GPT-4.1 Mini vs LLM-jp-3.1 8x13B instruct4
Pricing, context window and real answers (September 2026)
GPT-4.1 Mini (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-4.1 Mini | LLM-jp-3.1 8x13B instruct4 | |
|---|---|---|
| Provider | OpenAI | LLM-jp |
| Input (per 1M tokens) | ¥71.48 | ¥16 |
| Output (per 1M tokens) | ¥285.92 | ¥79 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | ¥214 | ¥56 |
| Context window | 1,047,576 tokens | 4,096 tokens |
| Release date | 4/14/2025 | 3/21/2026 |
| Input modalities | image, text, file | text |
| Throughput (p50) | — | — |
| Arena · overall | #172 · ELO 1,383 | Unranked |
| Arena · Japanese | #135 · ELO 1,330 | Unranked |
| Arena · coding | #164 · ELO 1,433 | 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, 72% less than GPT-4.1 Mini.
- For long documents, GPT-4.1 Mini: a 1,047,576-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-4.1 Mini
Let's count the number of 'r's in the word "strawberry" step by step:
1. Write down the word: s t r a w b e r r y
2. Look at each letter and check if it is 'r':
- s → not 'r'
- t → not 'r'
- r → yes, 1st 'r'
- a → not 'r'
- w → not 'r'
- b → not 'r'
- e → not 'r'
- r → yes, 2nd 'r'
- r → yes, 3rd 'r'
- y → not 'r'
3. Total number of 'r's found: 3
**Answer:** There are 3 'r's in the word "strawberry".
2.0s176 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-4.1 Mini
The error **"TypeError: Cannot read properties of undefined (reading 'map')"** means that the `users` prop passed to your `UserList` component is `undefined` at the time the component tries to render, so calling `.map()` on it throws an error.
### What's causing the error?
- Your component expects `users` to be an array, but for some reason it is `undefined` when the component renders.
- This could happen if:
- The parent component that passes the `users` prop did not pass it or passed `undefined`.
- The data is being fetched asynchronously and hasn't arrived yet.
- The default prop is missing or no default fallback is handled.
### How to fix it?
You want to ensure `users` is alway…
8.1s465 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-4.1 Mini
Your function is functional but can be improved for readability, performance, and best practices. Here are some suggestions and an improved version of your function:
### Issues and improvements
1. **Imports**: Import at the top of the file rather than inside the function.
2. **Catch specific exceptions**: Avoid bare `except:` clauses. Catch specific exceptions like `requests.RequestException` or `json.decoder.JSONDecodeError`.
3. **Use list comprehension**: Instead of a loop with `append()`, use list comprehension for clarity and conciseness.
4. **Check boolean conditions directly**: Instead of `item["active"] == True`, just use `item["active"]`.
5. **Avoid sleeping after last retry**: No…
5.6s690 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-4.1 Mini or LLM-jp-3.1 8x13B instruct4?
- Per 1M output tokens, GPT-4.1 Mini is ¥285.92 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-4.1 Mini and LLM-jp-3.1 8x13B instruct4 compare?
- GPT-4.1 Mini takes 1,047,576 tokens; LLM-jp-3.1 8x13B instruct4 takes 4,096.
- Can I use GPT-4.1 Mini and LLM-jp-3.1 8x13B instruct4 with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "gpt-4.1-mini" 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-4.1 Mini and LLM-jp-3.1 8x13B instruct4 from the browser chat and the API. No monthly fee.
More comparisons with GPT-4.1 Mini
More comparisons with LLM-jp-3.1 8x13B instruct4
- LLM-jp-3.1 8x13B instruct4 vs Claude Opus 5
- LLM-jp-3.1 8x13B instruct4 vs Claude Sonnet 5
- LLM-jp-3.1 8x13B instruct4 vs Claude Fable 5.1
- LLM-jp-3.1 8x13B instruct4 vs Claude Fable 5
- LLM-jp-3.1 8x13B instruct4 vs Claude Opus 4.8
- LLM-jp-3.1 8x13B instruct4 vs Claude Haiku 4.5
- LLM-jp-3.1 8x13B instruct4 details