GPT-5.6 Luna vs Muse Spark 1.3
Pricing, context window and real answers (September 2026)
GPT-5.6 Luna (OpenAI) and Muse Spark 1.3 (Meta), 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-5.6 Luna |  Muse Spark 1.3 | |
|---|---|---|
| Provider | OpenAI | Meta |
| Input (per 1M tokens) | ¥35.74 | ¥219.9 |
| Output (per 1M tokens) | ¥214.44 | ¥747.65 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | ¥143 | ¥594 |
| Context window | 1,050,000 tokens | 1,048,576 tokens |
| Release date | 7/9/2026 | 9/2/2026 |
| Input modalities | file, image, text | text, image, video, file, audio |
| Arena · overall | #65 · ELO 1,453 | Unranked |
| Arena · Japanese | #29 · ELO 1,449 | Unranked |
| Arena · coding | #70 · ELO 1,497 | 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-5.6 Luna: ¥214.44 per 1M output tokens, 71% less than Muse Spark 1.3.
- For long documents, GPT-5.6 Luna: a 1,050,000-token context window against 1,048,576 for Muse Spark 1.3.
- 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-5.6 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
Muse Spark 1.3
The word is **strawberry** — there are **3** 'r's in it.
Here's my reasoning step by step:
1. **Write out the word:**
strawberry
2. **Break it into individual letters:**
s - t - r - a - w - b - e - r - r - y
3. **Go through each letter one by one and check if it is 'r':**
1. s → not r, count = 0
2. t → not r, count = 0
3. r → is r, count = 1
4. a → not r, count = 1
5. w → not r, count = 1
6. b → not r, count = 1
7. e → not r, count = 1
8. r → is r, count = 2
9. r → is r, count = 3
10. y → not r, count = 3
4. **Total up the matches:**
The 'r' appears at position 3, 8, and 9.
**Final answer: 3**
10.5s1063 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-5.6 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
Muse Spark 1.3
This error means `users` is `undefined` when your component tries to render.
`users.map(...)` only works if `users` is an array. On the first render, if you haven't passed the prop yet, or your data hasn't loaded from an API yet, `users` is `undefined`, so JavaScript throws:
> Cannot read properties of undefined (reading 'map')
This almost always happens in one of these 2 cases:
1. You forgot to pass the prop:
```js
<UserList /> // users is undefined!
```
vs.
```js
<UserList users={myUsers} />
```
2. Your data is async and is `undefined` on the first render:
```js
const [users, setUsers] = useState(); // undefined at first!
// ...
<UserList users={users} />
```
### How to fix it
**1.…
16.7s1242 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-5.6 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
Muse Spark 1.3
Here's a friendly review of your `get_data` function — it works, but there are a few readability, robustness, and performance issues worth fixing:
### 1. Readability Issues
**a) Imports inside the function**
```python
import requests
import time
```
This runs on every call (though Python caches it). Move to the top of the file per PEP 8.
**b) Poor variable names & magic numbers**
`r`, `i`, `data`, `2`, `10`, `200` don't tell the reader much.
**c) `== True` anti-pattern**
```python
if item["active"] == True:
```
Just use `if item.get("active"):` or `is True`. The current version will also crash with `KeyError` if `active` is missing.
**d) No docstring / type hints**
No one knows what `ur…
26.9s2141 output tokens
Frequently asked questions
- Which is cheaper, GPT-5.6 Luna or Muse Spark 1.3?
- Per 1M output tokens, GPT-5.6 Luna is ¥214.44 and Muse Spark 1.3 is ¥747.65 on FastMetal (yen, before tax), so GPT-5.6 Luna is cheaper.
- How do the context windows of GPT-5.6 Luna and Muse Spark 1.3 compare?
- GPT-5.6 Luna takes 1,050,000 tokens; Muse Spark 1.3 takes 1,048,576.
- Can I use GPT-5.6 Luna and Muse Spark 1.3 with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "gpt-5.6-luna" or "muse-spark-1.3" 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-5.6 Luna and Muse Spark 1.3 from the browser chat and the API. No monthly fee.