A 774-sample benchmark of English-to-Chinese localization makes a simple but costly point for marketers: paying professional human localizers is not a universal quality shortcut. The study shows humans still lead where precision and terminology matter, but on marketing copy, product UI and social content several machine and hybrid workflows outperform or tie human work. For sourcing and testing, the takeaway is concrete: test models and post-editing workflows on your content, not abstract vendor labels.

What the study measured

EC Innovations and Jademond Digital evaluated 774 localized outputs across six content types (informational, SEO, technical, product UI, user-generated content, and marketing), seven workflow models (human, raw LLMs and machine translation, and post-edited versions of those), and three task types (translation, transcreation, creation). Native Chinese localizers scored each output blind on three equally weighted dimensions: accuracy/consistency, fluency/language quality, and style/cultural adaptation.

Headline findings that matter to buyers

Professional human localizers finished first on informational content (score 76.9) and SEO (74.1). But they placed outside the top five for technical, product UI, UGC and—notably—marketing, where human work scored 53.7 and ranked 10th of 15 workflows. The top marketing workflow was post-edited Qwen (PE-Qwen) at 75.9, a 22.2-point gap.

Those topline results illustrate two truths. First, human strength concentrates where factual accuracy and terminological consistency dominate—exact matches matter more than register. Second, for copy that depends on contemporary voice or short strings (marketing, UI, social), machine or hybrid workflows can produce stronger-sounding output than traditionally trained professional translators.

Model-level differences outweigh category averages

Aggregating models into “Chinese LLMs” or “Western LLMs” hides meaningful variance. For SEO content the category average for raw Chinese LLMs was 60.7, but individual model scores ranged from Qwen (65.7) to Kimi (56.5)—a 9.2-point spread. On technical and marketing content spreads were even larger (over 20 points). Put simply: the specific model you choose can be the difference between near-parity with professionals and clearly inferior output.

That explains another result: on SEO the human lead over the best AI workflow was only 2.8 points (human 74.1 vs post-edited Doubao/Qwen 71.3). A gap that small is procurement-level ambiguity—replicate tests on your own high-value pages before making big sourcing decisions.

Post-editing: a conditional multiplier, not a switch

Post-editing improved many outputs, especially messy user-generated content—raw UGC averaged 33.3 and rose to 63.9 after post-editing. A mature MT pipeline plus a termbase and post-editing can outperform raw LLM drafts: post-edited Google MT scored 66.7 on SEO, ahead of several raw LLMs.

But post-editing can also reduce quality when the draft requires a rewrite rather than a polish. The study records examples where human post-editing produced negative or negligible changes for certain Western LLM marketing drafts. Conclusion: apply post-editing where the draft is a close, editable approximation of the target, not where the editor must recraft tone and messaging from scratch.

Practical checklist for marketers and localization buyers

  • Run model-level bake-offs on representative content. Test the exact models and post-editing setups against your high-value pages—SEO landing pages, product UI strings, and top marketing creative.
  • Reserve human spend for precision work. Prioritize professional localization for informational and SEO assets where factual accuracy and consistent terminology are non-negotiable.
  • Apply post-editing selectively. Use it to lift MT or strong LLM drafts and for UGC rescue; avoid standardizing post-editing for creative marketing without evidence it improves outcomes.
  • Tie linguistic scores to business metrics. The benchmark measured localization quality only, not rankings, traffic or conversions. Use A/B tests or controlled rollouts to validate that a given workflow actually moves your KPIs.
  • Retest regularly. Model capabilities change quickly; vendor- or model-level performance can shift enough to alter a previous sourcing decision.

What to do next

Start with a focused experiment: pick a small set of pages that drive traffic or conversion, run parallel localizations across the human, raw-model and post-edited workflows you’re considering, and measure both linguistic quality and a business metric (CTR, conversion rate, engagement). Use those paired results to set a hybrid sourcing policy that allocates human budget where it demonstrably improves outcomes and routes everything else through the best-performing model-plus-post-edit pipeline.

The EC Innovations–Jademond Digital benchmark reframes a familiar procurement debate: the question isn’t “humans or models?” but “which model, and when should a human intervene?” That narrower question is harder to answer without testing—but it’s the one that saves budget and preserves quality.