GPT-4.1 Mini vs Muse Spark 1.2
Pricing, context window and real answers (September 2026)
GPT-4.1 Mini (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-4.1 Mini |  Muse Spark 1.2 | |
|---|---|---|
| Provider | OpenAI | Meta |
| Input (per 1M tokens) | ¥71.48 | ¥219.9 |
| Output (per 1M tokens) | ¥285.92 | ¥747.65 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | ¥214 | ¥594 |
| Context window | 1,047,576 tokens | 1,048,576 tokens |
| Release date | 4/14/2025 | 8/5/2026 |
| Input modalities | image, text, file | text, image, video, file, audio |
| Throughput (p50) | — | — |
| Arena · overall | #172 · ELO 1,383 | #4 · ELO 1,500 |
| Arena · Japanese | #135 · ELO 1,330 | Unranked |
| Arena · coding | #164 · ELO 1,433 | #9 · ELO 1,536 |
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 Mini: ¥285.92 per 1M output tokens, 62% less than Muse Spark 1.2.
- On the Overall arena board, Muse Spark 1.2 ranks higher (#4; GPT-4.1 Mini is #172).
- On the Coding arena board, Muse Spark 1.2 ranks higher (#9; GPT-4.1 Mini is #164).
- For long documents, Muse Spark 1.2: a 1,048,576-token context window against 1,047,576 for GPT-4.1 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-4.1 Mini
Let's count the number of 'r's in the word "strawberry" step by step:
1. Write down the word: s t r a w b e r r y
2. Look at each letter and check if it is 'r':
- s → not 'r'
- t → not 'r'
- r → yes, 1st 'r'
- a → not 'r'
- w → not 'r'
- b → not 'r'
- e → not 'r'
- r → yes, 2nd 'r'
- r → yes, 3rd 'r'
- y → not 'r'
3. Total number of 'r's found: 3
**Answer:** There are 3 'r's in the word "strawberry".
2.0s176 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-4.1 Mini
The error **"TypeError: Cannot read properties of undefined (reading 'map')"** means that the `users` prop passed to your `UserList` component is `undefined` at the time the component tries to render, so calling `.map()` on it throws an error.
### What's causing the error?
- Your component expects `users` to be an array, but for some reason it is `undefined` when the component renders.
- This could happen if:
- The parent component that passes the `users` prop did not pass it or passed `undefined`.
- The data is being fetched asynchronously and hasn't arrived yet.
- The default prop is missing or no default fallback is handled.
### How to fix it?
You want to ensure `users` is alway…
8.1s465 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-4.1 Mini
Your function is functional but can be improved for readability, performance, and best practices. Here are some suggestions and an improved version of your function:
### Issues and improvements
1. **Imports**: Import at the top of the file rather than inside the function.
2. **Catch specific exceptions**: Avoid bare `except:` clauses. Catch specific exceptions like `requests.RequestException` or `json.decoder.JSONDecodeError`.
3. **Use list comprehension**: Instead of a loop with `append()`, use list comprehension for clarity and conciseness.
4. **Check boolean conditions directly**: Instead of `item["active"] == True`, just use `item["active"]`.
5. **Avoid sleeping after last retry**: No…
5.6s690 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-4.1 Mini or Muse Spark 1.2?
- Per 1M output tokens, GPT-4.1 Mini is ¥285.92 and Muse Spark 1.2 is ¥747.65 on FastMetal (yen, before tax), so GPT-4.1 Mini is cheaper.
- How do the context windows of GPT-4.1 Mini and Muse Spark 1.2 compare?
- GPT-4.1 Mini takes 1,047,576 tokens; Muse Spark 1.2 takes 1,048,576.
- Which ranks higher, GPT-4.1 Mini or Muse Spark 1.2?
- Muse Spark 1.2 ranks higher on the public arena (GPT-4.1 Mini #135, Muse Spark 1.2 #4). Ranks move as the leaderboard updates.
- Can I use GPT-4.1 Mini and Muse Spark 1.2 with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "gpt-4.1-mini" 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-4.1 Mini and Muse Spark 1.2 from the browser chat and the API. No monthly fee.