GPT-6 Astra vs Muse Spark 1.2
Pricing, context window and real answers (September 2026)
GPT-6 Astra (OpenAI) and Muse Spark 1.2 (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-6 Astra |  Muse Spark 1.2 | |
|---|---|---|
| Provider | OpenAI | Meta |
| Input (per 1M tokens) | ¥1,717.72 | ¥219.9 |
| Output (per 1M tokens) | ¥8,588.62 | ¥747.65 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | ¥6,012 | ¥594 |
| Context window | 1,050,000 tokens | 1,048,576 tokens |
| Release date | 9/4/2026 | 8/5/2026 |
| Input modalities | file, image, text | text, image, video, file, audio |
| Arena · overall | Unranked | #5 · ELO 1,499 |
| Arena · Japanese | Unranked | Unranked |
| Arena · coding | Unranked | #10 · ELO 1,533 |
Prices are FastMetal's yen rates before tax. Arena ranks are as of the latest leaderboard scrape.
Which should you pick?
- On price, Muse Spark 1.2: ¥747.65 per 1M output tokens, 91% less than GPT-6 Astra.
- For long documents, GPT-6 Astra: a 1,050,000-token context window against 1,048,576 for Muse Spark 1.2.
- 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-6 Astra
“strawberry” is spelled **s-t-r-a-w-b-e-r-r-y**.
The letter **r** appears in positions **3, 8, and 9**, so there are **3 r’s**.
3.8s85 output tokens
Muse Spark 1.2
There are **3** 'r's in "strawberry".
Here is my reasoning step by step:
1. Write out the word and its spelling: **s-t-r-a-w-b-e-r-r-y**
2. Go through each letter one by one and check if it is an 'r':
* 1. s - not an 'r' -> count = 0
* 2. t - not an 'r' -> count = 0
* 3. **r** - yes, it's 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** - yes, it's an 'r' -> count = 2
* 9. **r** - yes, it's an 'r' -> count = 3
* 10. y - not an 'r' -> count = 3
3. Final total = **3**.
13.3s1123 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-6 Astra
The error means **`users` is `undefined` when React renders `UserList`**, so calling `users.map(...)` fails. This commonly happens when the parent doesn’t pass the prop or when data hasn’t finished loading.
### 1. Default `users` to an empty array
If an absent prop should mean “no users,” use a default value:
```jsx
function UserList({ users = [] }) {
return (
<ul>
{users.map(user => (
<li key={user.id}>{user.name}</li>
))}
</ul>
);
}
```
This handles a missing or `undefined` prop, but **not `null` or other non-array values**.
### 2. Check where the prop comes from
Make sure the parent passes an array. For data loaded asynchronously, initialize state to…
6.7s380 output tokens
Muse Spark 1.2
This error means `users` is `undefined` when the component tries to render.
You are calling `.map()` on `users` at line 12, but `.map()` only exists on arrays. If `users` is `undefined` or `null`, JavaScript throws: `Cannot read properties of undefined (reading 'map')`.
This usually happens for 2 reasons:
1. You didn't pass the `users` prop to the component: `<UserList />`
2. Your data is async and `users` is `undefined` on the first render. For example: `const [users, setUsers] = useState()` without an initial value, and the data hasn't been fetched yet.
### Fix it
You need to guarantee `users` is always an array before you call `.map()`.
#### 1. Best Fix: Give it a default value
U…
7.6s1142 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-6 Astra
The function is compact, but its biggest issue is that **all failures silently become `[]`**, making an unavailable API indistinguishable from a successful response with no active items.
### Suggested improvements
- **Move imports to module scope** for clarity and conventional organization.
- **Avoid bare `except:`.** It catches everything, including `KeyboardInterrupt`, and hides bugs such as missing dictionary keys.
- **Retry only transient failures**, such as connection errors, timeouts, rate limiting, and selected server errors. Retrying a `401` or `404` usually won’t help.
- **Don’t sleep after the final failed attempt.** Use exponential backoff rather than a fixed delay.
- **Use desc…
19.2s965 output tokens
Muse Spark 1.2
Here is a review of your function with a refactored, production-ready version:
### Issues in the Current Version
#### 1. Readability
1. **Generic name:** `get_data` tells nothing about what it does. `get_active_items` is better.
2. **Imports inside function:** `import requests, time` should be at the top of the file. It's slower and violates PEP 8.
3. **Magic numbers:** `200`, `10`, `2` have no context.
4. **Verbose loop:** Manual `for` loop + `append` can be replaced with a list comprehension.
5. **No documentation/typing:** No docstring, no type hints, so callers don't know what it expects or returns.
6. **`== True` is redundant:** `if item["active"] == True:` should be `if item["a…
11.8s2278 output tokens
Frequently asked questions
- Which is cheaper, GPT-6 Astra or Muse Spark 1.2?
- Per 1M output tokens, GPT-6 Astra is ¥8,588.62 and Muse Spark 1.2 is ¥747.65 on FastMetal (yen, before tax), so Muse Spark 1.2 is cheaper.
- How do the context windows of GPT-6 Astra and Muse Spark 1.2 compare?
- GPT-6 Astra takes 1,050,000 tokens; Muse Spark 1.2 takes 1,048,576.
- Can I use GPT-6 Astra and Muse Spark 1.2 with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "gpt-6-astra" or "muse-spark-1.2" 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-6 Astra and Muse Spark 1.2 from the browser chat and the API. No monthly fee.