GPT-5 Mini vs Kimi K3
Pricing, context window and real answers (September 2026)
GPT-5 Mini (OpenAI) and Kimi K3 (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
 GPT-5 Mini |  Kimi K3 | |
|---|---|---|
| Provider | OpenAI | Moonshot |
| Input (per 1M tokens) | ¥44.68 | ¥504 |
| Output (per 1M tokens) | ¥357.4 | ¥2,520 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | ¥223 | ¥1,764 |
| Context window | 400,000 tokens | 1,048,576 tokens |
| Release date | 8/7/2025 | 7/16/2026 |
| Input modalities | text, image, file | text, image |
| Throughput (p50) | — | — |
| Arena · overall | #163 · ELO 1,390 | #17 · ELO 1,485 |
| Arena · Japanese | #109 · ELO 1,365 | #2 · ELO 1,511 |
| Arena · coding | #172 · ELO 1,431 | #7 · ELO 1,538 |
Prices are FastMetal's yen rates before tax. Arena ranks are as of the latest leaderboard scrape.
Which should you pick?
- On price, GPT-5 Mini: ¥357.4 per 1M output tokens, 86% less than Kimi K3.
- On the Overall arena board, Kimi K3 ranks higher (#17; GPT-5 Mini is #163).
- On the Japanese arena board, Kimi K3 ranks higher (#2; GPT-5 Mini is #109).
- On the Coding arena board, Kimi K3 ranks higher (#7; GPT-5 Mini is #172).
- For long documents, Kimi K3: a 1,048,576-token context window against 400,000 for GPT-5 Mini.
- 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.
GPT-5 Mini
We examine the word "strawberry" letter by letter:
1. s — not an r (count = 0)
2. t — not an r (count = 0)
3. r — an r (count = 1)
4. a — not an r (count = 1)
5. w — not an r (count = 1)
6. b — not an r (count = 1)
7. e — not an r (count = 1)
8. r — an r (count = 2)
9. r — an r (count = 3)
10. y — not an r (count = 3)
Total number of 'r's = 3.
5.2s382 output tokens
Kimi K3
# Counting the 'r's in "strawberry"
**Step 1: Write out each letter with its position**
| Position | Letter |
|----------|--------|
| 1 | s |
| 2 | t |
| 3 | **r** ✓ |
| 4 | a |
| 5 | w |
| 6 | b |
| 7 | e |
| 8 | **r** ✓ |
| 9 | **r** ✓ |
| 10 | y |
**Step 2: Identify each 'r'**
- Position 3: the 'r' in "st**r**"
- Position 8: the first 'r' in "be**r**ry"
- Position 9: the second 'r' in "ber**r**y"
**Step 3: Verify by splitting the word**
"Strawberry" = "straw" + "berry"
- "straw" contains **1** r
- "berry" contains **2** r's
**Answer: There are 3 r's in "strawberry."** 🍓
12.2s594 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…
GPT-5 Mini
That error means users is undefined when you call users.map(...). map is an Array method, so calling it on undefined throws "Cannot read properties of undefined (reading 'map')".
Common causes
- The parent component didn't pass a users prop.
- users is still loading (e.g. fetched asynchronously) and is null/undefined until the fetch completes.
- The parent initialized users to null instead of an empty array.
How to fix
Choose one or more of these approaches depending on your app:
1) Initialize the parent state as an array
If users is state in the parent, initialize it to [] so the child always receives an array:
const [users, setUsers] = useState([]);
// fetch and setUsers(data)
2) Give…
8.0s966 output tokens
Kimi K3
## What's causing it
The error means `users` is `undefined` when the component renders — `.map()` only exists on arrays, so calling it on `undefined` throws. Your component code is fine; the problem is **upstream**: whatever renders `<UserList />` isn't passing a valid array.
The most common culprit is **async data fetching with uninitialized state** in the parent:
```jsx
function App() {
const [users, setUsers] = useState(); // ← undefined!
useEffect(() => {
fetch('/api/users')
.then(res => res.json())
.then(setUsers);
}, []);
return <UserList users={users} />; // undefined on first render
}
```
The component renders *before* the fetch resolves, so `users` is `…
47.5s1756 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):…
GPT-5 Mini
Thanks — this is a nice simple function, but it can be improved in several ways: move imports to module scope, avoid broad except:, use connection pooling (Session) for performance, use specific exception types, add logging and docstring, avoid comparing to True, use list comprehensions, validate JSON shape, and implement backoff (or use urllib3 Retry).
Below are two improved alternatives and a short rationale for the changes.
1) A simple, clear improvement (small changes, explicit errors, backoff + jitter)
- Moves imports to top-level
- Uses requests.Session for connection pooling
- Catches specific exceptions (requests.RequestException, ValueError)
- Uses list comprehension and item.get(…
13.6s1702 output tokens
Kimi K3
This works, but there are several issues worth addressing. Here's a breakdown, followed by an improved version.
## Issues
### Correctness & best practices
1. **Bare `except:`** — catches *everything*, including `KeyboardInterrupt` and programming bugs like `KeyError` on a malformed item (which then get pointlessly retried). Catch `requests.RequestException` specifically.
2. **Silent failures** — errors are swallowed with no logging, and returning `[]` makes "API is down" indistinguishable from "no active items." At minimum, log; ideally, raise after the final attempt.
3. **Retrying non-retryable errors** — a 400/401/404 will never succeed on retry, but the code sleeps and retries anyway. O…
57.6s8060 output tokens
Frequently asked questions
- Which is cheaper, GPT-5 Mini or Kimi K3?
- Per 1M output tokens, GPT-5 Mini is ¥357.4 and Kimi K3 is ¥2,520 on FastMetal (yen, before tax), so GPT-5 Mini is cheaper.
- How do the context windows of GPT-5 Mini and Kimi K3 compare?
- GPT-5 Mini takes 400,000 tokens; Kimi K3 takes 1,048,576.
- Which ranks higher, GPT-5 Mini or Kimi K3?
- Kimi K3 ranks higher on the public arena (GPT-5 Mini #109, Kimi K3 #2). Ranks move as the leaderboard updates.
- Can I use GPT-5 Mini and Kimi K3 with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "gpt-5-mini" or "kimi-k3" 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 GPT-5 Mini and Kimi K3 from the browser chat and the API. No monthly fee.