GPT-4.1 Nano vs MiniMax M3.1 Flash
Pricing, context window and real answers (October 2026)
GPT-4.1 Nano (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-4.1 Nano |  MiniMax M3.1 Flash | |
|---|---|---|
| Provider | OpenAI | MiniMax |
| Input (per 1M tokens) | $0.11 | $0.1583 |
| Output (per 1M tokens) | $0.44 | $0.633 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | $0.33 | $0.4748 |
| Context window | 1,047,576 tokens | 1,048,576 tokens |
| Release date | 4/14/2025 | 10/19/2026 |
| Input modalities | image, text, file | text, image |
| Throughput (p50) | 201 tokens/s (n=26) | — |
| Arena · overall | #258 · ELO 1,322 | Unranked |
| Arena · Japanese | #210 · ELO 1,233 | Unranked |
| Arena · coding | #240 · ELO 1,373 | 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, GPT-4.1 Nano: $0.44 per 1M output tokens, 30% less than MiniMax M3.1 Flash.
- For long documents, MiniMax M3.1 Flash: a 1,048,576-token context window against 1,047,576 for GPT-4.1 Nano.
- 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 Nano
Let's analyze the word "strawberry" step by step to count the number of 'r's.
1. Write down the word: **strawberry**
2. Examine each letter one by one:
- s: no
- t: no
- r: yes (first 'r')
- a: no
- w: no
- b: no
- e: no
- r: yes (second 'r')
- r: yes (third 'r')
- y: no
3. Counting the 'r's found:
- First 'r' (at position 3)
- Second 'r' (at position 8)
- Third 'r' (at position 9)
So, there are **3** 'r's in "strawberry."
**Final answer: 3**
2.0s183 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-4.1 Nano
The error:
```
TypeError: Cannot read properties of undefined (reading 'map')
```
indicates that `users` is `undefined` at the time your `UserList` component is rendering, and you're trying to call `map` on it.
### Why is this happening?
In React, if a prop (`users` in this case) is not provided or is undefined, trying to do `users.map(...)` causes this error. This can happen if:
- The parent component does not pass `users`.
- `users` might be `null` or `undefined` initially, especially if data fetching is involved.
### How to fix it
**Option 1: Provide a default value for `users`**
Ensure that `users` defaults to an empty array if it is not provided:
```jsx
function UserList({ users…
4.3s514 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-4.1 Nano
Here's a review and an improved version of your function:
### Key improvements:
1. **Move imports to the top**: Imports should generally be at the top of the file, not inside functions.
2. **Specify exception types**: Catch specific exceptions rather than a generic `except`.
3. **Use context managers if needed**: Not necessary here, but good to consider.
4. **Avoid repeated code**: The sleep statement is the same whether there is a request exception or a non-200 status.
5. **Simplify filtering**: Use list comprehensions for clarity.
6. **Improve variable names**: For readability.
7. **Add optional headers or parameters** if needed (not required here).
8. **Optional:** Add more descriptive e…
3.4s391 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-4.1 Nano or MiniMax M3.1 Flash?
- Per 1M output tokens, GPT-4.1 Nano is $0.44 and MiniMax M3.1 Flash is $0.633 on FastMetal (US dollars, before tax), so GPT-4.1 Nano is cheaper.
- How do the context windows of GPT-4.1 Nano and MiniMax M3.1 Flash compare?
- GPT-4.1 Nano takes 1,047,576 tokens; MiniMax M3.1 Flash takes 1,048,576.
- Can I use GPT-4.1 Nano and MiniMax M3.1 Flash with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "gpt-4.1-nano" 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-4.1 Nano and MiniMax M3.1 Flash from the browser chat and the API. No monthly fee.