GPT-5.4 Nano vs GPT-6 Luna
Pricing, context window and real answers (September 2026)
GPT-5.4 Nano (OpenAI) and GPT-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-5.4 Nano | GPT-6 Luna | |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Input (per 1M tokens) | ¥35.74 | ¥17.87 |
| Output (per 1M tokens) | ¥223.38 | ¥89.35 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | ¥147 | ¥63 |
| Context window | 400,000 tokens | 1,050,000 tokens |
| Release date | 3/17/2026 | 9/22/2026 |
| Input modalities | file, image, text | file, image, text |
| Throughput (p50) | — | — |
| Arena · overall | #146 · ELO 1,402 | Unranked |
| Arena · Japanese | #107 · ELO 1,368 | Unranked |
| Arena · coding | #128 · ELO 1,460 | Unranked |
Prices are FastMetal's yen rates before tax. Arena ranks are as of the latest leaderboard scrape.
Which should you pick?
- On price, GPT-6 Luna: ¥89.35 per 1M output tokens, 60% less than GPT-5.4 Nano.
- For long documents, GPT-6 Luna: a 1,050,000-token context window against 400,000 for GPT-5.4 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-5.4 Nano
**Step 1:** Write the word: **strawberry**
**Step 2:** Count each letter **r** in the word:
- s **t r** a w b e **r r** y
There are **3** **r**’s in total (**one** after the **t**, and **two** near the end).
✅ **Answer: 3**
1.5s86 output tokens
GPT-6 Luna
In **“strawberry”**, the letter **r** appears at positions 3, 8, and 9. So there are **3** “r”s.
2.7s94 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-5.4 Nano
This error means that **`users` is `undefined` (or otherwise not an array)** when your component renders, so calling `users.map(...)` throws:
```js
users.map(...)
^^^^ reading 'map' of undefined
```
### Why it happens
Common causes:
- You render `<UserList />` before data has loaded (e.g., `users` comes from an async fetch).
- The parent component passes the wrong prop name (e.g., `user` instead of `users`).
- The API/parent state initializes `users` as `undefined` rather than `[]`.
- You’re destructuring a prop incorrectly upstream.
### Fix options
#### 1) Ensure `users` is always an array (recommended)
Initialize/default it in the component:
```js
function UserList({ users = []…
3.3s383 output tokens
GPT-6 Luna
`users` is `undefined` when `UserList` renders, so calling `users.map(...)` throws. This often happens because the parent didn’t pass the prop or because data hasn’t loaded yet.
If an empty list is acceptable until the data arrives, provide a default:
```jsx
function UserList({ users = [] }) {
return (
<ul>
{users.map(user => (
<li key={user.id}>{user.name}</li>
))}
</ul>
);
}
```
Also check that the parent passes `users`:
```jsx
<UserList users={users} />
```
If `users` is loaded asynchronously, initialize it as an array:
```jsx
const [users, setUsers] = useState([]);
```
If `null` is also possible, normalize it before mapping, for example `(users ?? […
3.8s289 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-5.4 Nano
Here are key improvements (readability, performance, robustness, best practices) and a revised version of your function.
## What to improve
1. **Avoid bare `except:`**
Catch specific exceptions from `requests` (e.g., `requests.exceptions.RequestException`) to avoid hiding bugs.
2. **Prefer `raise_for_status()` or check status codes clearly**
`r.raise_for_status()` is a common pattern.
3. **Use exponential backoff (or at least a clear retry delay)**
Instead of sleeping the same amount every time, consider increasing delay.
4. **Reuse a `requests.Session()` (optional but best practice)**
If this function is called repeatedly, passing a session or creating one outside i…
4.4s718 output tokens
GPT-6 Luna
Key improvements:
- Move imports to module scope.
- Catch `requests.RequestException` instead of using a bare `except`; the current handler can hide programming errors as well as network failures.
- Call `raise_for_status()` so unsuccessful HTTP responses are handled explicitly.
- Use a list comprehension instead of building the result manually.
- Replace `item["active"] == True` with `item.get("active") is True` if the field is expected to be the Boolean `True`.
- Add backoff between attempts. A fixed two-second delay can make retries slower than necessary.
- Decide whether failures should raise an exception or return `[]`. Returning `[]` makes a failed request indistinguishable from a suc…
16.5s1713 output tokens
Frequently asked questions
- Which is cheaper, GPT-5.4 Nano or GPT-6 Luna?
- Per 1M output tokens, GPT-5.4 Nano is ¥223.38 and GPT-6 Luna is ¥89.35 on FastMetal (yen, before tax), so GPT-6 Luna is cheaper.
- How do the context windows of GPT-5.4 Nano and GPT-6 Luna compare?
- GPT-5.4 Nano takes 400,000 tokens; GPT-6 Luna takes 1,050,000.
- Can I use GPT-5.4 Nano and GPT-6 Luna with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "gpt-5.4-nano" or "gpt-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-5.4 Nano and GPT-6 Luna from the browser chat and the API. No monthly fee.