GPT-4.1 Nano vs Kimi K3.1 Pro
Pricing, context window and real answers (October 2026)
GPT-4.1 Nano (OpenAI) 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
 GPT-4.1 Nano |  Kimi K3.1 Pro | |
|---|---|---|
| Provider | OpenAI | Moonshot |
| Input (per 1M tokens) | $0.11 | $2.11 |
| Output (per 1M tokens) | $0.44 | $10.55 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | $0.33 | $7.39 |
| Context window | 1,047,576 tokens | 1,048,576 tokens |
| Release date | 4/14/2025 | 10/23/2026 |
| Input modalities | image, text, file | text, image, video |
| Throughput (p50) | 220 tokens/s (n=27) | — |
| Arena · overall | #259 · ELO 1,322 | Unranked |
| Arena · Japanese | #214 · ELO 1,233 | Unranked |
| Arena · coding | #242 · ELO 1,373 | 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, GPT-4.1 Nano: $0.44 per 1M output tokens, 96% less than Kimi K3.1 Pro.
- For long documents, Kimi K3.1 Pro: a 1,048,576-token context window against 1,047,576 for GPT-4.1 Nano.
- 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-4.1 Nano
Let's analyze the word "strawberry" step by step to count the number of 'r's.
1. Write down the word: **strawberry**
2. Examine each letter one by one:
- s: no
- t: no
- r: yes (first 'r')
- a: no
- w: no
- b: no
- e: no
- r: yes (second 'r')
- r: yes (third 'r')
- y: no
3. Counting the 'r's found:
- First 'r' (at position 3)
- Second 'r' (at position 8)
- Third 'r' (at position 9)
So, there are **3** 'r's in "strawberry."
**Final answer: 3**
2.0s183 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…
GPT-4.1 Nano
The error:
```
TypeError: Cannot read properties of undefined (reading 'map')
```
indicates that `users` is `undefined` at the time your `UserList` component is rendering, and you're trying to call `map` on it.
### Why is this happening?
In React, if a prop (`users` in this case) is not provided or is undefined, trying to do `users.map(...)` causes this error. This can happen if:
- The parent component does not pass `users`.
- `users` might be `null` or `undefined` initially, especially if data fetching is involved.
### How to fix it
**Option 1: Provide a default value for `users`**
Ensure that `users` defaults to an empty array if it is not provided:
```jsx
function UserList({ users…
4.3s514 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):…
GPT-4.1 Nano
Here's a review and an improved version of your function:
### Key improvements:
1. **Move imports to the top**: Imports should generally be at the top of the file, not inside functions.
2. **Specify exception types**: Catch specific exceptions rather than a generic `except`.
3. **Use context managers if needed**: Not necessary here, but good to consider.
4. **Avoid repeated code**: The sleep statement is the same whether there is a request exception or a non-200 status.
5. **Simplify filtering**: Use list comprehensions for clarity.
6. **Improve variable names**: For readability.
7. **Add optional headers or parameters** if needed (not required here).
8. **Optional:** Add more descriptive e…
3.4s391 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, GPT-4.1 Nano or Kimi K3.1 Pro?
- Per 1M output tokens, GPT-4.1 Nano is $0.44 and Kimi K3.1 Pro is $10.55 on FastMetal (US dollars, before tax), so GPT-4.1 Nano is cheaper.
- How do the context windows of GPT-4.1 Nano and Kimi K3.1 Pro compare?
- GPT-4.1 Nano takes 1,047,576 tokens; Kimi K3.1 Pro takes 1,048,576.
- Can I use GPT-4.1 Nano and Kimi K3.1 Pro with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "gpt-4.1-nano" 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 GPT-4.1 Nano and Kimi K3.1 Pro from the browser chat and the API. No monthly fee.