GPT-5.6 Sol vs Llama 3.3 70B Instruct
Pricing, context window and real answers (September 2026)
GPT-5.6 Sol (OpenAI) 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
 GPT-5.6 Sol |  Llama 3.3 70B Instruct | |
|---|---|---|
| Provider | OpenAI | Meta |
| Input (per 1M tokens) | ¥893.5 | ¥17.87 |
| Output (per 1M tokens) | ¥5,361 | ¥57.18 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | ¥3,574 | ¥46 |
| Context window | 1,050,000 tokens | 131,072 tokens |
| Release date | 7/9/2026 | 12/6/2024 |
| Input modalities | file, image, text | text |
| Throughput (p50) | — | — |
| Arena · overall | #18 · ELO 1,483 | #250 · ELO 1,318 |
| Arena · Japanese | #5 · ELO 1,503 | #210 · ELO 1,212 |
| Arena · coding | #17 · ELO 1,528 | #263 · ELO 1,346 |
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, 99% less than GPT-5.6 Sol.
- On the Overall arena board, GPT-5.6 Sol ranks higher (#18; Llama 3.3 70B Instruct is #250).
- On the Japanese arena board, GPT-5.6 Sol ranks higher (#5; Llama 3.3 70B Instruct is #210).
- On the Coding arena board, GPT-5.6 Sol ranks higher (#17; Llama 3.3 70B Instruct is #263).
- For long documents, GPT-5.6 Sol: a 1,050,000-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.
GPT-5.6 Sol
“strawberry” can be split into letters:
s, t, **r**, a, w, b, e, **r**, **r**, y
The letter **r** appears **3 times**.
3.0s78 output tokens
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…
GPT-5.6 Sol
The error occurs because `users` is `undefined` when `UserList` renders, so JavaScript cannot call `.map()` on it. This often happens when data is loaded asynchronously or the parent does not pass the prop.
### Fix options
**1. Give `users` a default value:**
```jsx
function UserList({ users = [] }) {
return (
<ul>
{users.map(user => (
<li key={user.id}>{user.name}</li>
))}
</ul>
);
}
```
Note that this handles `undefined`, but not an explicitly passed `null`.
**2. Handle missing or invalid data explicitly:**
```jsx
function UserList({ users }) {
if (!Array.isArray(users)) {
return <p>No users available.</p>;
}
return (
<ul>
{users.m…
7.1s395 output tokens
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):…
GPT-5.6 Sol
### Issues and suggested improvements
- Move imports to module scope.
- Avoid bare `except:`; it catches programming errors and interrupts such as `KeyboardInterrupt`.
- Use `response.raise_for_status()` instead of manually checking only for `200`; other 2xx responses may also be valid.
- Retry only transient failures, such as timeouts, connection errors, `429`, and selected `5xx` responses. Retrying most `4xx` errors is not useful.
- Use exponential backoff rather than a fixed delay.
- Avoid sleeping after the final failed attempt.
- Replace `item["active"] == True` with `item.get("active") is True`.
- Use a list comprehension for the transformation.
- Add type hints and validate arguments…
33.1s1646 output tokens
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, GPT-5.6 Sol or Llama 3.3 70B Instruct?
- Per 1M output tokens, GPT-5.6 Sol is ¥5,361 and Llama 3.3 70B Instruct is ¥57.18 on FastMetal (yen, before tax), so Llama 3.3 70B Instruct is cheaper.
- How do the context windows of GPT-5.6 Sol and Llama 3.3 70B Instruct compare?
- GPT-5.6 Sol takes 1,050,000 tokens; Llama 3.3 70B Instruct takes 131,072.
- Which ranks higher, GPT-5.6 Sol or Llama 3.3 70B Instruct?
- GPT-5.6 Sol ranks higher on the public arena (GPT-5.6 Sol #5, Llama 3.3 70B Instruct #210). Ranks move as the leaderboard updates.
- Can I use GPT-5.6 Sol and Llama 3.3 70B Instruct with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "gpt-5.6-sol" 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 GPT-5.6 Sol and Llama 3.3 70B Instruct from the browser chat and the API. No monthly fee.