LLM-jp-3.1 8x13B instruct4 vs Mercury 2.5
Pricing, context window and real answers (September 2026)
LLM-jp-3.1 8x13B instruct4 (LLM-jp) and Mercury 2.5 (Inception), 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
| LLM-jp-3.1 8x13B instruct4 | Mercury 2.5 | |
|---|---|---|
| Provider | LLM-jp | Inception |
| Input (per 1M tokens) | ¥16 | ¥6.77 |
| Output (per 1M tokens) | ¥79 | ¥25.38 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | ¥56 | ¥19 |
| Context window | 4,096 tokens | 260,000 tokens |
| Release date | 3/21/2026 | 9/8/2026 |
| Input modalities | text | text |
| Arena · overall | Unranked | Unranked |
| Arena · Japanese | Unranked | Unranked |
| Arena · coding | Unranked | Unranked |
Prices are FastMetal's yen rates before tax. Arena ranks are as of the latest leaderboard scrape.
Which should you pick?
- On price, Mercury 2.5: ¥25.38 per 1M output tokens, 68% less than LLM-jp-3.1 8x13B instruct4.
- For long documents, Mercury 2.5: a 260,000-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.
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
Mercury 2.5
To determine the number of 'r's in the word "strawberry", we can examine each letter in sequence:
1. **s** - Not 'r'
2. **t** - Not 'r'
3. **r** - First 'r' found
4. **a** - Not 'r'
5. **w** - Not 'r'
6. **b** - Not 'r'
7. **e** - Not 'r'
8. **r** - Second 'r' found
9. **r** - Third 'r' found
10. **y** - Not 'r'
Counting these occurrences, there are **3** 'r's in "strawberry".
1.8s968 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…
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
Mercury 2.5
This error occurs because the `users` prop is `undefined` when the component renders. Since the code calls `.map()` on `users`, JavaScript throws a TypeError when `users` is not an array.
Here is how to fix it:
1. **Set a Default Value**
Update the function signature to default `users` to an empty array if it is not provided:
```javascript
function UserList({ users = [] }) {
// ...
}
```
2. **Add a Conditional Check**
If `users` might be null or undefined during data loading, guard the map operation:
```javascript
{users && users.map(user => <li key={user.id}>{user.name}</li>)}
```
3. **Verify Parent Props**
Ensure the parent component pas…
2.4s1028 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):…
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
Mercury 2.5
Here is a review of the function with suggestions for improvement.
### Key Observations
1. **Imports:** Libraries should be imported at the module level, not inside functions.
2. **Exception Handling:** Bare `except:` clauses catch system exits and interrupts. Use specific exceptions like `requests.exceptions.RequestException`.
3. **Logic & Pythonicity:**
* Avoid `== True`; rely on truthiness or `item.get("active")`.
* List comprehensions are more concise and efficient than manual appending.
4. **Retry Strategy:** Implement exponential backoff instead of fixed sleeps, and avoid sleeping after the final failed attempt.
5. **Documentation:** Add type hints and a docstring fo…
4.6s1999 output tokens
Frequently asked questions
- Which is cheaper, LLM-jp-3.1 8x13B instruct4 or Mercury 2.5?
- Per 1M output tokens, LLM-jp-3.1 8x13B instruct4 is ¥79 and Mercury 2.5 is ¥25.38 on FastMetal (yen, before tax), so Mercury 2.5 is cheaper.
- How do the context windows of LLM-jp-3.1 8x13B instruct4 and Mercury 2.5 compare?
- LLM-jp-3.1 8x13B instruct4 takes 4,096 tokens; Mercury 2.5 takes 260,000.
- Can I use LLM-jp-3.1 8x13B instruct4 and Mercury 2.5 with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "llm-jp-3.1-8x13b-instruct4" or "mercury-2.5" 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 LLM-jp-3.1 8x13B instruct4 and Mercury 2.5 from the browser chat and the API. No monthly fee.
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