Llama 3.1 8B Instruct vs MiniMax M3.1 Flash
Pricing, context window and real answers (October 2026)
Llama 3.1 8B Instruct (Meta) 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
 Llama 3.1 8B Instruct |  MiniMax M3.1 Flash | |
|---|---|---|
| Provider | Meta | MiniMax |
| Input (per 1M tokens) | $0.0211 | $0.1583 |
| Output (per 1M tokens) | $0.0422 | $0.633 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | $0.0422 | $0.4748 |
| Context window | 131,072 tokens | 1,048,576 tokens |
| Release date | 7/23/2024 | 10/19/2026 |
| Input modalities | text | text, image |
| Throughput (p50) | 40 tokens/s (n=26) | — |
| Arena · overall | #343 · ELO 1,211 | Unranked |
| Arena · Japanese | #258 · ELO 1,087 | Unranked |
| Arena · coding | #337 · ELO 1,260 | 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, Llama 3.1 8B Instruct: $0.0422 per 1M output tokens, 93% less than MiniMax M3.1 Flash.
- For long documents, MiniMax M3.1 Flash: a 1,048,576-token context window against 131,072 for Llama 3.1 8B Instruct.
- 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.
Llama 3.1 8B Instruct
To count the number of 'r's in 'strawberry', I will break down the word into its individual letters and examine each one. Here's the step-by-step process:
1. The word 'strawberry' consists of the following letters: s-t-r-a-w-b-e-r-r-y.
2. I will start by counting the letters that are not 'r'. The word has 1 's', 1 't', 1 'a', 1 'w', 1 'b', 1 'e', 1 'y'.
3. Next, I will count the number of 'r's. The word has 2 'r's, one of which is part of the letter combination 'rr'.
Therefore, there are 2 'r's in the word 'strawberry'.
9.7s169 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…
Llama 3.1 8B Instruct
The error `TypeError: Cannot read properties of undefined (reading 'map')` typically occurs when you're trying to access a property (in this case, `map`) on an object that is `undefined`.
In your case, the `users` prop passed to the `UserList` component is `undefined`. This is likely because the parent component that renders `UserList` is not passing the `users` prop, or the prop is not being properly initialized.
To fix this error, you can add a simple check to ensure that `users` is not `undefined` before trying to call `map` on it:
```javascript
function UserList({ users }) {
if (!users) return null; // or return a default message, e.g. "No users found"
return (
<ul>
{us…
7.9s292 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):…
Llama 3.1 8B Instruct
Here are some suggestions for improving the function's readability, performance, and best practices:
**Readability Improvements**
1. **Function name**: Consider renaming `get_data` to `fetch_active_items` to better reflect its purpose.
2. **Variable naming**: Use more descriptive variable names, such as `response` instead of `r`, and `active_items` instead of `result`.
3. **Code organization**: Break up the function into smaller, more focused sections. This will improve readability and make it easier to understand the logic.
4. **Type hints**: Add type hints for function parameters and return types to make the code more self-documenting.
**Performance Improvements**
1. **Use a more effic…
29.7s533 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, Llama 3.1 8B Instruct or MiniMax M3.1 Flash?
- Per 1M output tokens, Llama 3.1 8B Instruct is $0.0422 and MiniMax M3.1 Flash is $0.633 on FastMetal (US dollars, before tax), so Llama 3.1 8B Instruct is cheaper.
- How do the context windows of Llama 3.1 8B Instruct and MiniMax M3.1 Flash compare?
- Llama 3.1 8B Instruct takes 131,072 tokens; MiniMax M3.1 Flash takes 1,048,576.
- Can I use Llama 3.1 8B Instruct and MiniMax M3.1 Flash with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "llama-3.1-8b-instruct" 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 Llama 3.1 8B Instruct and MiniMax M3.1 Flash from the browser chat and the API. No monthly fee.