DeepL vs ChatGPT Translation: Why Quality Matters for Help Centers

Your help center is often the first place customers go when they need answers. When you translate that content into other languages, quality isn't optional. Bad translations create support tickets. Good ones prevent them.

So which translation engine should power your multilingual help center: DeepL or ChatGPT?

We compared both on real support documentation. Here's what we found.

Side-by-Side: Translation Examples

The following examples illustrate the types of differences we commonly see between DeepL and ChatGPT translations. They represent typical patterns rather than specific test results.

Example 1: Technical Instructions

Original English:

"Navigate to Settings > Integrations > API Keys. Click 'Generate New Key' and copy the token before closing the modal. The key won't be shown again."

DeepL (German):

"Navigieren Sie zu Einstellungen > Integrationen > API-Schlüssel. Klicken Sie auf 'Neuen Schlüssel generieren' und kopieren Sie das Token, bevor Sie das Dialogfenster schließen. Der Schlüssel wird nicht erneut angezeigt."

ChatGPT-4 (German):

"Gehen Sie zu Einstellungen > Integrationen > API-Schlüssel. Klicken Sie auf 'Neuen Schlüssel erstellen' und kopieren Sie den Token, bevor Sie das Popup schließen. Der Schlüssel wird danach nicht mehr angezeigt."

What's different? ChatGPT uses "erstellen" (create) instead of "generieren" (generate). Subtle, but your UI says "Generate." Now customers are looking for a button that doesn't exist. ChatGPT also translates "modal" as "Popup" instead of the more accurate "Dialogfenster."

Example 2: Troubleshooting Steps

Original English:

"If syncing fails, check that your account has write permissions. Go to your workspace settings and verify the connection status shows 'Active'."

DeepL (Spanish):

"Si la sincronización falla, compruebe que su cuenta tenga permisos de escritura. Vaya a la configuración de su espacio de trabajo y verifique que el estado de la conexión muestre 'Activo'."

ChatGPT-4 (Spanish):

"Si la sincronización no funciona, asegúrese de que su cuenta tiene permisos para escribir. Vaya a los ajustes de su espacio de trabajo y confirme que el estado de conexión dice 'Activo'."

What's different? ChatGPT's version is longer and less precise. "No funciona" (doesn't work) is vaguer than "falla" (fails). "Permisos para escribir" is awkward compared to "permisos de escritura." And "confirme que dice" (confirm it says) adds unnecessary words.

Example 3: Brand Voice Preservation

Original English (casual tone):

"Stuck? No worries. Here are the most common fixes that get things working again."

DeepL (French):

"Bloqué ? Pas de souci. Voici les solutions les plus courantes pour rétablir le fonctionnement."

ChatGPT-4 (French):

"Vous êtes bloqué ? Ne vous inquiétez pas. Voici les correctifs les plus courants qui permettent de faire fonctionner les choses à nouveau."

What's different? ChatGPT's version is more formal ("Vous êtes bloqué" vs the casual "Bloqué"). It also gets wordy: "permettent de faire fonctionner les choses à nouveau" is a clunky literal translation. DeepL's "rétablir le fonctionnement" is cleaner and matches the original's brevity.

What Native Speakers Typically Report

In our conversations with support teams, we hear similar feedback patterns when they test both engines with native speakers:

Common DeepL feedback:

• "Reads like a human wrote it"
• "Technical terms stay accurate"
• "Tone matches our brand"

Common ChatGPT feedback:

• "Sometimes too formal, sometimes too casual"
• "Had to fix UI terminology"
• "Some sentences needed restructuring"

From what we've seen working with companies translating large help centers, native speakers often flag ChatGPT translations as "technically correct but awkward," while DeepL output typically needs less review.

The Numbers Behind the Gap

DeepL's own blind testing with language experts found:

• Language experts prefer DeepL 1.7x more often than ChatGPT-4 for translation quality
• ChatGPT-4 translations require 3x more edits to reach the same quality
• DeepL's specialized model shows reduced hallucination risk in translated content

These aren't small differences. If your team reviews 100 articles, that's significantly more editing time with ChatGPT. At $50/hour for a bilingual reviewer, the "free" API can cost thousands in labor.

Why Help Center Translation Is Different

Help center articles aren't blog posts. They're technical documentation with specific requirements.

Accuracy Is Non-Negotiable

When a customer follows your troubleshooting guide in Spanish or German, every step needs to be correct. A mistranslated instruction creates a support ticket. Multiply that by thousands of customers.

ChatGPT's general-purpose design means it sometimes "interprets" rather than translates. It adds context, changes phrasing, and occasionally alters meaning. That flexibility is great for creative writing. It's a problem for technical instructions.

