Llama 3.3 70B Instruct vs Pareto
Pricing, context window and real answers (September 2026)
Llama 3.3 70B Instruct (Meta) and Pareto (Unbiased), 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 3.3 70B Instruct | Pareto | |
|---|---|---|
| Provider | Meta | Unbiased |
| Input (per 1M tokens) | ¥17.87 | ¥446.75 |
| Output (per 1M tokens) | ¥57.18 | ¥1,340.25 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | ¥46 | ¥1,117 |
| Context window | 131,072 tokens | 262,144 tokens |
| Release date | 12/6/2024 | 9/17/2026 |
| Input modalities | text | text, image |
| Throughput (p50) | — | — |
| Arena · overall | #250 · ELO 1,318 | Unranked |
| Arena · Japanese | #210 · ELO 1,212 | Unranked |
| Arena · coding | #263 · ELO 1,346 | Unranked |
Prices are FastMetal's yen rates before tax. Arena ranks are as of the latest leaderboard scrape.
Which should you pick?
- On price, Llama 3.3 70B Instruct: ¥57.18 per 1M output tokens, 96% less than Pareto.
- For long documents, Pareto: a 262,144-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.
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
Pareto
To count the number of 'r's in the word "strawberry," let's break it down letter by letter:
1. **s** - not an 'r'
2. **t** - not an 'r'
3. **r** - **Yes**, this is the first 'r'.
4. **a** - not an 'r'
5. **w** - not an 'r'
6. **b** - not an 'r'
7. **e** - not an 'r'
8. **r** - **Yes**, this is the second 'r'.
9. **r** - **Yes**, this is the third 'r'.
10. **y** - not an 'r'
Counting the occurrences:
- The 3rd letter is 'r'.
- The 8th letter is 'r'.
- The 9th letter is 'r'.
There are **3** 'r's in "strawberry".
5.5s225 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 3.3 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
Pareto
The error means `users` is `undefined` when `UserList` renders. Destructuring a prop does **not** protect against it:
```jsx
function UserList({ users }) { // users can still be undefined
return <ul>{users.map(...)}</ul>; // 💥
}
```
This usually happens when:
- The parent renders `<UserList />` without passing `users`
- The parent’s state starts as `undefined` before an async fetch finishes
- The API response is not an array, or `users` is passed as `undefined`
## Fix 1: Default the prop to an empty array
```jsx
function UserList({ users = [] }) {
return (
<ul>
{users.map(user => (
<li key={user.id}>{user.name}</li>
))}
</ul>
);
}
```
This works when…
11.6s1478 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 3.3 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
Pareto
The function is straightforward, but its error handling hides failures and retries requests that may never succeed.
### Main improvements
- **Move imports to module scope** for readability and conventional dependency management.
- **Avoid bare `except:`.** It catches everything, including programming errors and `KeyboardInterrupt`. Catch specific request failures instead.
- **Retry selectively.** Timeouts, connection failures, rate limits, and certain server errors may be temporary; most other HTTP errors should surface immediately.
- **Use exponential backoff** and avoid sleeping after the last attempt.
- **Use a list comprehension** to express the filtering and transformation directly.
-…
30.3s1090 output tokens
Frequently asked questions
- Which is cheaper, Llama 3.3 70B Instruct or Pareto?
- Per 1M output tokens, Llama 3.3 70B Instruct is ¥57.18 and Pareto is ¥1,340.25 on FastMetal (yen, before tax), so Llama 3.3 70B Instruct is cheaper.
- How do the context windows of Llama 3.3 70B Instruct and Pareto compare?
- Llama 3.3 70B Instruct takes 131,072 tokens; Pareto takes 262,144.
- Can I use Llama 3.3 70B Instruct and Pareto with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "llama-3.3-70b-instruct" or "pareto" 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 3.3 70B Instruct and Pareto from the browser chat and the API. No monthly fee.