GPT-5 Mini vs Muse Spark 1.3
Pricing, context window and real answers (September 2026)
GPT-5 Mini (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 Mini |  Muse Spark 1.3 | |
|---|---|---|
| Provider | OpenAI | Meta |
| Input (per 1M tokens) | ¥44.68 | ¥219.9 |
| Output (per 1M tokens) | ¥357.4 | ¥747.65 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | ¥223 | ¥594 |
| Context window | 400,000 tokens | 1,048,576 tokens |
| Release date | 8/7/2025 | 9/2/2026 |
| Input modalities | text, image, file | text, image, video, file, audio |
| Throughput (p50) | — | — |
| Arena · overall | #163 · ELO 1,390 | #8 · ELO 1,493 |
| Arena · Japanese | #109 · ELO 1,365 | Unranked |
| Arena · coding | #172 · ELO 1,431 | #8 · ELO 1,537 |
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 Mini: ¥357.4 per 1M output tokens, 52% less than Muse Spark 1.3.
- On the Overall arena board, Muse Spark 1.3 ranks higher (#8; GPT-5 Mini is #163).
- On the Coding arena board, Muse Spark 1.3 ranks higher (#8; GPT-5 Mini is #172).
- For long documents, Muse Spark 1.3: a 1,048,576-token context window against 400,000 for GPT-5 Mini.
- 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 Mini
We examine the word "strawberry" letter by letter:
1. s — not an r (count = 0)
2. t — not an r (count = 0)
3. r — 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 — an r (count = 2)
9. r — an r (count = 3)
10. y — not an r (count = 3)
Total number of 'r's = 3.
5.2s382 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 Mini
That error means users is undefined when you call users.map(...). map is an Array method, so calling it on undefined throws "Cannot read properties of undefined (reading 'map')".
Common causes
- The parent component didn't pass a users prop.
- users is still loading (e.g. fetched asynchronously) and is null/undefined until the fetch completes.
- The parent initialized users to null instead of an empty array.
How to fix
Choose one or more of these approaches depending on your app:
1) Initialize the parent state as an array
If users is state in the parent, initialize it to [] so the child always receives an array:
const [users, setUsers] = useState([]);
// fetch and setUsers(data)
2) Give…
8.0s966 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 Mini
Thanks — this is a nice simple function, but it can be improved in several ways: move imports to module scope, avoid broad except:, use connection pooling (Session) for performance, use specific exception types, add logging and docstring, avoid comparing to True, use list comprehensions, validate JSON shape, and implement backoff (or use urllib3 Retry).
Below are two improved alternatives and a short rationale for the changes.
1) A simple, clear improvement (small changes, explicit errors, backoff + jitter)
- Moves imports to top-level
- Uses requests.Session for connection pooling
- Catches specific exceptions (requests.RequestException, ValueError)
- Uses list comprehension and item.get(…
13.6s1702 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 Mini or Muse Spark 1.3?
- Per 1M output tokens, GPT-5 Mini is ¥357.4 and Muse Spark 1.3 is ¥747.65 on FastMetal (yen, before tax), so GPT-5 Mini is cheaper.
- How do the context windows of GPT-5 Mini and Muse Spark 1.3 compare?
- GPT-5 Mini takes 400,000 tokens; Muse Spark 1.3 takes 1,048,576.
- Which ranks higher, GPT-5 Mini or Muse Spark 1.3?
- Muse Spark 1.3 ranks higher on the public arena (GPT-5 Mini #109, Muse Spark 1.3 #8). Ranks move as the leaderboard updates.
- Can I use GPT-5 Mini and Muse Spark 1.3 with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "gpt-5-mini" 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 Mini and Muse Spark 1.3 from the browser chat and the API. No monthly fee.