GLM 5.3 Flash vs Kimi K3.1 Pro
Pricing, context window and real answers (October 2026)
GLM 5.3 Flash (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 Flash |  Kimi K3.1 Pro | |
|---|---|---|
| Provider | Z.ai | Moonshot |
| Input (per 1M tokens) | $0.1583 | $2.11 |
| Output (per 1M tokens) | $0.5275 | $10.55 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | $0.422 | $7.39 |
| Context window | 1,310,720 tokens | 1,048,576 tokens |
| Release date | 8/26/2026 | 10/23/2026 |
| Input modalities | text, image, video | text, image, video |
| Throughput (p50) | 224 tokens/s (n=14) | — |
| Arena · overall | #35 · ELO 1,476 | Unranked |
| Arena · Japanese | #38 · ELO 1,450 | 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 Flash: $0.5275 per 1M output tokens, 95% less than Kimi K3.1 Pro.
- For long documents, GLM 5.3 Flash: a 1,310,720-token context window against 1,048,576 for 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 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
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 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
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 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
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 Flash or Kimi K3.1 Pro?
- Per 1M output tokens, GLM 5.3 Flash is $0.5275 and Kimi K3.1 Pro is $10.55 on FastMetal (US dollars, before tax), so GLM 5.3 Flash is cheaper.
- How do the context windows of GLM 5.3 Flash and Kimi K3.1 Pro compare?
- GLM 5.3 Flash takes 1,310,720 tokens; Kimi K3.1 Pro takes 1,048,576.
- Can I use GLM 5.3 Flash and Kimi K3.1 Pro with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "glm-5.3-flash" 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 Flash and Kimi K3.1 Pro from the browser chat and the API. No monthly fee.