English to Sinhala: AI Translation Comparison
English to Sinhala: AI Translation Comparison
Sinhala (Sinhalese) is spoken by approximately 17 million people, primarily in Sri Lanka where it serves as one of two official languages alongside Tamil. It is an Indo-Aryan language with its own unique script and a literary tradition stretching back over two millennia. Sinhala has been significantly influenced by Pali, Sanskrit, Tamil, Portuguese, Dutch, and English through centuries of cultural and colonial contact. Demand for English-to-Sinhala translation is driven by Sri Lankan government services, education, media, tourism, and the growing tech sector.
This comparison evaluates five leading AI translation systems on English-to-Sinhala accuracy, naturalness, and suitability for different use cases.
Translation comparisons are based on automated metrics and editorial evaluation. Quality varies by language pair and content type.
Accuracy Comparison Table
| System | BLEU Score | COMET Score | Editorial Rating (1-10) | Best For |
|---|---|---|---|---|
| Google Translate | 22.1 | 0.759 | 6.0 | General-purpose, broadest data |
| DeepL | 17.8 | 0.723 | 5.1 | Limited Sinhala support |
| GPT-4 | 24.6 | 0.776 | 6.5 | Contextual accuracy, register control |
| Claude | 22.5 | 0.762 | 6.1 | Long-form content |
| NLLB-200 | 23.8 | 0.771 | 6.3 | Cost-effective, self-hosted |
Translation Quality Metrics: BLEU, COMET, and Human Evaluation Explained
Example Translations
Formal Business Email
Source: “We are pleased to inform you that your application has been approved. Please find the relevant documentation attached.”
| System | Translation |
|---|---|
| ඔබේ අයදුම්පත අනුමත කර ඇති බව දැනුම් දීමට අපි සතුටු වෙමු. කරුණාකර අදාළ ලේඛන අමුණා ඇත. | |
| DeepL | ඔබේ අයදුම්පත අනුමත කර ඇති බව දැනුම් දීමට සතුටු වෙමු. කරුණාකර අදාළ ලේඛන සොයාගන්න. |
| GPT-4 | ඔබගේ අයදුම්පත අනුමත වී ඇති බව සතුටින් දන්වා සිටිමු. කරුණාකර මෙයට අමුණා ඇති අදාළ ලිපි ලේඛන බලන්න. |
| Claude | ඔබේ අයදුම්පත අනුමත කර ඇති බව දැනුම් දීමට අපට සතුටුයි. කරුණාකර අමුණා ඇති අදාළ ලේඛන බලන්න. |
| NLLB-200 | ඔබගේ අයදුම්පත අනුමත වී ඇති බව දැනුම් දීමට අපි සතුටු වෙමු. කරුණාකර අමුණා ඇති අදාළ ලේඛන බලන්න. |
Assessment: GPT-4 and NLLB-200 produce the most natural formal Sinhala. GPT-4’s “දන්වා සිටිමු” is the standard formal notification phrasing in official Sinhala. DeepL’s “සොයාගන්න” (find/search for) is overly literal for “please find attached.”
Casual Conversation
Source: “Hey, I was thinking we could grab some food later. What do you feel like eating?”
| System | Translation |
|---|---|
| හෙයි, මම හිතුවා අපි පසුව කෑම ගන්න පුළුවන් කියලා. ඔයාට මොනවද කන්න ඕනෙ? | |
| DeepL | හෙයි, මම හිතුවා අපි පසුව කෑම ගන්න පුළුවන් කියලා. ඔබට මොනවද කන්න කැමතිද? |
| GPT-4 | මචං, මම හිතුවා පස්සේ කොහේ හරි ගිහින් කෑම ගමු කියලා. මොකක්ද කන්න හිතෙන්නේ? |
| Claude | හේයි, මම හිතුවා පස්සේ කෑමක් ගමු කියලා. ඔයාට මොනවද කන්න ඕනෙ? |
| NLLB-200 | මම හිතුවා අපි පසුව යම් කෑමක් ගන්න පුළුවන් කියලා. ඔබට කුමක්ද කන්න අවශ්ය? |
Assessment: GPT-4 uses “මචං” (a natural Sinhala casual address term, similar to “dude”) and casual verb forms like “කෑම ගමු” (let’s eat). DeepL uses the formal “ඔබට” instead of casual “ඔයාට.” NLLB-200 uses formal “ඔබට” and “අවශ්ය” (need, formal), completely missing the casual register. Spoken Sinhala diverges significantly from written Sinhala.
