GLM 4.7 vs Llama 3.1 8B Instruct
Pricing, context window and real answers (September 2026)
GLM 4.7 (Z.ai) and Llama 3.1 8B Instruct (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 4.7 |  Llama 3.1 8B Instruct | |
|---|---|---|
| Provider | Z.ai | Meta |
| Input (per 1M tokens) | ¥107.22 | ¥8.94 |
| Output (per 1M tokens) | ¥393.14 | ¥14.3 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | ¥304 | ¥16 |
| Context window | 202,752 tokens | 131,072 tokens |
| Release date | 12/22/2025 | 7/23/2024 |
| Input modalities | text | text |
| Throughput (p50) | — | — |
| Arena · overall | #83 · ELO 1,442 | #329 · ELO 1,211 |
| Arena · Japanese | Unranked | #243 · ELO 1,087 |
| Arena · coding | #91 · ELO 1,485 | #323 · ELO 1,260 |
Prices are FastMetal's yen rates before tax. Arena ranks are as of the latest leaderboard scrape.
Which should you pick?
- On price, Llama 3.1 8B Instruct: ¥14.3 per 1M output tokens, 96% less than GLM 4.7.
- On the Overall arena board, GLM 4.7 ranks higher (#83; Llama 3.1 8B Instruct is #329).
- On the Coding arena board, GLM 4.7 ranks higher (#91; Llama 3.1 8B Instruct is #323).
- For long documents, GLM 4.7: a 202,752-token context window against 131,072 for Llama 3.1 8B Instruct.
- 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
Llama 3.1 8B Instruct
To count the number of 'r's in 'strawberry', I will break down the word into its individual letters and examine each one. Here's the step-by-step process:
1. The word 'strawberry' consists of the following letters: s-t-r-a-w-b-e-r-r-y.
2. I will start by counting the letters that are not 'r'. The word has 1 's', 1 't', 1 'a', 1 'w', 1 'b', 1 'e', 1 'y'.
3. Next, I will count the number of 'r's. The word has 2 'r's, one of which is part of the letter combination 'rr'.
Therefore, there are 2 'r's in the word 'strawberry'.
9.7s169 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
Llama 3.1 8B Instruct
The error `TypeError: Cannot read properties of undefined (reading 'map')` typically occurs when you're trying to access a property (in this case, `map`) on an object that is `undefined`.
In your case, the `users` prop passed to the `UserList` component is `undefined`. This is likely because the parent component that renders `UserList` is not passing the `users` prop, or the prop is not being properly initialized.
To fix this error, you can add a simple check to ensure that `users` is not `undefined` before trying to call `map` on it:
```javascript
function UserList({ users }) {
if (!users) return null; // or return a default message, e.g. "No users found"
return (
<ul>
{us…
7.9s292 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
Llama 3.1 8B Instruct
Here are some suggestions for improving the function's readability, performance, and best practices:
**Readability Improvements**
1. **Function name**: Consider renaming `get_data` to `fetch_active_items` to better reflect its purpose.
2. **Variable naming**: Use more descriptive variable names, such as `response` instead of `r`, and `active_items` instead of `result`.
3. **Code organization**: Break up the function into smaller, more focused sections. This will improve readability and make it easier to understand the logic.
4. **Type hints**: Add type hints for function parameters and return types to make the code more self-documenting.
**Performance Improvements**
1. **Use a more effic…
29.7s533 output tokens
Frequently asked questions
- Which is cheaper, GLM 4.7 or Llama 3.1 8B Instruct?
- Per 1M output tokens, GLM 4.7 is ¥393.14 and Llama 3.1 8B Instruct is ¥14.3 on FastMetal (yen, before tax), so Llama 3.1 8B Instruct is cheaper.
- How do the context windows of GLM 4.7 and Llama 3.1 8B Instruct compare?
- GLM 4.7 takes 202,752 tokens; Llama 3.1 8B Instruct takes 131,072.
- Which ranks higher, GLM 4.7 or Llama 3.1 8B Instruct?
- GLM 4.7 ranks higher on the public arena (GLM 4.7 #83, Llama 3.1 8B Instruct #243). Ranks move as the leaderboard updates.
- Can I use GLM 4.7 and Llama 3.1 8B Instruct with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "glm-4.7" or "llama-3.1-8b-instruct" 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 Llama 3.1 8B Instruct from the browser chat and the API. No monthly fee.