GLM 5.3 vs Kimi K3.1 Pro
Pricing, context window and real answers (October 2026)
GLM 5.3 (Z.ai) and Kimi K3.1 Pro (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 K3.1 Pro | |
|---|---|---|
| Provider | Z.ai | Moonshot |
| Input (per 1M tokens) | $1.48 | $2.11 |
| Output (per 1M tokens) | $4.65 | $10.55 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | $3.80 | $7.39 |
| Context window | 1,048,576 tokens | 1,048,576 tokens |
| Release date | 8/18/2026 | 10/23/2026 |
| Input modalities | text | text, image, video |
| Throughput (p50) | 85 tokens/s (n=15) | — |
| Arena · overall | #26 · ELO 1,481 | Unranked |
| Arena · Japanese | #29 · ELO 1,467 | Unranked |
| Arena · coding | #27 · ELO 1,525 | Unranked |
Prices are FastMetal's rates, before tax.
Rankings: LMArena leaderboard dataset (lmarena-ai/leaderboard-dataset), CC BY 4.0, snapshot 2026-10-22; category names mapped and ratings rounded by FastMetal.
Which should you pick?
- On price, GLM 5.3: $4.65 per 1M output tokens, 56% less than Kimi K3.1 Pro.
- 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 K3.1 Pro
There are **3** ‘r’s in **“strawberry.”**
1. Write out each letter: **s – t – r – a – w – b – e – r – r – y**
2. Locate the ‘r’s: positions **3, 8, and 9**.
3. Count them: **1 + 1 + 1 = 3**.
11.3s
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 K3.1 Pro
## What's causing the error
The error means `users` is `undefined` when the component renders, and you can't call `.map()` on `undefined`. The component itself is fine — the problem is that whatever renders `<UserList />` either isn't passing the prop, or is passing a value that isn't an array yet.
The most common cause is **async data fetching**: the parent renders `UserList` before the data has loaded.
```jsx
function App() {
const [users, setUsers] = useState(); // ❌ undefined on first render
useEffect(() => {
fetch('/api/users')
.then(res => res.json())
.then(setUsers);
}, []);
return <UserList users={users} />; // users is undefined here initially
}
```
On…
36.3s
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 K3.1 Pro
The timeout and bounded retry loop are good starting points. The biggest issue is that failures—and even programming errors—are silently converted into retries and then `[]`.
### Suggested improvements
- **Avoid bare `except:`**. It catches `KeyboardInterrupt`, `KeyError`, JSON/schema errors, and other bugs. Retry only expected transient network failures; let malformed responses fail visibly.
- **Use a deliberate retry policy**. Retrying `401`, `403`, or `404` usually wastes time. Retry only transient statuses, use exponential backoff, and avoid sleeping after the final attempt.
- **Do not use `[]` for both failure and no results**. Prefer raising the final exception so callers can disting…
560.2s
Frequently asked questions
- Which is cheaper, GLM 5.3 or Kimi K3.1 Pro?
- Per 1M output tokens, GLM 5.3 is $4.65 and Kimi K3.1 Pro is $10.55 on FastMetal (US dollars, before tax), so GLM 5.3 is cheaper.
- How do the context windows of GLM 5.3 and Kimi K3.1 Pro compare?
- GLM 5.3 takes 1,048,576 tokens; Kimi K3.1 Pro takes 1,048,576.
- Can I use GLM 5.3 and Kimi K3.1 Pro with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "glm-5.3" or "kimi-k3.1-pro" 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 K3.1 Pro from the browser chat and the API. No monthly fee.