GPT-4.1 Nano vs GPT-5.6 Luna
Pricing, context window and real answers (September 2026)
GPT-4.1 Nano (OpenAI) and GPT-5.6 Luna (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
 GPT-4.1 Nano | GPT-5.6 Luna | |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Input (per 1M tokens) | ¥17.87 | ¥35.74 |
| Output (per 1M tokens) | ¥71.48 | ¥214.44 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | ¥54 | ¥143 |
| Context window | 1,047,576 tokens | 1,050,000 tokens |
| Release date | 4/14/2025 | 7/9/2026 |
| Input modalities | image, text, file | file, image, text |
| Throughput (p50) | — | — |
| Arena · overall | #243 · ELO 1,322 | #67 · ELO 1,452 |
| Arena · Japanese | #195 · ELO 1,234 | #42 · ELO 1,439 |
| Arena · coding | #226 · ELO 1,374 | #71 · ELO 1,498 |
Prices are FastMetal's yen rates before tax. Arena ranks are as of the latest leaderboard scrape.
Which should you pick?
- On price, GPT-4.1 Nano: ¥71.48 per 1M output tokens, 67% less than GPT-5.6 Luna.
- On the Overall arena board, GPT-5.6 Luna ranks higher (#67; GPT-4.1 Nano is #243).
- On the Japanese arena board, GPT-5.6 Luna ranks higher (#42; GPT-4.1 Nano is #195).
- On the Coding arena board, GPT-5.6 Luna ranks higher (#71; GPT-4.1 Nano is #226).
- For long documents, GPT-5.6 Luna: a 1,050,000-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
GPT-5.6 Luna
The word **“strawberry”** is spelled:
**s – t – r – a – w – b – e – r – r – y**
The letter **“r”** appears at positions **3, 8, and 9**.
**Answer: 3**
3.0s120 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-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
GPT-5.6 Luna
`users` is `undefined` when `UserList` renders, so calling `.map()` on it fails. `.map()` only exists on arrays.
Common causes include:
- The parent does not pass a `users` prop.
- The prop name differs, such as `userList` instead of `users`.
- Users are loaded asynchronously and are initially undefined.
- The API response has a different shape, such as `{ data: [...] }`.
### Provide a default value
```jsx
function UserList({ users = [] }) {
return (
<ul>
{users.map(user => (
<li key={user.id}>{user.name}</li>
))}
</ul>
);
}
```
The parent should pass an array:
```jsx
<UserList users={users} />
```
If the list comes from state, initialize it as an empty…
4.8s507 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-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
GPT-5.6 Luna
### Main issues
- Imports should be at module level, not inside the function.
- A bare `except:` catches everything, including programming errors and `KeyboardInterrupt`.
- The function sleeps even after the final failed attempt.
- It retries all HTTP statuses, including permanent errors such as `404`.
- `status_code == 200` excludes other successful `2xx` responses.
- `if item["active"] == True` should generally be `if item.get("active") is True`.
- A `requests.Session` can reuse connections and improve performance.
- Returning `[]` for every failure makes it impossible to distinguish “no active items” from “request failed.”
- Retries should generally use backoff rather than a fixed delay.…
23.3s1978 output tokens
Frequently asked questions
- Which is cheaper, GPT-4.1 Nano or GPT-5.6 Luna?
- Per 1M output tokens, GPT-4.1 Nano is ¥71.48 and GPT-5.6 Luna is ¥214.44 on FastMetal (yen, before tax), so GPT-4.1 Nano is cheaper.
- How do the context windows of GPT-4.1 Nano and GPT-5.6 Luna compare?
- GPT-4.1 Nano takes 1,047,576 tokens; GPT-5.6 Luna takes 1,050,000.
- Which ranks higher, GPT-4.1 Nano or GPT-5.6 Luna?
- GPT-5.6 Luna ranks higher on the public arena (GPT-4.1 Nano #195, GPT-5.6 Luna #42). Ranks move as the leaderboard updates.
- Can I use GPT-4.1 Nano and GPT-5.6 Luna with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "gpt-4.1-nano" or "gpt-5.6-luna" 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 GPT-5.6 Luna from the browser chat and the API. No monthly fee.