For Chinese users: evaluate your own language data

Why this deserves its own chapter

The official model page describes multilingual support while identifying English as the strongest language at this time. An English demonstration does not establish quality on Chinese, mixed-language messages or industry jargon.

Define acceptance before demonstrating

Choose a low-impact task such as labeling redacted support emails. Write clear labeling rules. Keep development data separate from a held-out test set. A few dozen examples can expose obvious issues, but a small sample cannot establish stable production accuracy.

Bundled examples are teaching material, not a representative industry benchmark.

Compare three baselines

Measure simple rules, your existing model and Jev on the same inputs, definitions and scoring rules. Record provider, model ID, prompt version, date, retries and batching choices.

Report more than one accuracy number

Track classification accuracy, automated coverage, error rate among accepted decisions, end-to-end p50 / p95 latency and total cost. For imbalanced classes, inspect class-level recall and confusion matrices. Human review adds work and belongs in the comparison.

Evidence still needed

Collect authorized Chinese examples, failures, measurements from target user regions and regression checks after version changes. Until those exist, this site does not claim verified Chinese reliability, guaranteed mainland access or a fixed speedup.