We tested how three neural networks — Kimi, DeepSeek, and GLM — handle translating and localizing a landing page for a given geo. This article covers the test results.
How the testing was conducted
For the test, we took a ready-made Spanish landing page from an affiliate network, which was used for a similar test of Claude.ai in this piece: https://cpa.rip/prompts/landing-page-adaptation/. We translated it and adapted it for the French audience.
We used: Kimi 2.6, DeepSeek V4 Flash, and GLM 5.2. We uploaded the landing page’s index.html file into each of them. The exception was GLM 5.2 — it couldn’t read the HTML file, so we had to upload the landing page content as a text file instead.
In the previous article, where Claude performed a similar task, we were able to upload the entire landing page archive at once.
Each model was given two prompts in sequence.
Translation of the landing page text.
Adapt the initial landing page to French and translate all user-visible text into French.
Adapting the content to the French geo.
Replace the city and university names in the text with similarly significant equivalents from France. Replace the names of famous people (e.g., TV hosts) with French personalities of comparable popularity and cultural relevance. Adapt the landing page more deeply to the French market/geo, ensuring the content feels native and locally appropriate.
Test results
Key takeaways
- Speed: DeepSeek V4 Flash finished the task fastest — it took about 13 minutes. GLM 5.2 completed the same work in roughly 35 minutes, while Kimi 2.6 needed about an hour and a half.
- Prompt comprehension: All three neural networks correctly understood both prompts and immediately started working on the task. The only difference was Kimi 2.6: during the process, it asked for confirmation before continuing.
- Text translation and adaptation: The accuracy of the translation and the quality of the localization were assessed using ChatGPT.
GLM delivers the highest-quality localization: it not only translates the text but also adapts the page more effectively for a French audience, although some phrasing sounds unnatural.
DeepSeek writes very good French, but leaves in Spanish brands and interface elements, which makes the localization feel incomplete.
Kimi comes across as a direct machine translation: literal translations of names, unnatural expressions, and weak cultural adaptation are all noticeable.
| Localization Aspect | GLM 5.2 | DeepSeek V4 Flash | Kimi 2.6 |
|---|---|---|---|
| French Media Brands | ✅ Replaced Spanish links with French ones (TF1, LCI, MYTF1) | ⚠️ Mixed French language with Spanish media brands (Antena 3, Atresplayer) | ❌ Inconsistent adaptation (e.g., "TF1 Noticias") |
| Cultural References | ✅ Adapted for a French audience (e.g., rugby, Loto, familiar institutions) | ⚠️ Limited adaptation: some Spanish references remain | ❌ Mostly literal translation with minimal localization |
| Institution Names | ✅ Mostly localized, though some references could be more specific | ⚠️ Partially localized, but sometimes sounds unnatural | ❌ Weak localization and inconsistent naming |
| Interface Labels / Navigation | ✅ Natural French interface terminology | ✅ Mostly correct French interface | ❌ Some literal translations and awkward interface labels (e.g., "Miroir Public") |
| Brand Consistency | ✅ Unified French identity throughout the page | ⚠️ Hybrid Spanish-French identity | ❌ Mixed terminology and inconsistent branding |
| Trust of French Readers | ✅ Feels like a landing page originally built for France | ⚠️ Clearly recognizable as a machine-translated page | ❌ Easily identifiable as a machine-translated localization |
| Overall Localization Quality | 9/10 | 7.5/10 | 5.5/10 |
Kimi
Kimi is a Chinese neural network and chat assistant from Moonshot AI that can understand questions in natural language, write and edit text, explain complex topics, analyze large documents, help with code, and search for information.
Report on the completed landing page localization:
Final result
Analysis from ChatGPT 5.5
Downsides
- The weakest adaptation for France: TF1 Noticias → more correct would be TF1 Info or Actualités TF1.
- Interface localization errors: Miroir Public — a literal translation of the Spanish program name (Espejo Público).
- Incorrect terminology: Basketball → should be Basket-ball or Basket.
- Unnatural phrasing: élimine jusqu’à 4 kg…, C’est ainsi : — not typical of French ad copy.
DeepSeek
DeepSeek is a family of Chinese neural networks from the company DeepSeek. The models are designed for conversation, coding, text analysis, and logical problem-solving.
Report on the completed translation and localization of the landing page:
Final result
Analysis from ChatGPT 5.5
Pros
- Very good, natural French.
- Successful phrasing: sans régime ni renoncer à ses plats favoris, sans effort supplémentaire, sans effets secondaires.
Downsides
- Incomplete adaptation for France: Antena 3 Noticias and Atresplayer create the impression of a Spanish site and reduce trust.
- Université de la Sorbonne à Paris sounds unnatural; Sorbonne Université would be preferable.
GLM
GLM (General Language Model) is a family of language models developed by the Chinese company Zhipu AI. GLM models are used for text generation, dialogue, translation, coding, and multimodal tasks.
Report on the completed translation and localization of the landing page:
Final result
Analysis from ChatGPT 5.5
Pros
- The most natural French.
- Uses familiar constructions: se débarrasser de 13 kg, sans suivre aucun régime, plats préférés.
- Successful media localization: TF1, LCI, MYTF1.
- Vocabulary close to French journalism.
Downsides
- There are some minor unnatural bits:
- il élimine jusqu’à 4 kg de graisse pure → better: permet d’éliminer / il est possible d’éliminer.
- Université de Paris sounds less natural; Sorbonne Université or the name of a specific university would be preferable.