DeepL was built specifically for translation. It maintains the original meaning with higher fidelity.

UI Terminology Must Match

Your help center references your product's interface. Button names, menu items, and feature names need to translate consistently.

Here's a real problem: Your English help center says "Click 'Generate Report'." ChatGPT translates this to Spanish as "Haga clic en 'Crear Informe'" (Create Report). But your Spanish UI actually says "Generar Informe." Now your customer is confused.

DeepL's glossary feature lets you define exactly how terms should translate. ChatGPT doesn't offer this level of control without complex prompting that still doesn't guarantee consistency.

Formatting Breaks Things

Help center content includes:

• Code snippets and commands
• URLs and placeholders
• Bullet lists and numbered steps
• Bold text and warnings

ChatGPT sometimes modifies formatting. It might convert a numbered list to bullets, add extra line breaks, or "helpfully" explain a code snippet instead of preserving it.

DeepL preserves formatting faithfully. What you send is what you get back.

The Hidden Cost of Poor Translation

When translation quality drops, costs appear in unexpected places.

More support tickets: Customers who can't understand your documentation contact support instead. Poor translations often send users straight to live chat.

Longer resolution times: Agents spend time clarifying what the help article meant to say.

Brand damage: Poorly translated content signals you don't care about international customers.

Review overhead: Your team spends hours fixing machine translation output instead of creating new content.

What Competitors Use (And Why It Matters)

Some help center translation tools use ChatGPT. It's cheaper to implement and the API is flexible. But those savings get passed to you as editing time.

When evaluating translation tools, ask: "What engine powers your translations?"

If the answer is ChatGPT or a general-purpose LLM, expect to review every translation manually. If the answer is DeepL or a specialized translation model, you'll spend less time editing.

When ChatGPT Works

ChatGPT isn't a bad tool. It's just not built for production translation at scale.

ChatGPT can work for:

• One-off translations where you review every word
• Creative rewriting where interpretation is acceptable
• Generating draft content in multiple languages

ChatGPT struggles with:

• Large volumes of help articles requiring consistency
• Technical documentation where accuracy is critical
• Maintaining terminology across hundreds of articles
• Production workflows without human review

What TranslateDesk Uses (And Why)

TranslateDesk uses DeepL's translation engine. We made this choice specifically because help center translation demands:

1. High accuracy for technical instructions
2. Consistent terminology across your entire knowledge base
3. Natural-sounding output that reflects your brand
4. Reliability at scale without manual review of every article

When you translate your Intercom help center with TranslateDesk, you get DeepL's quality applied systematically across your entire knowledge base.

Making the Right Choice

If you're evaluating translation options for your help center:

Choose DeepL-powered tools like TranslateDesk when:

• You need to translate your full help center
• Accuracy and consistency are requirements
• You want to minimize review time
• Your documentation includes technical content

Consider ChatGPT when:

• You're doing one-off translations with full review
• You want creative reinterpretation, not translation
• You're willing to invest in heavy post-editing

For most support teams, the math is simple: better translation quality means fewer customer issues, less review time, and a more professional international presence.

---

FAQ

Is DeepL more accurate than ChatGPT for translation?

DeepL's blind testing shows language experts prefer DeepL translations 1.7x more often than ChatGPT-4. DeepL was built specifically for translation, while ChatGPT is a general-purpose language model. For technical content like help centers, this specialization matters.

Is DeepL more expensive than ChatGPT for translation?

The API costs are comparable. The real cost difference comes from editing time. DeepL's testing found ChatGPT translations require 3x more edits to reach the same quality. For a 500-article help center, that editing overhead can cost thousands of dollars.

Can I use ChatGPT to translate my entire help center?

You can, but plan for significant review time. ChatGPT lacks consistency controls for terminology, and translations can vary in tone and accuracy. For large knowledge bases, the editing overhead often eliminates any cost savings from using a "free" model.

Which languages does DeepL support?

DeepL supports 30+ languages including all major European languages, Japanese, Chinese, Korean, Arabic, and more. For help center translation, this covers the vast majority of global customer bases.

Does TranslateDesk use DeepL or ChatGPT?

TranslateDesk uses DeepL's translation engine. We chose DeepL specifically for its superior accuracy on technical documentation and consistent terminology handling. Both are critical for help center content where customers need precise instructions.

How do I check what translation engine a tool uses?

Ask directly. Any reputable translation tool should disclose their translation engine. If they're vague about it or say "AI-powered" without specifics, they're likely using a general-purpose LLM that may produce inconsistent results.

Does TranslateDesk support glossaries?

Yes. You can define custom terminology to ensure consistent translation of product names, features, and technical terms across all your articles. This prevents the UI mismatch problem where translated instructions don't match your localized interface.