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Japanese Language AI Models Benchmark Compare Multilingual LLM Performance

The top 5 Japanese language AI models are Gemini 3.1 Pro Preview, Gemini 3 Pro Preview (high), Claude Opus 4.6 (max), Claude Opus 4.6, and Claude Sonnet 4.6 (max). 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 Japanese language models

#1
Gemini 3.1 Pro Preview
Gemini 3.1 Pro Preview
94
#2
Gemini 3 Pro Preview (high)
Gemini 3 Pro Preview (high)
93
#3
Claude Opus 4.6 (Adaptive Reasoning, Max Effort)
Claude Opus 4.6 (max)
93
#4
Claude Opus 4.6 (Non-reasoning, High Effort)
Claude Opus 4.6
93
#5
Claude Sonnet 4.6 (Adaptive Reasoning, Max Effort)
Claude Sonnet 4.6 (max)
93
Intelligence
Multilingual Index: Japanese; Higher is better
Speed
Output Tokens per Second; Higher is better
Price
USD per 1M Tokens; Lower is better

Multilingual Index: Japanese Language

Artificial Analysis Multilingual Index; Higher is better
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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; Price: USD per 1M Tokens
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Most attractive quadrant
Claude 4.5 Sonnet
Claude Opus 4.5
DeepSeek V3.2
Gemini 3 Pro Preview (high)
GPT-5.2 (medium)
gpt-oss-120B (high)
Grok 4
Llama 4 Maverick
Magistral Medium 1.2
MiniMax-M2.1
MiniMax-M2.5

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

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.

Price per token, represented as USD per million Tokens. Price is a blend of Input & Output token prices (3:1 ratio).

Multilingual Index: Japanese Language vs. Output Speed

Artificial Analysis Multilingual Index; Output Speed: Output Tokens per Second
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Most attractive quadrant
Claude 4.5 Sonnet
Claude Opus 4.5
DeepSeek V3.2
Gemini 3 Pro Preview (high)
gpt-oss-120B (high)
Grok 4
Llama 4 Maverick
Magistral Medium 1.2
MiniMax-M2.1
MiniMax-M2.5

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

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.

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).

Price per token, represented as USD per million Tokens. Price is a blend of Input & Output token prices (3:1 ratio).

Multilingual Index: Japanese Language vs. Context Window

Artificial Analysis Multilingual Index; Context Window: Tokens Limit
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Most attractive quadrant
Claude 4.5 Sonnet
Claude Opus 4.5
DeepSeek V3.2
Gemini 3 Pro Preview (high)
GPT-5.2 (medium)
gpt-oss-120B (high)
Grok 4
K-EXAONE
K2-V2 (high)
Llama 4 Maverick
Magistral Medium 1.2
MiniMax-M2.1
MiniMax-M2.5

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.

Maximum number of combined input & output tokens. Output tokens commonly have a significantly lower limit (varied by model).

Price per token included in the request/message sent to the API, represented as USD per million Tokens.

Multilingual Global-MMLU-Lite: Japanese Language

Multilingual Global-MMLU-Lite; Higher is better
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Pricing: Input and Output Prices

Price: USD per 1M Tokens
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Input price
Output price
Reasoning models are indicated by a lightbulb icon.

Price per token included in the request/message sent to the API, represented as USD per million Tokens.

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).

Output Speed

Output Tokens per Second; Higher is better
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Reasoning models are indicated by a lightbulb icon.

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
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Input processing
Thinking (reasoning models, when applicable)
Reasoning models are indicated by a lightbulb icon.

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
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Input processing time
'Thinking' time (reasoning models)
Outputting time
Reasoning models are indicated by a lightbulb icon.

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).