GLM 5.3 vs GPT-6.1 Sol
Pricing, context window and real answers (September 2026)
GLM 5.3 (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 |  GPT-6.1 Sol | |
|---|---|---|
| Provider | Z.ai | OpenAI |
| Input (per 1M tokens) | ¥235.2 | ¥357.4 |
| Output (per 1M tokens) | ¥739.2 | ¥1,787 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | ¥605 | ¥1,251 |
| Context window | 1,048,576 tokens | 1,050,000 tokens |
| Release date | 8/18/2026 | 9/29/2026 |
| Input modalities | text | file, image, text |
| Throughput (p50) | — | — |
| Arena · overall | #24 · ELO 1,480 | Unranked |
| Arena · Japanese | #35 · ELO 1,447 | 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: ¥739.2 per 1M output tokens, 59% less than GPT-6.1 Sol.
- For long documents, GPT-6.1 Sol: a 1,050,000-token context window against 1,048,576 for GLM 5.3.
- 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
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
## 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
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
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
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 or GPT-6.1 Sol?
- Per 1M output tokens, GLM 5.3 is ¥739.2 and GPT-6.1 Sol is ¥1,787 on FastMetal (yen, before tax), so GLM 5.3 is cheaper.
- How do the context windows of GLM 5.3 and GPT-6.1 Sol compare?
- GLM 5.3 takes 1,048,576 tokens; GPT-6.1 Sol takes 1,050,000.
- Can I use GLM 5.3 and GPT-6.1 Sol with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "glm-5.3" 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 and GPT-6.1 Sol from the browser chat and the API. No monthly fee.