetch_active_items(
url: str,
*,
retries: int = 3,
timeout: float = 10.0,
backoff: float = 2.0,
) -> list[dict[str, Any]]:
"""Fetch active items from an API endpoint with retry logic.
Args:
url: The API endpoint URL.
retries: Number of retry attempts for transient failures.
timeout: HTTP request timeout in seconds.
backoff: Base delay in seconds between retries (linear backoff).
Returns:
A list of activ…
17.9s4061 output tokens
Frequently asked questions
- Which is cheaper, GLM 5.3 or Kimi K2.6?
- Per 1M output tokens, GLM 5.3 is ¥786.28 and Kimi K2.6 is ¥714.8 on FastMetal (yen, before tax), so Kimi K2.6 is cheaper.
- How do the context windows of GLM 5.3 and Kimi K2.6 compare?
- GLM 5.3 takes 1,048,576 tokens; Kimi K2.6 takes 262,144.
- Can I use GLM 5.3 and Kimi K2.6 with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "glm-5.3" or "kimi-k2.6" 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 and Kimi K2.6 from the browser chat and the API. No monthly fee.