Japanese Language - AI Models Benchmark Compare Multilingual LLM Performance

Name: Intelligence
Creator: Artificial Analysis
License: https://artificialanalysis.ai/docs/legal/Terms-of-Use.pdf

The top 5 AI models for Japanese language tasks are Gemini 3.1 Pro Preview, Gemini 3 Pro Preview (high), Claude Opus 4.6 (max), Claude Opus 4.6 (Non-reasoning, high), and Claude Opus 4.5. They achieve the highest Japanese language reasoning scores in the Artificial Analysis Multilingual Index.

To compare performance across all supported languages, see the full Multilingual AI Model Benchmark page.

🇯🇵 Top models for Japanese language

Gemini 3.1 Pro Preview

94#2

Gemini 3 Pro Preview (high)

93#3

Claude Opus 4.6 (max)

93#4

Claude Opus 4.6 (Non-reasoning, high)

93#5

Claude Opus 4.5

Highlights

Intelligence

Multilingual Index: Japanese · Higher is better

Speed

Output tokens per second · Higher is better

Price

USD per 1M tokens (blended) · Lower is better

Multilingual Index

Multilingual Index: Japanese Language

Artificial Analysis Multilingual Index · Higher is better

Based on the Global-MMLU-Lite evaluation, assessing general reasoning performance in a single language. Results are computed exclusively within the selected language. See methodology for further details.

Multilingual Index: Japanese Language vs. Price

Artificial Analysis Multilingual Index · USD per 1M tokens (blended)

Most attractive quadrant

While higher intelligence models are typically more expensive, they do not all follow the same price-quality curve.

Multilingual Index: Japanese Language vs. Output Speed

Artificial Analysis Multilingual Index · Output speed: output tokens per second

Most attractive quadrant

There is a trade-off between model quality and output speed, with higher intelligence models typically having lower output speed.

Multilingual Index: Japanese Language vs. Context Window

Artificial Analysis Multilingual Index · Context window: tokens limit

Most attractive quadrant

Global-MMLU-Lite

Multilingual Global-MMLU-Lite: Japanese Language

Multilingual Global-MMLU-Lite · Higher is better

A multilingual version of Massive Multitask Language Understanding, evaluated across multiple languages. Tests general knowledge and reasoning ability in areas like science, humanities, mathematics and more. See methodology for further details.

Pricing

Pricing: Cache Hit, Input, and Output

Price (USD per M Tokens)

Price per token for cached prompts (previously processed), typically offering a significant discount compared to regular input price, represented as USD per million tokens. The values shown here are the cache hit price; cache write and cache storage are billed separately and vary by provider — see "Cache pricing by provider" for detail.

Speed & Latency

Output Speed

Output tokens per second · Higher is better

Tokens per second received while the model is generating tokens (ie. after first chunk has been received from the API for models which support streaming).

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Latency: Time To First Answer Token

Seconds to first answer token received · Accounts for reasoning model 'thinking' time

Time to first answer token received, in seconds, after API request sent. For reasoning models, this includes the 'thinking' time of the model before providing an answer. For models which do not support streaming, this represents time to receive the completion.

End-to-End Response Time

Seconds to output 500 tokens, including reasoning model 'thinking' time · Lower is better

Seconds to receive a 500 token response. Key components:

Input time: Time to receive the first response token
Thinking time (only for reasoning models): Time reasoning models spend outputting tokens to reason prior to providing an answer. Amount of tokens based on the average reasoning tokens across a diverse set of 60 prompts (methodology details).
Answer time: Time to generate 500 output tokens, based on output speed

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Japanese Language - AI Models Benchmark Compare Multilingual LLM Performance

🇯🇵 Top models for Japanese language

Intelligence

Speed

Price

Multilingual Index

Multilingual Index: Japanese Language

Per-Language Reasoning Index

Multilingual Index: Japanese Language vs. Price

Price-Quality Variance

Multilingual Index: Japanese Language vs. Output Speed

Intelligence vs. Speed Trade-off

Multilingual Index: Japanese Language vs. Context Window

Per-Language Reasoning Index

Global-MMLU-Lite

Multilingual Global-MMLU-Lite: Japanese Language

Global-MMLU-Lite

Pricing

Pricing: Cache Hit, Input, and Output

Cache Hit

Speed & Latency

Output Speed

Output Speed

Model Performance Representation

Latency: Time To First Answer Token

Time to First Answer Token

End-to-End Response Time

End-to-End Response Time

Model Performance Representation