Quick answer
The model that wins on single sentences is not automatically the model that wins on a 200-page document. For documents, the deciding factor is whether the model stays consistent with itself from start to finish.
What matters in a model for document translation
- Long-context retention: whether page 180 is still translated the way page 3 was
- Terminology control: whether a defined term survives unchanged, especially when a glossary is supplied
- Structural awareness: whether tables, lists, and headings are treated as structure rather than as prose
- Handling of things that should not be translated at all — code, formulas, part numbers, units
- Behaviour on the language pair you actually use, which can differ sharply from its average performance
Common mistakes when choosing
- Judging by parameter count or benchmark score instead of output on your document type
- Testing with one paragraph, which is exactly the case where all serious models look similar
- Ignoring consistency and grading only fluency — a fluent translation that renames a term every ten pages is worse than a plain one that does not
- Assuming the newest model is best for your language pair without checking
How to test a model on your own document
- Run the same real file through more than one model rather than a sample paragraph
- Search the output for a key term and check every occurrence matches
- Check tables and numbers before checking prose quality
- Repeat with a glossary attached to see how well the model honours it
Where to compare
Belin Doc lets you pick the model per job — see the available models for what each one is suited to, and premium translation for the highest-fidelity pipeline.
Bottom line
Choose on whole-document consistency, not sentence-level polish. Test on your own file, with your own terminology, in your own language pair.