MiniMax M3 vs Muse Spark 1.2
Pricing, context window and real answers (August 2026)
MiniMax M3 (MiniMax) 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
 MiniMax M3 |  Muse Spark 1.2 | |
|---|---|---|
| Provider | MiniMax | Meta |
| Input (per 1M tokens) | ¥53.61 | ¥219.9 |
| Output (per 1M tokens) | ¥214.44 | ¥747.65 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | ¥161 | ¥594 |
| Context window | 1,048,576 tokens | 1,048,576 tokens |
| Release date | 5/31/2026 | 8/5/2026 |
| Input modalities | text, image, video | text, image, video, file, audio |
| Arena · overall | #78 · ELO 1,442 | #4 · ELO 1,498 |
| Arena · Japanese | #73 · ELO 1,402 | Unranked |
| Arena · coding | #64 · ELO 1,499 | #7 · 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, MiniMax M3: ¥214.44 per 1M output tokens, 71% less than Muse Spark 1.2.
- On the Overall arena board, Muse Spark 1.2 ranks higher (#4; MiniMax M3 is #78).
- On the Coding arena board, Muse Spark 1.2 ranks higher (#7; MiniMax M3 is #64).
- 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.
MiniMax M3
# Counting 'r's in "strawberry"
Let me go through the word letter by letter:
**s** - t - **r** - a - w - b - e - **r** - **r** - y
| Position | Letter | Is it 'r'? |
|----------|--------|------------|
| 1 | s | No |
| 2 | t | No |
| 3 | **r** | ✓ (1st) |
| 4 | a | No |
| 5 | w | No |
| 6 | b | No |
| 7 | e | No |
| 8 | **r** | ✓ (2nd) |
| 9 | **r** | ✓ (3rd) |
| 10 | y | No |
## Answer: **3**
The word "strawberry" contains **three 'r's** — one at position 3, and two consecutive 'r's at positions 8 and 9.
25.6s455 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…
MiniMax M3
# Error Analysis
The error `Cannot read properties of undefined (reading 'map')` tells you that **`users` is `undefined`** when your component runs. JavaScript can't call `.map()` on `undefined`, so it throws this error.
The component itself is fine syntactically — the problem is that the `users` prop isn't being passed, or hasn't loaded yet.
## Common Causes
**1. The prop isn't passed at all:**
```jsx
<UserList /> // No users prop → users is undefined
```
**2. Data is still loading (async fetch):**
```jsx
const [users, setUsers] = useState(); // undefined initially
```
**3. API returns undefined:**
```jsx
const { data } = await fetch('/api/users').then(r => r.json());
setUsers(data);…
32.8s710 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):…
MiniMax M3
## Code Review
This function works for the happy path, but it has several issues around error handling, retries, and readability. Here's a breakdown.
### 🐛 Bugs & Correctness Issues
| # | Issue | Why It Matters |
|---|-------|----------------|
| 1 | `except:` with no exception type | Swallows `KeyboardInterrupt`, `SystemExit`, and *everything else* (typos, `MemoryError`). You lose visibility into real failures. |
| 2 | Retries on every non‑200, including `4xx` | A `404` or `401` will never succeed by retrying — it just wastes time and rate-limit budget. |
| 3 | `if item["active"] == True` | Both linters (PEP 8 E712) and Python idiom prefer `if item["active"]:`. |
| 4 | `item["active"]` (…
33.4s3977 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, MiniMax M3 or Muse Spark 1.2?
- Per 1M output tokens, MiniMax M3 is ¥214.44 and Muse Spark 1.2 is ¥747.65 on FastMetal (yen, before tax), so MiniMax M3 is cheaper.
- How do the context windows of MiniMax M3 and Muse Spark 1.2 compare?
- MiniMax M3 takes 1,048,576 tokens; Muse Spark 1.2 takes 1,048,576.
- Which ranks higher, MiniMax M3 or Muse Spark 1.2?
- Muse Spark 1.2 ranks higher on the public arena (MiniMax M3 #73, Muse Spark 1.2 #4). Ranks move as the leaderboard updates.
- Can I use MiniMax M3 and Muse Spark 1.2 with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "minimax-m3" 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 MiniMax M3 and Muse Spark 1.2 from the browser chat and the API. No monthly fee.