Best AI models for Japanese
The same text can need a different number of tokens in Japanese than in English, and the gap depends on the model. We measured it ourselves, then combined it with independent quality scores and current prices to show which models give the best results for your money in Japanese.
The cost of Japanese, by model family
| Tokenizeri | Tokens (English) | Tokens (Japanese) | Extra vs English |
|---|---|---|---|
| Google (Gemma 3) | 218 | 255 | +17% |
| Meta (Llama 4) | 219 | 291 | +33% |
| Alibaba (Qwen 3) | 219 | 308 | +41% |
| Z.ai (GLM-4.5) | 219 | 335 | +53% |
| DeepSeek (V3.1) | 219 | 338 | +54% |
| OpenAI (GPT-4o) | 219 | 353 | +61% |
| Mistral (Nemo) | 220 | 375 | +70% |
Anthropic (Claude) and xAI (Grok) do not publish their tokenizers, so they cannot be measured; for their models we use the average of the measured families (marked ≈). Google’s Gemini is estimated with Google’s open Gemma tokenizer.
Most capable models and their real cost in Japanese
Rankings →| Model | Overall indexi | Japanese factor | Inputi | 1,000 pages |
|---|---|---|---|---|
GPT-6 AstraOpenAI | 166.6 | ×1.61 | $10 | $5.46 |
Claude Fable 5.1Anthropic | 165.0 | ≈ ×1.47 | $10 | $4.98 |
Claude Fable 5Anthropic | 163.6 | ≈ ×1.47 | $10 | $4.98 |
Claude Opus 5Anthropic | 162.7 | ≈ ×1.47 | $5 | $2.49 |
GPT-5.5 ProOpenAI | 162.5 | ×1.61 | $30 | $16.38 |
GPT-5.6 SolOpenAI | 162.0 | ×1.61 | $2 | $1.09 |
GPT-5.6 TerraOpenAI | 159.3 | ×1.61 | $2 | $1.09 |
GPT-5.5OpenAI | 159.3 | ×1.61 | $5 | $2.73 |
GPT-5.4 ProOpenAI | 159.1 | ×1.61 | $30 | $16.38 |
Claude Opus 4.8Anthropic | 158.3 | ≈ ×1.47 | $5 | $2.49 |
Gemini 3.7 FlashGoogle DeepMind | 157.7 | ×1.17 | $0.75 | $0.296 |
Kimi K3Moonshot AI | 157.7 | ≈ ×1.47 | $3 | $1.49 |
Gemini 3.8 FlashGoogle DeepMind | 157.1 | ×1.17 | $0.75 | $0.296 |
Muse Spark 1.3Meta | 156.9 | ×1.33 | $1.25 | $0.563 |
GPT-5.4OpenAI | 156.9 | ×1.61 | $2.50 | $1.36 |
Cost to read 1,000 pages of text (about 300,000 words in English) written in Japanese, as input. Each model gets the factor measured on its company’s latest public tokenizer, so treat it as an estimate: newer models may use a different tokenizer. ≈ marks companies that publish no tokenizer (average of measured families).
Best value for Japanese
Strong models (overall index 140 or more), cheapest first for processing Japanese text.
- $0.0094 / 1,000 pages
- $0.014 / 1,000 pages
- $0.03 / 1,000 pages
- $0.031 / 1,000 pages
- $0.035 / 1,000 pages
- $0.071 / 1,000 pages
- $0.075 / 1,000 pages
- $0.077 / 1,000 pages
Tips for using AI in Japanese
- Ask for answers in Japanese explicitly, especially when your question contains English terms; otherwise some models switch to English.
- Test with your own content. Quality scores are measured mostly in English, so try your finalists on real Japanese examples before you decide.
- Compare token costs. Depending on the model, the same content in Japanese costs between +17% and +70% compared with English, and Google (Gemma 3) handles it most efficiently.
- Name your audience. Vocabulary, tone and formality vary between regions and situations: tell the model who you are writing for and how formal it should be.
- Proofread names, numbers and punctuation. Generated text can be fluent yet get proper names, dates or local conventions wrong, so review anything you publish.
How we measured
We wrote the same four texts (a news item, a customer email, a technical explanation and travel tips) in 15 languages, then counted the tokens that each public tokenizer produces. Quality scores come from Epoch AI and are measured mostly in English, so always test the finalists on your own Japanese content.
Get a personal recommendationCount the tokens of your own text