LLM-jp-3.1 8x13B instruct4 vs Qwen3.8 2.4T A95B
Pricing, context window and real answers (August 2026)
LLM-jp-3.1 8x13B instruct4 (Llm Jp) and Qwen3.8 2.4T A95B (Qwen), 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 |  Qwen3.8 2.4T A95B | |
|---|---|---|
| Provider | Llm Jp | Qwen |
| Input (per 1M tokens) | ¥16 | ¥351.83 |
| Output (per 1M tokens) | ¥79 | ¥1,055.5 |
| Typical cost (1,000 in + 500 out tokens × 1,000 calls) | ¥56 | ¥880 |
| Context window | 4,096 tokens | 1,048,576 tokens |
| Release date | 3/21/2026 | 8/12/2026 |
| 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, LLM-jp-3.1 8x13B instruct4: ¥79 per 1M output tokens, 93% less than Qwen3.8 2.4T A95B.
- For long documents, Qwen3.8 2.4T A95B: 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.
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
Qwen3.8 2.4T A95B
To count the number of **“r”**s in **“strawberry”**, spell it out letter by letter:
**s t r a w b e r r y**
Now check each letter:
1. s — not r
2. t — not r
3. r — yes, count 1
4. a — not r
5. w — not r
6. b — not r
7. e — not r
8. r — yes, count 2
9. r — yes, count 3
10. y — not r
So there are **3** “r”s in **strawberry**.
1.3s203 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
Qwen3.8 2.4T A95B
The error happens because `users` is `undefined` when React tries to render this line:
```js
{users.map(user => <li key={user.id}>{user.name}</li>)}
```
JavaScript cannot call `.map()` on `undefined`.
In your component:
```js
function UserList({ users }) {
return (
<ul>
{users.map(user => <li key={user.id}>{user.name}</li>)}
</ul>
);
}
```
React expects `users` to be an array, but something is rendering `<UserList />` without providing a valid `users` prop.
---
## Common causes
### 1. The parent component is not passing `users`
For example:
```js
<UserList />
```
Instead of:
```js
<UserList users={users} />
```
---
### 2. The data has not loaded yet
If `users…
10.9s1318 output tokens
Professional Email
Write a professional email to a client explaining that the project delivery will be delayed by two weeks due to unexpected technical challenges discovered during integration testing. The tone should be apologetic but con…
LLM-jp-3.1 8x13B instruct4
Subject: Important Update on Project Timeline and Mitigation Steps for Integration Testing Challenges
Dear [Client's Name],
I hope this message finds you well. I am writing to provide an update on our project and address the unexpected technical challenges we have encountered during the integration testing phase.
We appreciate your patience as we work diligently to ensure the successful delivery of your project. Unfortunately, we have discovered some unforeseen complexities that require additional time to resolve. Specifically, the integration testing has revealed critical issues that need thorough examination and resolution, which will unfortunately cause a delay in the original project…
4.7s505 output tokens
Qwen3.8 2.4T A95B
Subject: Updated Project Timeline – Revised Delivery Date
Dear [Client Name],
I hope this message finds you well.
I’m writing to provide an important update regarding the delivery timeline for [Project Name]. During our recent integration testing phase, we identified several unexpected technical challenges that require additional resolution to ensure the final deliverable meets the quality and performance standards we committed to.
After carefully reviewing the scope of these items with our technical team, we have determined that the project delivery will need to be extended by two weeks. I sincerely apologize for any inconvenience this may cause. Please be assured that we are taking imm…
4.6s449 output tokens
Frequently asked questions
- Which is cheaper, LLM-jp-3.1 8x13B instruct4 or Qwen3.8 2.4T A95B?
- Per 1M output tokens, LLM-jp-3.1 8x13B instruct4 is ¥79 and Qwen3.8 2.4T A95B is ¥1,055.5 on FastMetal (yen, before tax), so LLM-jp-3.1 8x13B instruct4 is cheaper.
- How do the context windows of LLM-jp-3.1 8x13B instruct4 and Qwen3.8 2.4T A95B compare?
- LLM-jp-3.1 8x13B instruct4 takes 4,096 tokens; Qwen3.8 2.4T A95B takes 1,048,576.
- Can I use LLM-jp-3.1 8x13B instruct4 and Qwen3.8 2.4T A95B with the same API key?
- Yes. On FastMetal's OpenAI-compatible endpoint, switch by passing "llm-jp-3.1-8x13b-instruct4" or "qwen3.8-2.4t-a95b" 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 Qwen3.8 2.4T A95B from the browser chat and the API. No monthly fee.