Choosing a translator for the texts on your desk
A folder of support replies asks different things of a translation model than a folder of contracts. Both need accurate language, but the expensive mistakes differ: an awkward greeting in one, a changed obligation in the other. Choose a model by comparing those mistakes, not by looking for a universal winner.
A useful starting pair is Gemini 3.8 Flash and Claude Sonnet 5.5. Treat them as candidates to test, not as a predetermined ranking. The comparison below separates two kinds of translation work and gives each candidate the same evidence to work from.
§ Short, repetitive texts beside context-heavy prose
Support replies, product fields, and interface strings: prioritize stable terminology, intact placeholders, and predictable output. A translation may read naturally yet still fail if it changes a variable name or uses three different terms for the same button. For this workload, test Gemini 3.8 Flash first, then run the same sample through Claude Sonnet 5.5 as a comparison.
Editorial pages, policy explanations, and customer letters: prioritize relationships across sentences, tone, and implied meaning. A sentence-by-sentence translation can preserve individual facts while losing the argument. For this workload, reverse the trial order: start with Claude Sonnet 5.5, then compare Gemini 3.8 Flash.
The starting order is a testing convenience, not a claim about either model’s measured quality. Your language pair, subject matter, and review standard determine which result is usable.
§ Constrained instructions beside interpretive instructions
For repetitive texts: supply a glossary, a target locale, and explicit preservation rules. State that identifiers, placeholders, markup, and product names must remain unchanged. Specify whether the response must contain only the translation. Check these requirements mechanically wherever possible.
For context-heavy texts: add the intended reader, document purpose, and surrounding paragraphs. Explain whether the translation should closely follow the source or permit restructuring for natural expression. If ambiguity matters, request a separate note rather than letting the model silently resolve it.
Keep those instructions identical across candidates within each test. Otherwise, you may be measuring a better prompt rather than a better model. When you revise the prompt, rerun both candidates.
§ Cheap drafts beside cheap approved translations
For repetitive texts: record token charges, turnaround time, and the percentage of items accepted without edits. A low-cost response is useful only if it passes the required checks. Repeated terminology fixes can erase an apparent saving.
For context-heavy texts: record reviewer minutes alongside token charges. Ask reviewers to mark omissions, altered meaning, register problems, and unnecessary rewriting separately. A polished paragraph should not receive a high score if it changes a condition or weakens a warning.
Vana API bills per real token with no monthly fee, making a bounded comparison practical. Still, compare the cost of an approved translation, not just the cost of generating one. For legal, medical, or other consequential material, retain qualified human review regardless of which candidate scores better.
§ A shared test beside a shared workflow
Build a small evaluation set from actual work, with permission to process it. Include ordinary passages and difficult cases: ambiguous pronouns, regional vocabulary, long sentences, numbers, and embedded formatting. Remove sensitive information where necessary. Hide model names from reviewers and alternate the order of outputs.
- Meaning: are facts, qualifications, and obligations preserved?
- Language: does the text suit the target locale and audience?
- Consistency: are approved terms used throughout?
- Structure: do placeholders, lists, and output fields survive?
Vana API provides one OpenAI-compatible endpoint and also supports Anthropic Messages and Gemini native protocols. You can keep the comparison harness in an OpenAI SDK workflow while changing the selected model, rather than rebuilding the integration for each trial. Create an account when you are ready to run a small, representative batch.
The final choice may be a split: one model for structured, repeatable material and another for documents needing closer contextual review. Keep the test set, record the decision, and revisit it when your texts change. That is a more durable selection method than choosing by reputation alone.