NLLB English to Sinhala: AI Translation Comparison
English to Sinhala: AI Translation Comparison
How We Evaluated: Our editorial team researched English to Sinhala translation quality using BLEU and COMET automated metrics, editorial side-by-side evaluation, and native-speaker fluency ratings. Rankings reflect translation accuracy, naturalness, handling of idioms, and suitability for formal vs. casual contexts. Last updated: March 2026. See our editorial policy for full methodology.
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
- Deciding between Google Translate and premium options? Read Google Translate vs. DeepL vs. AI for a cost-quality breakdown.
- The Translation Accuracy Leaderboard tracks how each system performs on Sinhala output quality over time.
- Compare English-to-Sinhala results on your own documents in the Translation Playground to find the best fit for your workflow.
- If you handle large volumes of English content, our guide on high-volume AI translation workflows covers batch processing strategies.