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Do AI decision models work as well in Japanese as in English? In my evaluation, the answer depended on the kind of decision: choice performance transferred from Japanese training to English almost intact, while yes/no performance did not. A multilingual model also showed a striking first-option failure on a synthetic Japanese urgency test. Those results argue for testing the exact language, task, and option order you plan to use—not assuming one score represents them all.
What a decision model returns
A decision model can expose a typed interface instead of generating prose that an application must parse. In the account published on DEV Community on October 1, 2026, the model returns a probability for a yes/no question, a probability distribution over named options for a choice question, or an estimated ordered level and its distribution for a score question. The author describes these outputs as probabilities produced with zero output tokens.
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These output types call for different evaluations. A model may choose the right category but assign poorly calibrated probabilities; it may estimate an ordered score reasonably while favoring one option position; or its performance may change when the question is asked in another language. Accuracy alone cannot answer all three questions.
What happened in the Japanese evaluation
The multilingual model’s urgency scores
The author created 300 synthetic Japanese support messages in bench_ja, using three schemas not seen by the model: routing each message to one of four departments, scoring urgency on three levels, and flagging churn intent.
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For laya-multilingual, the author reports a ranked probability score (RPS) of 0.232 on urgency, worse than the 0.197 score from an always-majority-class baseline. The model selected the first-listed urgency level zero times in 300 cases, even though that level was correct for some messages. The author interprets this as position bias: the result suggests that option placement, not just the meaning of the labels, may have affected the model’s responses.
That is a result from one author-reported synthetic evaluation, not evidence that multilingual models generally fail on Japanese or that every position-sensitive result has the same cause. It does show why evaluators should record option order and inspect distributions rather than relying only on a final selected label.
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A practical option-order diagnostic
The author suggests a diagnostic that does not require knowing the correct answer: make the option descriptions identical and see whether the model spreads probability evenly. Then compare the log probability assigned to slot 0 with the mean across slots. Finally, permute three real options and measure how often the first slot is selected; if the model is order-neutral, the reference rate is one-third. This is a proposed diagnostic, not a formal standard, and it should complement tests against known answers.
The Japanese model and reported results
In the same October 1, 2026 account, the author says they built sokudan, an open 310-million-parameter Japanese decision model. On a separate set of 300 Japanese business messages, the author reports choice accuracy of 0.880, score RPS of 0.075, and binary AUROC of 0.844. The author also reports support for CPU, CUDA, and Apple Silicon via MLX, and provides installation with pip install sokudan.
These are the author’s reported figures and capabilities; they are not independent replications. The available account does not establish a full training recipe or broad generalization beyond its stated evaluations. Treat the metrics as results for those tasks and data, not a general ranking of Japanese decision models.
How well did Japanese-trained behavior transfer to English?
The author reports that choice performance transferred from Japanese training to English “almost intact,” while yes/no performance did not. That contrast is why transfer should be measured by decision type and direction rather than summarized as a single cross-language score.
A useful evaluation report distinguishes at least these dimensions:
- Language and direction: Japanese evaluation after Japanese training, English evaluation after Japanese training, and—where relevant—the reverse direction.
- Decision type: binary probability, categorical choice, or ordered score.
- Data and task: whether examples are synthetic, translated, adapted, or authored for the evaluation, and what the labels represent.
- Metric and baseline: report the metric appropriate to the output, alongside a meaningful baseline.
- Option order: test permutations when options are presented in a list.
- Evaluation conditions: hold prompt format and environment constant when comparing systems.
Why cross-language scores need careful reading
Other evaluations provide useful context, but they do not reproduce or verify the author’s sokudan results. The 2026 JOR-Bench preprint describes 1,319 problems across five Japanese-language operations-research benchmarks translated from English resources. Its authors report an average Japanese-versus-English accuracy difference of −0.3 percentage points for the strong multilingual models they evaluated. Their error analysis nevertheless identifies Japanese pragmatic-disambiguation problems in some domains. Near-equal aggregate accuracy therefore does not establish equivalent behavior in every task or linguistic situation. JOR-Bench (2026)
The 2024 Swallow-Evaluation project covers 10 Japanese and 9 English tasks across 35 large language models. It explicitly cautions that prompt formatting and evaluation-environment differences can affect scores independently of model performance. Swallow-Evaluation
The 2024 Open Japanese LLM Leaderboard overview describes a 16-task suite that includes both datasets created with human expertise and datasets translated or adapted to Japanese. These are different forms of evidence: a translated benchmark tests performance on translated material, while a Japanese-authored task can surface language-specific behavior that translation may not capture. Open Japanese LLM Leaderboard
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What Japanese-language procurement guidance adds
Japan’s Digital Agency lists alignment with the Japanese linguistic and cultural environment as an optional additional procurement criterion. Its guideline also calls for documentation of verification policies and Japanese-language benchmark results, and notes that selecting or combining models can be useful because their functions and behavior differ. This is procurement guidance, not evidence that any particular model meets those criteria. Digital Agency generative AI procurement guideline
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The author’s experiments make a focused case for measuring the behavior a system will actually need: a model can show a position effect in one Japanese scoring test, promising reported results on separate Japanese business-message tasks, and uneven transfer by decision type. They do not establish that decision models as a category are English-first, that Japanese-only training is generally superior, or that the reported outcomes will hold across other models and datasets.
For a deployment decision, evaluate the intended language and task directly, report both directions when transfer matters, and inspect calibration and option-order effects separately from whether the chosen outcome is correct. Use controlled prompts and conditions so differences are less likely to be artifacts of the evaluation setup.
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