R1 0528 vs GPT-5.4 for Translation

Translation requires two primary capabilities: multilingual competence (produce equivalent quality in target languages) and faithfulness (preserve meaning and avoid hallucination). In our suite the task is measured by multilingual and faithfulness tests; both models score 5/5 on those dimensions and are tied for rank 1 of 52. Supporting capabilities that influence real-world translation: long_context for large documents (both score 5/5; R1 0528 context window = 163,840 tokens, GPT-5.4 = 1,050,000 tokens), structured_output for JSON or CAT-tool schema adherence (GPT-5.4 scores 5 vs R1 0528 at 4), tool_calling for integrating with translation toolchains (R1 0528 scores 5 vs GPT-5.4 at 4), and safety_calibration for handling sensitive or restricted content (GPT-5.4 scores 5 vs R1 0528 at 4). Important quirk for R1 0528: it can return empty responses on structured_output and consumes reasoning tokens from the output budget, requiring high max-completion tokens and sometimes yielding empty results on short structured tasks; that reduces its reliability for strict schema outputs despite a high multilingual/faithfulness score. Pricing and throughput are also decisive: R1 0528 output cost is $2.15/MTOK vs GPT-5.4 at $15.00/MTOK, which matters for large-volume localization.

1. High-volume website localization: Both score 5/5 on Translation quality, but R1 0528's output cost ($2.15/MTOK) vs GPT-5.4 ($15.00/MTOK) and tool_calling 5 make R1 0528 the cost-effective choice for automated pipelines that call translation functions and post-process outputs. 2) Strict TMS/CAT integration with JSON schemas: GPT-5.4 (structured_output 5) is better — R1 0528 has a structured_output score of 4 and a documented quirk that can return empty responses on structured_output, making GPT-5.4 more reliable for exact schema compliance. 3) Translating long, multimodal documents (ebooks, slides with images): GPT-5.4’s 1,050,000-token context window and modality support (text+image+file->text) outperform R1 0528’s 163,840-token window when you need single-pass translation across images and long source files. 4) Sensitive or regulated content: GPT-5.4’s safety_calibration 5 vs R1 0528’s 4 means GPT-5.4 is safer at refusing or correctly handling restricted content in our tests. 5) Language routing and variant classification: R1 0528’s classification 4 (vs GPT-5.4’s 3) gives it an edge when you need accurate dialect/language detection before translation.

For Translation, choose R1 0528 if you need 5/5 translation quality at much lower output cost ($2.15 vs $15.00/MTOK), tight tool integration (tool_calling 5), and high-throughput pipelines. Choose GPT-5.4 if you require strict structured-output/JSON reliability (structured_output 5), the largest document/context support (1,050,000-token window), multimodal inputs (files/images), or stronger safety calibration (5). Both models score 5/5 on multilingual and faithfulness in our tests and are tied for 1st, so pick based on cost, schema reliability, context size, and modality needs.