GPT-5.6 Luna vs LLM-jp-3.1 8x13B instruct4
Pricing, context window and real answers (August 2026)
GPT-5.6 Luna (OpenAI) and LLM-jp-3.1 8x13B instruct4 (Llm Jp), 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 Luna | LLM-jp-3.1 8x13B instruct4 | |
|---|---|---|
| Provider | OpenAI | Llm Jp |
| Input (per 1M tokens) | ¥35.74 | ¥16 |
| Output (per 1M tokens) | ¥214.44 | ¥79 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | ¥143 | ¥56 |
| Context window | 1,050,000 tokens | 4,096 tokens |
| Release date | 7/9/2026 | 3/21/2026 |
| Input modalities | file, image, text | text |
| Arena · overall | #63 · ELO 1,452 | Unranked |
| Arena · Japanese | #36 · ELO 1,444 | Unranked |
| Arena · coding | #69 · ELO 1,496 | Unranked |
Prices are FastMetal's yen rates before tax. Arena ranks are as of the latest leaderboard scrape.
Which should you pick?
- On price, LLM-jp-3.1 8x13B instruct4: ¥79 per 1M output tokens, 63% less than GPT-5.6 Luna.
- For long documents, GPT-5.6 Luna: a 1,050,000-token context window against 4,096 for LLM-jp-3.1 8x13B instruct4.
- 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 Luna
The word **“strawberry”** is spelled:
**s – t – r – a – w – b – e – r – r – y**
The letter **“r”** appears at positions **3, 8, and 9**.
**Answer: 3**
3.0s120 output tokens
LLM-jp-3.1 8x13B instruct4
To count the number of 'r's in the word 'strawberry', follow these steps:
1. Identify the word: The word we are examining is 'strawberry'.
2. Understand the task: We need to find and count all occurrences of the letter 'r' in this word.
3. Begin with the first letter: The first letter is 's', which is not 'r', so we move on to the next letter.
4. Proceed to the second letter: The second letter is 't', which is also not 'r', so we continue to the third letter.
5. Reach the third letter: The third letter is 'r'. We have found one 'r'.
6. Continue to the fourth letter: The fourth letter is 'a', which is not 'r', so we move on to the fifth letter.
7. Examine the fifth letter: The fifth letter i…
4.0s400 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 Luna
`users` is `undefined` when `UserList` renders, so calling `.map()` on it fails. `.map()` only exists on arrays.
Common causes include:
- The parent does not pass a `users` prop.
- The prop name differs, such as `userList` instead of `users`.
- Users are loaded asynchronously and are initially undefined.
- The API response has a different shape, such as `{ data: [...] }`.
### Provide a default value
```jsx
function UserList({ users = [] }) {
return (
<ul>
{users.map(user => (
<li key={user.id}>{user.name}</li>
))}
</ul>
);
}
```
The parent should pass an array:
```jsx
<UserList users={users} />
```
If the list comes from state, initialize it as an empty…
4.8s507 output tokens
LLM-jp-3.1 8x13B instruct4
The error TypeError: Cannot read properties of undefined (reading 'map') suggests that the 'users' argument passed to the UserList component is undefined or does not have a 'map' method.
The cause of the problem may be that the users variable is not being passed correctly or is not defined in the parent component. Check the props being passed to the UserList component to ensure that they are defined and contain the expected data.
To fix the issue, you can add some error handling and debugging to your component. Try logging the users variable to the console to verify that it is not undefined:
```jsx
function UserList({ users }) {
console.log("Users:", users);
if (!users) {
return <…
3.0s279 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 Luna
### Main issues
- Imports should be at module level, not inside the function.
- A bare `except:` catches everything, including programming errors and `KeyboardInterrupt`.
- The function sleeps even after the final failed attempt.
- It retries all HTTP statuses, including permanent errors such as `404`.
- `status_code == 200` excludes other successful `2xx` responses.
- `if item["active"] == True` should generally be `if item.get("active") is True`.
- A `requests.Session` can reuse connections and improve performance.
- Returning `[]` for every failure makes it impossible to distinguish “no active items” from “request failed.”
- Retries should generally use backoff rather than a fixed delay.…
23.3s1978 output tokens
LLM-jp-3.1 8x13B instruct4
The provided Python function aims to get data from a URL using the requests library, filter out specific items based on their "active" field, and return the result. Here are some suggestions to improve readability, performance, and adherence to best practices:
1. Configure the linter (e.g., flake8 or pylint) to ensure code quality and enforce a consistent coding style.
2. Use type hints to improve readability and maintainability.
3. Use context managers (with statement) for handling requests:
4. Rewrite the function using a more functional programming style:
Here's the improved version of the function:
```python
import requests
import time
from typing import List, Dict, Any
def get_da…
3.9s406 output tokens
Frequently asked questions
- Which is cheaper, GPT-5.6 Luna or LLM-jp-3.1 8x13B instruct4?
- Per 1M output tokens, GPT-5.6 Luna is ¥214.44 and LLM-jp-3.1 8x13B instruct4 is ¥79 on FastMetal (yen, before tax), so LLM-jp-3.1 8x13B instruct4 is cheaper.
- How do the context windows of GPT-5.6 Luna and LLM-jp-3.1 8x13B instruct4 compare?
- GPT-5.6 Luna takes 1,050,000 tokens; LLM-jp-3.1 8x13B instruct4 takes 4,096.
- Can I use GPT-5.6 Luna and LLM-jp-3.1 8x13B instruct4 with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "gpt-5.6-luna" or "llm-jp-3.1-8x13b-instruct4" 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 Luna and LLM-jp-3.1 8x13B instruct4 from the browser chat and the API. No monthly fee.