GLM 5.3 Flash vs GPT-6.1 Sol
Pricing, context window and real answers (September 2026)
GLM 5.3 Flash (Z.ai) and GPT-6.1 Sol (OpenAI), 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 Flash |  GPT-6.1 Sol | |
|---|---|---|
| Provider | Z.ai | OpenAI |
| Input (per 1M tokens) | ¥26.81 | ¥357.4 |
| Output (per 1M tokens) | ¥107.22 | ¥1,787 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | ¥80 | ¥1,251 |
| Context window | 1,310,720 tokens | 1,050,000 tokens |
| Release date | 8/26/2026 | 9/29/2026 |
| Input modalities | text, image, video | file, image, text |
| Throughput (p50) | — | — |
| Arena · overall | #35 · ELO 1,474 | Unranked |
| Arena · Japanese | #58 · ELO 1,427 | Unranked |
| Arena · coding | #28 · ELO 1,523 | Unranked |
Prices are FastMetal's yen rates before tax.
Rankings: LMArena leaderboard dataset (lmarena-ai/leaderboard-dataset), CC BY 4.0, snapshot 2026-09-25; category names mapped and ratings rounded by FastMetal.
Which should you pick?
- On price, GLM 5.3 Flash: ¥107.22 per 1M output tokens, 94% less than GPT-6.1 Sol.
- For long documents, GLM 5.3 Flash: a 1,310,720-token context window against 1,050,000 for GPT-6.1 Sol.
- 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 Flash
# Counting 'r's in 'strawberry'
**Step 1: Write out the word letter by letter**
s - t - r - a - w - b - e - r - r - y
**Step 2: Check each letter one at a time**
| Position | Letter | Is it 'r'? |
|----------|--------|-----------|
| 1 | s | No |
| 2 | t | No |
| 3 | r | **Yes! (1)** |
| 4 | a | No |
| 5 | w | No |
| 6 | b | No |
| 7 | e | No |
| 8 | r | **Yes! (2)** |
| 9 | r | **Yes! (3)** |
| 10 | y | No |
**Step 3: Add up the matches**
The 'r's appear at positions 3, 8, and 9.
## Answer: There are **3** 'r's in "strawberry" 🍓
7.2s449 output tokens
GPT-6.1 Sol
“strawberry” is spelled **s-t-r-a-w-b-e-r-r-y**.
The letter **r** appears at positions **3, 8, and 9**.
So, there are **3 r’s** in “strawberry.”
2.8s88 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 Flash
## What's happening
The error means the `users` prop is `undefined` at the time `UserList` renders. Since you can't call `.map()` on `undefined`, JavaScript throws the TypeError.
This usually happens for one of these reasons:
1. **The parent isn't passing the prop** — rendering `<UserList />` without `users`, or with a typo'd prop name like `user={users}`.
2. **Data hasn't loaded yet** — the parent fetches data asynchronously and its state starts as `undefined`:
```jsx
const [users, setUsers] = useState(); // undefined until fetch resolves!
return <UserList users={users} />;
```
3. **API response shape mismatch** — the array is nested, e.g., you need `response.data.users` inst…
31.3s1639 output tokens
GPT-6.1 Sol
The error means **`users` is `undefined` when `UserList` renders**. Calling `.map()` on `undefined` throws this error.
This commonly happens when the parent doesn’t pass the `users` prop, or when data hasn’t finished loading yet.
### Fix: provide a default empty array
```jsx
function UserList({ users = [] }) {
return (
<ul>
{users.map(user => (
<li key={user.id}>{user.name}</li>
))}
</ul>
);
}
```
This renders an empty list if `users` is omitted or `undefined`. It does **not** handle `null`.
### Check the parent component too
Make sure it passes an array:
```jsx
<UserList users={users} />
```
If you’re fetching users asynchronously, initialize the stat…
5.8s306 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 Flash
Solid foundation — you're already passing an explicit `timeout` and implementing retries, which many people skip. Here's a breakdown by category:
## Correctness & robustness (most important)
- **Bare `except:`** catches *everything*, including `KeyboardInterrupt` and `SystemExit`, and silently discards the error. Catch specific exceptions: `requests.RequestException` for network issues, `ValueError` for invalid JSON.
- **Retrying non-retryable failures**: a 401/403/404 will never succeed on retry, so you're sleeping 6 seconds for nothing. Only retry transient errors (5xx, 429, timeouts/connection errors).
- **`item["active"]` raises `KeyError`** on malformed data. Use `item.get("active")`…
116.1s6185 output tokens
GPT-6.1 Sol
The function is short, but its main weakness is that it hides failures: a network error, invalid JSON, and a genuinely empty result all return `[]`.
### Suggested improvements
- **Move imports to module scope.** This is conventional and makes dependencies visible. Repeated imports are cached, so the performance benefit is minor.
- **Avoid bare `except:`.** It catches everything, including `KeyboardInterrupt` and `SystemExit`, and can hide programming errors.
- **Handle HTTP errors explicitly.** `raise_for_status()` provides useful exceptions. Retry transient failures such as timeouts, connection errors, HTTP 429, and HTTP 5xx—not permanent errors such as HTTP 404.
- **Don’t sleep after the…
19.0s1094 output tokens
Frequently asked questions
- Which is cheaper, GLM 5.3 Flash or GPT-6.1 Sol?
- Per 1M output tokens, GLM 5.3 Flash is ¥107.22 and GPT-6.1 Sol is ¥1,787 on FastMetal (yen, before tax), so GLM 5.3 Flash is cheaper.
- How do the context windows of GLM 5.3 Flash and GPT-6.1 Sol compare?
- GLM 5.3 Flash takes 1,310,720 tokens; GPT-6.1 Sol takes 1,050,000.
- Can I use GLM 5.3 Flash and GPT-6.1 Sol with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "glm-5.3-flash" or "gpt-6.1-sol" 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 Flash and GPT-6.1 Sol from the browser chat and the API. No monthly fee.