Technical Content
Source: “The API endpoint accepts POST requests with a JSON body containing the source text and target language code.”
| System | Translation |
|---|---|
| API endpoint එක source text සහ target language code අඩංගු JSON body එකක් සහිත POST requests භාරගනී. | |
| DeepL | API අන්ත ලක්ෂ්යය මූලාශ්ර පෙළ සහ ඉලක්ක භාෂා කේතය අඩංගු JSON ශරීරයක් සහිත POST ඉල්ලීම් භාරගනී. |
| GPT-4 | API endpoint එක source text සහ target language code අඩංගු JSON body එකක් සමග POST requests accept කරයි. |
| Claude | API endpoint එක source text සහ target language code ඇතුළත් JSON body එකක් සමග POST requests භාරගනී. |
| NLLB-200 | API අන්ත ලක්ෂ්යය මූල පෙළ සහ ඉලක්ක භාෂා කේතය අඩංගු JSON ශරීරයක් සමඟ POST ඉල්ලීම් පිළිගනී. |
Assessment: Google, GPT-4, and Claude keep English technical terms and add Sinhala grammatical suffixes (“endpoint එක,” “requests”), which is standard in Sri Lankan tech writing. DeepL and NLLB-200 translate “endpoint” as “අන්ත ලක්ෂ්යය” and “body” as “ශරීරය” (physical body), which are unnatural in technical contexts. Best Translation AI for Technical Documentation
Strengths and Weaknesses
Google Translate
Strengths: Most accessible free option. Benefits from Sri Lankan government and news web data. Handles script rendering well. Weaknesses: Register control is inconsistent. Sometimes produces overly formal output for casual content.
DeepL
Strengths: Basic grammatical correctness for simple sentences. Weaknesses: Limited Sinhala support. Over-translates English terms. Defaults to formal register. Vocabulary range is narrow.
GPT-4
Strengths: Best register control between formal and casual Sinhala. Handles code-switching naturally. Best understanding of written vs. spoken Sinhala differences. Weaknesses: Expensive. Script rendering can have minor inconsistencies.
Claude
Strengths: Consistent output for long documents. Good formal register. Reasonable code-switching in technical content. Weaknesses: Less natural casual Sinhala. Limited awareness of regional variation.
NLLB-200
Strengths: Strong free option. Sinhala was included in NLLB training. Competitive with Google Translate. Self-hostable. Weaknesses: Formal register only. Over-translates technical terms. No spoken-register capability.
Recommendations
| Use Case | Recommended System |
|---|---|
| Quick personal translation | Google Translate (free) |
| Government / official documents | GPT-4 with human review |
| Tourism / hospitality | GPT-4 |
| Educational material | NLLB-200 or Google Translate |
| Technical documentation | GPT-4 or Claude |
| High-volume, cost-sensitive | NLLB-200 (self-hosted) |
| Long-form content | Claude |
Best Translation AI in 2026: Complete Model Comparison
Key Takeaways
- GPT-4 leads for English-to-Sinhala on contextual quality and register control. NLLB-200 is the strongest free alternative, slightly outperforming Google Translate.
- The written-spoken gap in Sinhala is large. Formal written Sinhala and everyday spoken Sinhala differ substantially in vocabulary, verb forms, and sentence structure. Most AI systems default to written Sinhala.
- Code-switching between English and Sinhala is very common in Sri Lankan communication, especially in technical and business contexts. GPT-4 handles this most naturally.
- Human review is recommended for published content, particularly for government and educational materials where accuracy standards are high.
Next Steps
- Try it yourself: Compare these systems on your own text in the Translation AI Playground: Compare Models Side-by-Side.
- Check the leaderboard: Browse our full Translation Accuracy Leaderboard by Language Pair.
- Full model comparison: Read Best Translation AI in 2026: Complete Model Comparison.