GLM 4.7 vs Kimi K2.6
Pricing, context window and real answers (August 2026)
GLM 4.7 (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 4.7 |  Kimi K2.6 | |
|---|---|---|
| Provider | Z.ai | Moonshot |
| Input (per 1M tokens) | ¥107.22 | ¥169.77 |
| Output (per 1M tokens) | ¥393.14 | ¥714.8 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | ¥304 | ¥527 |
| Context window | 202,752 tokens | 262,144 tokens |
| Release date | 12/22/2025 | 4/20/2026 |
| Input modalities | text | text, image |
| Arena · overall | #79 · ELO 1,442 | #49 · ELO 1,461 |
| Arena · Japanese | Unranked | #35 · ELO 1,445 |
| Arena · coding | #87 · ELO 1,485 | #34 · ELO 1,514 |
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, 45% less than Kimi K2.6.
- On the Overall arena board, Kimi K2.6 ranks higher (#49; GLM 4.7 is #79).
- On the Coding arena board, Kimi K2.6 ranks higher (#34; GLM 4.7 is #87).
- For long documents, Kimi K2.6: a 262,144-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
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 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
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 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
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 4.7 or Kimi K2.6?
- Per 1M output tokens, GLM 4.7 is ¥393.14 and Kimi K2.6 is ¥714.8 on FastMetal (yen, before tax), so GLM 4.7 is cheaper.
- How do the context windows of GLM 4.7 and Kimi K2.6 compare?
- GLM 4.7 takes 202,752 tokens; Kimi K2.6 takes 262,144.
- Which ranks higher, GLM 4.7 or Kimi K2.6?
- Kimi K2.6 ranks higher on the public arena (GLM 4.7 #79, Kimi K2.6 #35). Ranks move as the leaderboard updates.
- Can I use GLM 4.7 and Kimi K2.6 with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "glm-4.7" 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 4.7 and Kimi K2.6 from the browser chat and the API. No monthly fee.