Claude Sonnet 5 vs GPT-5.6 Sol
for policy translation

The bottom line
No proof exists so workflow decides

There is no public, like-for-like benchmark proving which model keeps a glossary more consistent across a long policy document. GPT-5.6 Sol is the quality-first choice for a single-pass translation, since it has stronger general-capability evidence. Claude Sonnet 5 is the safer default for a cost-controlled translate-and-review workflow.

That split rests on a small commercial document-translation test that includes Sol but not Sonnet¹, a general reasoning index², and the published token prices^5, 4, not on a dedicated glossary-consistency benchmark, since none exists publicly for these exact models.

The practical rule is to match the model to how the workflow is structured. If the translation must be right in one pass with no budget for a second review, Sol's stronger general-capability evidence is the safer bet. If the team can run a translate-then-audit process, Sonnet's lower price and steady full-window rate make that process cheaper to run twice.

Who this is for
Which localization roles this fits

What we compared
Terminology not the app

This page compares the two models through their API in one neutral setup, not one model inside a localization platform's translation memory against the other inside a different tool.

The parts that matter for this task are glossary adherence across a long document, consistency across distant sections, preserving numbering and cross-references, and fluency in a formal register. Official docs and the closest independent evidence come first.

We left tools out of the spec table on purpose. A translation-management platform's memory, project workflow or reviewer assignment depends on the app around the model, so the same model can behave differently in a chat product, the API or a workspace. Judging those here would compare software, not translation quality.

Specs at a glance
The translation-relevant numbers

The model facts that actually affect translating a long policy document. Tool features are left out, since they change with the app around the model.

Head to head
Where each model wins on translation

The answer changes by part of the job, not by brand. This is the main analysis: which model has the edge on each part of translating a policy document, and what backs it up.

How to test
A fair test on your own documents

A useful test feels boring. Same glossary, same source, no editing before scoring. Then judge what your team actually pays for: the share of glossary occurrences using the approved term, and how much a bilingual reviewer had to fix.

What the evidence shows
Thin on this exact task

The direct evidence for this exact comparison is thin. Here is what each source actually helps judge.

How to prompt each one
State the glossary's scope explicitly

Both models need the glossary's scope stated explicitly, since neither should be assumed to generalize a rule from one section to every later one.

Claude Sonnet 5's documented literalism favors an explicitly scoped, structured instruction that separates the binding glossary from the source text and states that every mapping applies to every occurrence, including headings, tables and cross-references⁶.

GPT-5.6 Sol does best with a lean prompt that states each instruction once, treats the glossary as binding, and makes a terminology audit a required output rather than an optional note⁸.

A Claude Sonnet 5 prompt: an explicitly scoped glossary

<task>Translate the complete policy into formal
German.</task>

<glossary>
<term source="Covered Person" target="versicherte
Person"/>
</glossary>

<rules>
Apply every glossary mapping to every occurrence in
every section, heading, table and cross-reference.
Do not use synonyms for defined terms. Preserve
numbering. After translating, report any occurrence
where the required term could not be used
grammatically.
</rules>

<document>...</document>

A GPT-5.6 Sol prompt: a required terminology audit

Translate the complete policy into formal German.

Treat the glossary as binding and use each approved
target term consistently in headings, body text,
tables and cross-references. Preserve legal force,
numbering and defined-term capitalization. If
grammar requires an inflected form, preserve the
same lexical term.

Return the translation followed by a terminology
audit listing each glossary term, occurrence count
and any deviation.

Weak spots
And how to fix them

The main risk for both models is a fluent passage that quietly drifts from the approved term. The useful question is where each one adds risk, and what to change in the prompt.

Which one to choose
Start from the cost of one wrong term

One question first. What is the consequence of one inconsistent defined term? Then follow the branch that matches your document.

Recommendations
Pick by the cost of one wrong term

If one inconsistent defined term carries a material legal, regulatory or employee-rights impact, start with GPT-5.6 Sol for the initial quality-first run, then require bilingual review either way¹.

If this is high-volume handbook localization with a mature glossary and QA process already in place, pick Claude Sonnet 5 and spend the savings on a separate review pass⁵. If any single input exceeds 272,000 tokens, Sonnet is also the safer default, since its price does not rise for a larger document^3, 4.

If the document has many ambiguous exceptions and cross-referenced definitions, start with GPT-5.6 Sol². For a rare or morphologically complex target language, treat neither model as a default until bilingual reviewers have scored a language-specific pilot.

One case neither model nor Playgram solves on its own: wiring translation straight into a translation-memory system with automatic termbase enforcement and no human sign-off. That is a localization workflow with its own tooling, not a chat workspace, so a team running that kind of pipeline should evaluate the models directly through Anthropic's or OpenAI's API rather than through Playgram.

Bottom line
Pick by pass count and budget

GPT-5.6 Sol is the better quality-first bet for a one-pass formal translation. Claude Sonnet 5 is the better production default when the team can enforce a translate-audit-revise process.

Neither model has proved, through a public exact-model head-to-head, that it will keep page-one terminology unchanged on page twenty. Benchmarks disagree across configurations, the available translation evidence is uneven, and context-window figures do not by themselves measure usable document consistency.

The safest final step is to test the shape of your own documents, not a generic policy from the internet. A fair test needs the same setup for both models: the same glossary, the same source and the same place to run them, so the result reflects the models and not the tool around them. In practice that is harder than it sounds, since most teams end up running one model in one app and the other in a different one, on two separate subscriptions, which tilts the comparison before the first draft comes back. The cleaner the setup, the more the difference you see is really Claude Sonnet 5 vs GPT-5.6 Sol, and not just which one happened to be easier to reach that day.

Frequently asked
questions

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