How much does the LLM-jp-3.1 8x13B instruct4 API cost?

On FastMetal, LLM-jp-3.1 8x13B instruct4 is billed per token in yen: ¥16 per 1M input tokens and ¥79 per 1M output tokens, before tax. Usage is drawn from a prepaid balance; there is no subscription or monthly fee.

Can I call LLM-jp-3.1 8x13B instruct4 with the OpenAI SDK?

Yes. Point base_url at https://api.fastmetal.ai/v1 and pass "llm-jp-3.1-8x13b-instruct4" as the model; existing OpenAI-style code works unchanged, including streaming, tool calls and structured output.

What is the context window of LLM-jp-3.1 8x13B instruct4?

4,096 tokens, shared between the prompt and the response.

What do I need to try LLM-jp-3.1 8x13B instruct4?

Create an account and add credit; the model is then available both in the browser chat and over the API. There is no contract or minimum spend.

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llm-jp/llm-jp-3-1-8x13b-instruct4

LLM-jp-3.1 8x13B instruct4

LLM-jp-3.1 8x13B instruct4 is a Japanese-specialized open Mixture-of-Experts model from Japan's National Institute of Informatics (NII), with 73B total and 22B active parameters. It is strongest on Japanese-language tasks — topping the humanities category and exceeding GPT-4-0613 on Japanese evaluations — while lagging frontier models on coding and math.

3/21/2026

4,096 tokens

Input: ¥16/M

Output: ¥79/M

Try in Chat Get API Key

Specifications

Modalities

Input

text

Output

text

Code Examples

curl https://api.fastmetal.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "llm-jp-3.1-8x13b-instruct4",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

How LLM-jp-3.1 8x13B instruct4 actually answers

Real responses to our standard prompts, recorded on FastMetal.

Count the number of 'r's in 'strawberry'

Count the number of 'r's in 'strawberry'. Explain your reasoning step by step.

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…

4.0s response400 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…

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 <…

3.0s response279 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):…

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…

3.9s response406 output tokens

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Frequently asked questions

How much does the LLM-jp-3.1 8x13B instruct4 API cost?: On FastMetal, LLM-jp-3.1 8x13B instruct4 is billed per token in yen: ¥16 per 1M input tokens and ¥79 per 1M output tokens, before tax. Usage is drawn from a prepaid balance; there is no subscription or monthly fee.
Can I call LLM-jp-3.1 8x13B instruct4 with the OpenAI SDK?: Yes. Point base_url at https://api.fastmetal.ai/v1 and pass "llm-jp-3.1-8x13b-instruct4" as the model; existing OpenAI-style code works unchanged, including streaming, tool calls and structured output.
What is the context window of LLM-jp-3.1 8x13B instruct4?: 4,096 tokens, shared between the prompt and the response.
What do I need to try LLM-jp-3.1 8x13B instruct4?: Create an account and add credit; the model is then available both in the browser chat and over the API. There is no contract or minimum spend.

Try LLM-jp-3.1 8x13B instruct4 right now

LLM-jp-3.1 8x13B instruct4 is available on FastMetal through one API key. Start in the browser, or call it from the OpenAI SDK.

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