Kimi K3.1 Pro vs Llama 3.1 8B Instruct
Pricing, context window and real answers (October 2026)
Kimi K3.1 Pro (Moonshot) and Llama 3.1 8B Instruct (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
 Kimi K3.1 Pro |  Llama 3.1 8B Instruct | |
|---|---|---|
| Provider | Moonshot | Meta |
| Input (per 1M tokens) | $2.11 | $0.0211 |
| Output (per 1M tokens) | $10.55 | $0.0422 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | $7.39 | $0.0422 |
| Context window | 1,048,576 tokens | 131,072 tokens |
| Release date | 10/23/2026 | 7/23/2024 |
| Input modalities | text, image, video | text |
| Throughput (p50) | — | 40 tokens/s (n=27) |
| Arena · overall | Unranked | #345 · ELO 1,211 |
| Arena · Japanese | Unranked | #262 · ELO 1,087 |
| Arena · coding | Unranked | #339 · ELO 1,259 |
Prices are FastMetal's rates, before tax.
Rankings: LMArena leaderboard dataset (lmarena-ai/leaderboard-dataset), CC BY 4.0, snapshot 2026-10-22; 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, 100% less than Kimi K3.1 Pro.
- For long documents, Kimi K3.1 Pro: 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.
Kimi K3.1 Pro
There are **3** ‘r’s in **“strawberry.”**
1. Write out each letter: **s – t – r – a – w – b – e – r – r – y**
2. Locate the ‘r’s: positions **3, 8, and 9**.
3. Count them: **1 + 1 + 1 = 3**.
11.3s
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
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…
Kimi K3.1 Pro
## What's causing the error
The error means `users` is `undefined` when the component renders, and you can't call `.map()` on `undefined`. The component itself is fine — the problem is that whatever renders `<UserList />` either isn't passing the prop, or is passing a value that isn't an array yet.
The most common cause is **async data fetching**: the parent renders `UserList` before the data has loaded.
```jsx
function App() {
const [users, setUsers] = useState(); // ❌ undefined on first render
useEffect(() => {
fetch('/api/users')
.then(res => res.json())
.then(setUsers);
}, []);
return <UserList users={users} />; // users is undefined here initially
}
```
On…
36.3s
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
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):…
Kimi K3.1 Pro
The timeout and bounded retry loop are good starting points. The biggest issue is that failures—and even programming errors—are silently converted into retries and then `[]`.
### Suggested improvements
- **Avoid bare `except:`**. It catches `KeyboardInterrupt`, `KeyError`, JSON/schema errors, and other bugs. Retry only expected transient network failures; let malformed responses fail visibly.
- **Use a deliberate retry policy**. Retrying `401`, `403`, or `404` usually wastes time. Retry only transient statuses, use exponential backoff, and avoid sleeping after the final attempt.
- **Do not use `[]` for both failure and no results**. Prefer raising the final exception so callers can disting…
560.2s
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
Frequently asked questions
- Which is cheaper, Kimi K3.1 Pro or Llama 3.1 8B Instruct?
- Per 1M output tokens, Kimi K3.1 Pro is $10.55 and Llama 3.1 8B Instruct is $0.0422 on FastMetal (US dollars, before tax), so Llama 3.1 8B Instruct is cheaper.
- How do the context windows of Kimi K3.1 Pro and Llama 3.1 8B Instruct compare?
- Kimi K3.1 Pro takes 1,048,576 tokens; Llama 3.1 8B Instruct takes 131,072.
- Can I use Kimi K3.1 Pro and Llama 3.1 8B Instruct with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "kimi-k3.1-pro" or "llama-3.1-8b-instruct" 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 Kimi K3.1 Pro and Llama 3.1 8B Instruct from the browser chat and the API. No monthly fee.