GPT-5.4 vs Grok 4 for Translation

What Translation demands: high multilingual fluency, strict faithfulness to source meaning, and often the ability to preserve format (structured outputs) and handle long documents or localization memory. Our Translation task uses two primary tests: multilingual and faithfulness. Both GPT-5.4 and Grok 4 score 5/5 on those tests in our suite, so they match on core translation accuracy. Supporting capabilities that matter and that differentiate the models: safety calibration (important for refusing disallowed or culturally risky output), structured output (for JSON/CSV bilingual glossaries or CAT tool exports), and context window size (for book-length or enterprise localization). In our testing GPT-5.4 leads on safety calibration (5 vs 2) and structured output (5 vs 4), and its context_window is 1,050,000 tokens versus Grok 4's 256,000 — these supporting strengths explain why GPT-5.4 is the pragmatic winner for production localization despite the tied primary task scores. No external benchmark is provided for this task, so our internal test scores are the basis for the verdict.

Where GPT-5.4 shines (concrete scenarios):

• Large-document localization: translating a 200k-word manual with full context and glossary carryover — GPT-5.4's 1,050,000-token window avoids context chopping. (Context windows: GPT-5.4 = 1,050,000; Grok 4 = 256,000.)
• Safety-sensitive content: localizing medical disclaimers or regulatory text where refusal and correct handling of harmful prompts matter — GPT-5.4 scores 5 on safety calibration vs Grok 4's 2 in our tests.
• Format-preserving exports: producing strict JSON or CSV bilingual files for a CAT pipeline — GPT-5.4 scored 5 vs Grok 4's 4 on structured output in our testing. Where Grok 4 shines (concrete scenarios):
• Fast iteration on short-site copy or UI strings where core translation quality suffices — Grok 4 matches GPT-5.4 on multilingual and faithfulness (both 5/5 in our tests) but can be a simpler integration for mid-length contexts (256k window).
• Classification-driven routing before translation: Grok 4 scored 4 on classification versus GPT-5.4's 3 in our testing, so Grok 4 can be preferable when you need robust auto-routing or label-based pipelines prior to translation. Cost and parameter notes grounded in data: output cost per mtok is equal ($15) for both; input cost per mtok is $2.50 for GPT-5.4 vs $3.00 for Grok 4. Use those numbers when modeling large-batch localization budgets.

For Translation, choose GPT-5.4 if you need enterprise-grade localization: long-context documents, strict output formats, or safety-sensitive content (GPT-5.4 leads on safety 5 vs 2 and structured output 5 vs 4, and offers a 1,050,000-token window). Choose Grok 4 if you need an equally accurate translator for short-to-mid length content with stronger classification routing (Grok 4 classification 4 vs GPT-5.4's 3) and you prefer its parameter set for developer experimentation; note Grok 4 has a 256,000-token window and higher input cost ($3.00 vs $2.50 per mtok).