Kimi K3.1 Pro vs Llama 3.3 70B Instruct
Pricing, context window and real answers (October 2026)
Kimi K3.1 Pro (Moonshot) and Llama 3.3 70B 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 3.3 70B Instruct | |
|---|---|---|
| Provider | Moonshot | Meta |
| Input (per 1M tokens) | $2.11 | $0.1055 |
| Output (per 1M tokens) | $10.55 | $0.3376 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | $7.39 | $0.2743 |
| Context window | 1,048,576 tokens | 131,072 tokens |
| Release date | 10/23/2026 | 12/6/2024 |
| Input modalities | text, image, video | text |
| Throughput (p50) | — | 20 tokens/s (n=26) |
| Arena · overall | Unranked | #266 · ELO 1,318 |
| Arena · Japanese | Unranked | #228 · ELO 1,214 |
| Arena · coding | Unranked | #279 · ELO 1,346 |
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 3.3 70B Instruct: $0.3376 per 1M output tokens, 97% 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 3.3 70B 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 3.3 70B Instruct
To count the number of 'r's in 'strawberry', I'll break down the word into its individual letters and then examine each one to identify the 'r's. Here are the steps:
1. **Write down the word 'strawberry' with its individual letters separated**: s-t-r-a-w-b-e-r-r-y
2. **Examine each letter to find the 'r's**: Going through the word letter by letter, I identify the 'r's.
- The first 'r' appears after 't', so that's one 'r'.
- Continuing through the word, I find another 'r' after 'e', which gives me a second 'r'.
- Then, immediately after this second 'r', I find a third 'r'.
3. **Count the total number of 'r's identified**: From the step above, I've identified a total of 3 'r's in th…
12.6s217 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 3.3 70B Instruct
# Step-by-step analysis of the problem:
1. **The error is occurring because the `map()` function is being called on `undefined`**: This means that the `users` prop is not being passed to the `UserList` component, or its value is `undefined`.
2. **The `map()` function is a method of arrays**: It is used to iterate over the elements of an array and apply a transformation to each element. If `users` is not an array (i.e., it's `undefined`), this method will not be available, hence the error.
3. **To fix this error, you need to ensure that `users` is always an array**: You can achieve this by adding a conditional statement to check if `users` is an array before calling `map()` on it.
# Fixed so…
28.4s560 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 3.3 70B Instruct
### Review and Suggestions
The provided Python function `get_data` appears to be designed to fetch data from a given URL, filter active items, and return the filtered results. Here's a review of the function focusing on readability, performance, and best practices, along with suggestions for improvement.
#### Issues and Improvements:
1. **Import Statements**: Import statements should be placed at the top of the file, not inside the function. This adheres to PEP 8, the official Python style guide.
2. **Exception Handling**: The `except` block is too broad. It's better to catch specific exceptions that you anticipate might occur. For example, `requests.get` might raise `requests.RequestExc…
40.3s815 output tokens
Frequently asked questions
- Which is cheaper, Kimi K3.1 Pro or Llama 3.3 70B Instruct?
- Per 1M output tokens, Kimi K3.1 Pro is $10.55 and Llama 3.3 70B Instruct is $0.3376 on FastMetal (US dollars, before tax), so Llama 3.3 70B Instruct is cheaper.
- How do the context windows of Kimi K3.1 Pro and Llama 3.3 70B Instruct compare?
- Kimi K3.1 Pro takes 1,048,576 tokens; Llama 3.3 70B Instruct takes 131,072.
- Can I use Kimi K3.1 Pro and Llama 3.3 70B Instruct with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "kimi-k3.1-pro" or "llama-3.3-70b-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 3.3 70B Instruct from the browser chat and the API. No monthly fee.