DeepSeek V4 Pro vs LLM-jp-3.1 8x13B instruct4
Pricing, context window and real answers (August 2026)
DeepSeek V4 Pro (DeepSeek) 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
 DeepSeek V4 Pro | LLM-jp-3.1 8x13B instruct4 | |
|---|---|---|
| Provider | DeepSeek | Llm Jp |
| Input (per 1M tokens) | ¥341.32 | ¥16 |
| Output (per 1M tokens) | ¥684.42 | ¥79 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | ¥684 | ¥56 |
| Context window | 1,048,576 tokens | 4,096 tokens |
| Release date | 4/24/2026 | 3/21/2026 |
| Input modalities | text | text |
| Arena · overall | #54 · ELO 1,458 | Unranked |
| Arena · Japanese | #22 · ELO 1,463 | Unranked |
| Arena · coding | #59 · ELO 1,502 | 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, 88% less than DeepSeek V4 Pro.
- For long documents, DeepSeek V4 Pro: a 1,048,576-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.
DeepSeek V4 Pro
The word is "strawberry". Let's count the occurrences of the letter 'r' step by step:
1. Write the word: s t r a w b e r r y
2. Examine each character:
- s: not an 'r'
- t: not an 'r'
- r: is an 'r' → count 1
- a: not an 'r'
- w: not an 'r'
- b: not an 'r'
- e: not an 'r'
- r: is an 'r' → count 2
- r: is an 'r' → count 3
- y: not an 'r'
3. Total number of 'r's = 3.
Therefore, there are **3** 'r's in "strawberry".
7.3s1136 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…
DeepSeek V4 Pro
The error is occurring because `users` is `undefined` when the component renders, and you're trying to call `.map()` on it. This typically happens when the data hasn't been loaded yet or wasn't passed correctly to the component.
## Here are several ways to fix this:
### 1. **Add a default value with empty array (Simplest fix)**
```javascript
function UserList({ users = [] }) {
return (
<ul>
{users.map(user => <li key={user.id}>{user.name}</li>)}
</ul>
);
}
```
### 2. **Add a conditional check**
```javascript
function UserList({ users }) {
if (!users) {
return <div>No users found</div>;
}
return (
<ul>
{users.map(user => <li key={user.id}>{user.name}<…
6.9s442 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):…
DeepSeek V4 Pro
Here’s a review of your `get_data` function, highlighting issues and suggesting improvements.
## Overall Observations
- The function works but has several readability, robustness, and maintainability issues.
- Imports are inside the function, bare `except` hides errors, and the filtering logic is not Pythonic.
- No type hints or docstring, making it harder to use and understand.
## Specific Improvements
### 1. Move Imports to the Top of the Module
**Why:** Imports inside a function are executed every time the function is called, which is slightly slower and violates PEP 8 style guidelines.
**Before:**
```python
def get_data(url, retries=3):
import requests
import time
# ...
`…
40.5s2277 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, DeepSeek V4 Pro or LLM-jp-3.1 8x13B instruct4?
- Per 1M output tokens, DeepSeek V4 Pro is ¥684.42 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 DeepSeek V4 Pro and LLM-jp-3.1 8x13B instruct4 compare?
- DeepSeek V4 Pro takes 1,048,576 tokens; LLM-jp-3.1 8x13B instruct4 takes 4,096.
- Can I use DeepSeek V4 Pro and LLM-jp-3.1 8x13B instruct4 with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "deepseek-v4-pro" 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 DeepSeek V4 Pro and LLM-jp-3.1 8x13B instruct4 from the browser chat and the API. No monthly fee.