GPT-5 Mini vs MiniMax M3.1 Flash
Pricing, context window and real answers (October 2026)
GPT-5 Mini (OpenAI) and MiniMax M3.1 Flash (MiniMax), 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 |  MiniMax M3.1 Flash | |
|---|---|---|
| Provider | OpenAI | MiniMax |
| Input (per 1M tokens) | $0.2638 | $0.1583 |
| Output (per 1M tokens) | $2.11 | $0.633 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | $1.32 | $0.4748 |
| Context window | 400,000 tokens | 1,048,576 tokens |
| Release date | 8/7/2025 | 10/19/2026 |
| Input modalities | text, image, file | text, image |
| Throughput (p50) | — | — |
| Arena · overall | #177 · ELO 1,390 | Unranked |
| Arena · Japanese | #121 · ELO 1,364 | Unranked |
| Arena · coding | #185 · ELO 1,432 | Unranked |
Prices are FastMetal's rates, before tax.
Rankings: LMArena leaderboard dataset (lmarena-ai/leaderboard-dataset), CC BY 4.0, snapshot 2026-10-15; category names mapped and ratings rounded by FastMetal.
Which should you pick?
- On price, MiniMax M3.1 Flash: $0.633 per 1M output tokens, 70% less than GPT-5 Mini.
- For long documents, MiniMax M3.1 Flash: 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
MiniMax M3.1 Flash
1. Spell **strawberry**:
**s – t – r – a – w – b – e – r – r – y**
2. Count the letter **r**: it appears at positions **3, 8, and 9**.
**Total: 3 “r”s.**
3.0s193 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
MiniMax M3.1 Flash
The error occurs because `users` is `undefined` when `UserList` renders. Calling `.map()` on `undefined` causes the `TypeError`.
This commonly happens when:
- `<UserList />` is rendered without passing the `users` prop.
- The prop has a different name, such as `user`.
- Data is still loading and the parent initializes its state as `undefined`.
- An API response is accessed before it exists.
### Immediate fix
Provide a default empty array:
```jsx
function UserList({ users = [] }) {
return (
<ul>
{users.map(user => (
<li key={user.id}>{user.name}</li>
))}
</ul>
);
}
```
Then pass the actual data from the parent:
```jsx
const [users, setUsers] = useState([…
7.3s744 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
MiniMax M3.1 Flash
The function is understandable, but it has a few important issues:
- `except:` catches programming errors such as `KeyError`, `TypeError`, and even `KeyboardInterrupt`.
- Every non-200 response is retried, including permanent failures such as 400, 401, and 404.
- It sleeps after the final failed attempt.
- Returning `[]` makes a failed request indistinguishable from a successful response containing no active records.
- A fixed delay can cause clients to retry simultaneously; exponential backoff and `Retry-After` are preferable.
- `item["active"] == True` should be `item.get("active") is True` if the API contract says it is a JSON boolean.
- A `Session` can reuse connections, while `requests…
146.5s
Frequently asked questions
- Which is cheaper, GPT-5 Mini or MiniMax M3.1 Flash?
- Per 1M output tokens, GPT-5 Mini is $2.11 and MiniMax M3.1 Flash is $0.633 on FastMetal (US dollars, before tax), so MiniMax M3.1 Flash is cheaper.
- How do the context windows of GPT-5 Mini and MiniMax M3.1 Flash compare?
- GPT-5 Mini takes 400,000 tokens; MiniMax M3.1 Flash takes 1,048,576.
- Can I use GPT-5 Mini and MiniMax M3.1 Flash with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "gpt-5-mini" or "minimax-m3.1-flash" 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 MiniMax M3.1 Flash from the browser chat and the API. No monthly fee.