R1 0528 vs GPT-5.4 for Multilingual

What Multilingual demands: equivalent fluency, faithfulness, cultural awareness, and consistent formatting across non-English languages. Key capabilities that matter for this task: core multilingual quality (fluency and fidelity), structured_output (JSON/schema compliance when translations feed downstream systems), safety_calibration (correctly refusing or permitting sensitive requests in other languages), long_context (handling long bilingual documents), and integration (tool_calling) where workflows call external translation or QA tools. In our testing both models score 5/5 on the Multilingual test itself, so raw multilingual quality is equal. Use supporting internal benchmarks to separate them: GPT-5.4 has structured_output 5 vs R1 0528's 4 and safety_calibration 5 vs 4, indicating more reliable schema adherence and safer behavior in non-English content. R1 0528 scores 5 on tool_calling and classification, and costs far less per mTok, but its documented quirk — it "returns empty responses on structured_output" in our testing — can break schema-dependent pipelines unless you work around its requirement for large completion tokens.

Where GPT-5.4 shines (based on scores):

• Automated multilingual API that must return strict JSON (structured_output 5 vs 4). If translations must conform to schemas for downstream parsing, GPT-5.4 reduces format errors.
• Regulated or safety-sensitive translations (safety_calibration 5 vs 4). When you need reliable refusal or careful handling of harmful content in other languages, GPT-5.4 is safer in our testing.
• Multimodal translation tasks requiring images/files plus text: GPT-5.4 supports text+image+file->text and a 1M+ token context window, helpful for long documents and mixed media.

Where R1 0528 shines (based on scores and cost):

• High-volume, low-cost translation pipelines: R1 0528 charges $0.50 input / $2.15 output per mTok vs GPT-5.4's $2.50 / $15 per mTok — a material saving for bulk workloads.
• Tool-driven localization workflows: R1 0528 scores 5 on tool_calling (vs GPT-5.4's 4), so it performs better in our testing when chaining external QA, glossary lookup, or project-management tools.

Caveat grounded in testing: both models scored 5/5 on multilingual quality in our suite, so choose based on these operational differences rather than raw translation quality.

For Multilingual, choose R1 0528 if you need low-cost, tool-integrated translation at scale (R1 0528: input $0.50/mtok, output $2.15/mtok) and can tolerate or work around its structured_output quirk. Choose GPT-5.4 if you require strict schema-compliant outputs and stronger safety calibration (structured_output 5 vs 4; safety_calibration 5 vs 4 in our testing), or if you need multimodal and very large-context handling despite higher costs (input $2.50/mtok, output $15/mtok).