Catalan to Spanish: AI Translation Comparison
Catalan to Spanish: AI Translation Comparison
Catalan and Spanish are closely related Romance languages with approximately 10 million and 580 million speakers respectively. Catalan is co-official in Catalonia, Valencia, and the Balearic Islands alongside Spanish, and is the sole official language of Andorra. The languages share extensive mutual intelligibility, estimated at 80 to 90 percent for written text, due to their common Latin origins, similar grammatical structures, and overlapping vocabulary. However, they differ in important ways: Catalan has a richer vowel system with eight vowels versus five in Spanish, different article forms, distinct preposition usage, and vocabulary items borrowed from French and Occitan rather than Arabic. This pair is critical for regional governance, media, publishing, education, and the cultural identity politics of Catalonia. AI translation must produce distinctly Spanish output without Catalan interference artifacts.
This comparison evaluates five leading AI translation systems on Catalan-to-Spanish 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 | 43.2 | 0.895 | 8.5 | General-purpose, speed |
| DeepL | 45.6 | 0.907 | 9.0 | Natural fluency, formal text |
| GPT-4 | 44.8 | 0.902 | 8.7 | Register adaptation, context |
| Claude | 43.5 | 0.897 | 8.5 | Long-form, editorial consistency |
| NLLB-200 | 40.1 | 0.878 | 7.9 | Self-hosted, cost-effective |
Translation Quality Metrics: BLEU, COMET, and Human Evaluation Explained
Example Translations
Formal Business Email
Source: “Benvolgut Sr. Puig, ens complau informar-lo que la seva sol·licitud ha estat aprovada. Si us plau, reviseu la documentacio adjunta.”
| System | Translation |
|---|---|
| Estimado Sr. Puig, nos complace informarle de que su solicitud ha sido aprobada. Por favor, revise la documentacion adjunta. | |
| DeepL | Estimado Sr. Puig, tenemos el placer de informarle de que su solicitud ha sido aprobada. Le rogamos que revise la documentacion adjunta. |
| GPT-4 | Estimado Sr. Puig, es grato para nosotros comunicarle que su solicitud ha sido aprobada. Le rogamos tenga a bien revisar la documentacion que se adjunta. |
| Claude | Estimado Sr. Puig, nos complace informarle de que su solicitud ha sido aprobada. Por favor, revise la documentacion adjunta. |
| NLLB-200 | Sr. Puig, su solicitud ha sido aprobada. La documentacion esta adjunta. |
Assessment: DeepL produces the most polished Spanish business prose with tenemos el placer and Le rogamos. GPT-4’s es grato para nosotros and tenga a bien are authentically formal Spanish constructions. NLLB-200 drops all courtesy markers, producing a blunt communication.
Casual Conversation
Source: “Ei, has anat al nou restaurant? Es genial! Hi has d’anar segur.”
| System | Translation |
|---|---|
| Oye, has ido al nuevo restaurante? Es genial! Tienes que ir seguro. | |
| DeepL | Oye, has ido al restaurante nuevo? Es genial! Tienes que ir sin falta. |
| GPT-4 | Eh, fuiste al restau nuevo? Mola un monton! Tienes que ir, en serio. |
| Claude | Oye, has ido al nuevo restaurante? Es genial! Tienes que ir seguro. |
| NLLB-200 | Has ido al nuevo restaurante? Es bueno. Debes ir. |
Assessment: GPT-4 captures casual Spanish best with restau (informal shortening), Mola un monton (Spain Spanish slang for super cool), and en serio (for real). DeepL’s sin falta adds natural emphasis. NLLB-200 produces flat output with Es bueno, losing the enthusiasm of Catalan genial.
Technical Content
Source: “El model d’aprenentatge profund utilitza una arquitectura de transformer amb mecanismes d’atencio per processar dades seqüencials.”
| System | Translation |
|---|---|
| El modelo de aprendizaje profundo utiliza una arquitectura de transformer con mecanismos de atencion para procesar datos secuenciales. | |
| DeepL | El modelo de aprendizaje profundo emplea una arquitectura transformer con mecanismos de atencion para el procesamiento de datos secuenciales. |
| GPT-4 | El modelo de deep learning utiliza una arquitectura transformer con mecanismos de attention para procesar datos secuenciales. |
| Claude | El modelo de aprendizaje profundo utiliza una arquitectura de transformer con mecanismos de atencion para procesar datos secuenciales. |
| NLLB-200 | El modelo de aprendizaje profundo utiliza una arquitectura de transformador con mecanismos de atencion para procesar datos secuenciales. |
Assessment: The Catalan-to-Spanish technical conversion is nearly transparent due to the extreme vocabulary similarity. GPT-4 retains deep learning and attention as English terms, common in Spanish tech. NLLB-200 uses transformador (literal Spanish), less standard. See Best AI for Technical Translation for domain comparisons.
Strengths and Weaknesses
Google Translate
Strengths: Fast and free. Benefits from the extensive Catalan-Spanish bilingual corpora in Spain. Weaknesses: Occasionally produces Catalan-influenced constructions. Less polished than DeepL on formal register.
DeepL
Strengths: Most natural Spanish output from Catalan. Best handling of the subtle vocabulary and preposition differences. Weaknesses: May default to Peninsular Spanish; Latin American users should verify vocabulary choices.
GPT-4
Strengths: Best register adaptation. Can target different Spanish regional variants. Handles cultural context well. Weaknesses: Higher cost. Smaller advantage on this extremely close language pair.
Claude
Strengths: Consistent long-form quality. Good for publishing and editorial content. Weaknesses: Less distinctive than DeepL for formal content on this high-similarity pair.
NLLB-200
Strengths: Free and self-hostable. Benefits enormously from Romance language similarity. Weaknesses: Lowest quality. Occasional Catalan vocabulary bleeding. Less polished formal output.
Recommendations
| Use Case | Recommended System |
|---|---|
| Personal use | Google Translate |
| Official documents | DeepL |
| Media and publishing | DeepL |
| Marketing content | GPT-4 |
| Long-form editorial | Claude |
| High-volume processing | NLLB-200 (self-hosted) |
Best Translation AI in 2026: Complete Model Comparison
Key Takeaways
- DeepL leads for Catalan-to-Spanish with the most natural output, leveraging its strong Romance language support.
- The extreme similarity between Catalan and Spanish means all systems achieve high scores, but Catalan vocabulary contamination in Spanish output is the primary risk.
- Preposition differences (Catalan per vs. Spanish para/por) and article forms are the most common error sources.
- Cultural and political sensitivity around Catalan-Spanish language choice adds a non-linguistic dimension that AI systems do not address.
Next Steps
- Try it yourself: Compare these systems on your own text in the Translation AI Playground: Compare Models Side-by-Side.
- Reverse direction: See Galician to Portuguese: AI Translation Comparison.
- Check the leaderboard: Browse our full Translation Accuracy Leaderboard by Language Pair.
- Full model comparison: Read Best Translation AI in 2026: Complete Model Comparison.