English to Yoruba: AI Translation Comparison
English to Yoruba: AI Translation Comparison
Yoruba is spoken by over 45 million people, primarily in southwestern Nigeria, Benin, and Togo, with significant diaspora communities worldwide. It is a tonal language with three lexical tones (high, mid, low) that are essential for meaning — the same sequence of consonants and vowels can produce entirely different words depending on tone. Demand for English-to-Yoruba translation is driven by Nigerian government services, education, media, Nollywood film subtitling, and digital content localization for Nigeria’s massive internet population.
This comparison evaluates five leading AI translation systems on English-to-Yoruba 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 | 18.3 | 0.724 | 5.4 | General-purpose, broadest data |
| DeepL | 14.6 | 0.691 | 4.4 | Very limited Yoruba support |
| GPT-4 | 20.9 | 0.742 | 5.9 | Contextual accuracy, tonal awareness |
| Claude | 18.8 | 0.728 | 5.5 | Long-form content |
| NLLB-200 | 22.1 | 0.753 | 6.2 | Best Yoruba coverage, 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 |
|---|---|
| A ni idunnu lati fi to yin leti pe ohun elo yin ti je itewogba. Ejowo, e wa awon iwe afikun ti a so po. | |
| DeepL | A ni idunnu lati so fun yin pe ohun elo yin ti gba ifọwọsi. Jọwọ wa awọn iwe ti o yẹ ti a so mọ. |
| GPT-4 | A ni inudidun lati fi to yin leti pe iwe ibere yin ti gba ifọwọsi. Ẹ jọwọ wo awọn iwe afikun ti a so pọ mọ lẹta yii. |
| Claude | A ni inudidun lati fi to yin leti pe ohun elo yin ti je itẹwọgba. Ẹ jọwọ, ẹ wo awọn iwe to yẹ ti a fi kun. |
| NLLB-200 | A ni inudidun lati fi to yin leti pe iwe ibere yin ti gba ifọwọsi. Ẹ jọwọ wo awọn iwe afikun ti a so pọ mọ. |
Assessment: NLLB-200 and GPT-4 produce the most natural formal Yoruba, with correct use of diacritics (subdots and tone marks). Google’s output omits most diacritics, which significantly impairs readability and can change word meanings. The presence of proper Yoruba diacritics (ẹ, ọ, ṣ) is a basic quality indicator for this language.
Casual Conversation
Source: “Hey, I was thinking we could grab some food later. What do you feel like eating?”
| System | Translation |
|---|---|
| Hey, mo n ro pe a le lo mu ounje nigbamii. Kini o fe je? | |
| DeepL | Hey, mo n ro pe a le gba ounje nigbamii. Kini o fe je? |
| GPT-4 | Ẹ n lẹ, mo n ro pe a le lọ jẹun nigbamii. Kini o fẹ jẹ? |
| Claude | Bawo, mo n ro pe a le lọ mu ounjẹ nigbamii. Kini o fẹ jẹ? |
| NLLB-200 | Mo n ro pe a le lọ mu ounjẹ nigbamii. Kini o fẹ jẹ? |
Assessment: GPT-4 and Claude attempt Yoruba greetings instead of transliterating “Hey.” Claude’s “Bawo” is a natural casual greeting. Google and DeepL keep “Hey” untranslated, which is actually common in modern Nigerian Yoruba, reflecting English influence. All systems struggle to fully capture casual Nigerian Yoruba, which is heavily influenced by code-switching with English.
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 gba awon ibere POST pelu JSON body ti o ni orisun ọrọ ati koodu ede ibi-afẹde. | |
| DeepL | Aaye ipari API gba awọn ibeere POST pẹlu ara JSON ti o ni ọrọ orisun ati koodu ede ibi-afẹde. |
| GPT-4 | API endpoint n gba awọn POST requests pẹlu JSON body ti o ni source text ati target language code ninu. |
| Claude | API endpoint n gba awọn ibeere POST pẹlu JSON body ti o ni ọrọ orisun ati koodu ede ti a n wa. |
| NLLB-200 | Aaye ipari API n gba awọn ibeere POST pẹlu ara JSON ti o ni ọrọ orisun ati koodu ede afojusun ninu. |
Assessment: GPT-4 keeps English technical terms intact, reflecting how Nigerian Yoruba developers actually communicate. DeepL and NLLB-200 translate “endpoint” as “aaye ipari” (end point), which has no established usage in Yoruba tech communities. Best Translation AI for Technical Documentation
Strengths and Weaknesses
Google Translate
Strengths: Accessible and free. Benefits from Nigerian web content. Weaknesses: Frequently omits Yoruba diacritics (subdots and tone marks), which can change word meanings entirely. Quality below the other systems for this pair.
DeepL
Strengths: Basic grammatical structure is usually correct. Weaknesses: Very limited Yoruba support. Lowest quality overall. Poor diacritical mark handling. Unnatural word choices.
GPT-4
Strengths: Best contextual understanding. Better diacritical mark usage than Google. Can handle English-Yoruba code-switching naturally. Best tonal awareness in text output. Weaknesses: Expensive. Not always consistent with tone marks across longer texts.
Claude
Strengths: Consistent output for long documents. Reasonable diacritical mark handling. Weaknesses: Less natural than GPT-4 for idiomatic Yoruba. Limited casual register capability.
NLLB-200
Strengths: Best free option for Yoruba by a significant margin. Meta’s NLLB project made Yoruba a priority language. Consistent diacritical mark usage. Self-hostable. Weaknesses: No register control. Over-translates English terms in technical content. Cannot handle code-switching.
Recommendations
| Use Case | Recommended System |
|---|---|
| Quick personal translation | Google Translate (free, but verify diacritics) |
| Government / official documents | GPT-4 with human review |
| Educational material | NLLB-200 |
| Media / Nollywood subtitles | GPT-4 |
| Technical documentation | GPT-4 |
| High-volume, cost-sensitive | NLLB-200 (self-hosted) |
| Long-form content | Claude |
Best Translation AI in 2026: Complete Model Comparison
Key Takeaways
- NLLB-200 leads as the best free option, outperforming Google Translate on Yoruba thanks to Meta’s targeted investment in African languages. GPT-4 offers the highest contextual quality at a cost.
- Diacritical marks are not optional in Yoruba — they distinguish different words. Systems that omit subdots and tone marks produce ambiguous or incorrect text.
- Code-switching between English and Yoruba is extremely common in Nigerian communication. GPT-4 handles this best; NMT systems struggle with mixed-language input.
- All systems produce lower quality for Yoruba than for high-resource languages. Human review is essential for published content.
Next Steps
- Try it yourself: Compare these systems on your own text in the Translation AI Playground: Compare Models Side-by-Side.
- Low-resource languages: Learn more in Low-Resource Languages: Where NLLB and Aya Shine.
- Check the leaderboard: Browse our full Translation Accuracy Leaderboard by Language Pair.
- Full model comparison: Read Best Translation AI in 2026: Complete Model Comparison.