Best AI models for Spanish

Spanish text uses more tokens than English, and the gap depends on the model. We measured it ourselves, then combined it with independent quality scores and current prices to show which models give the best results for your money in Spanish.

The same text needs 17% to 45% more tokens in Spanish than in English, depending on the model. OpenAI (GPT-4o) is the most efficient, DeepSeek (V3.1) the least.

The cost of Spanish, by model family

TokenizeriTokens (English)Tokens (Spanish)Extra vs English
OpenAI (GPT-4o)219256
+17%
Google (Gemma 3)218256
+17%
Meta (Llama 4)219261
+19%
Mistral (Nemo)220276
+25%
Z.ai (GLM-4.5)219281
+28%
Alibaba (Qwen 3)219302
+38%
DeepSeek (V3.1)219318
+45%

Anthropic (Claude) and xAI (Grok) do not publish their tokenizers, so they cannot be measured; for their models we use the average of the measured families (marked ≈). Google’s Gemini is estimated with Google’s open Gemma tokenizer.

Most capable models and their real cost in Spanish

Rankings →
ModelOverall indexiSpanish factorInputi1,000 pages
166.6×1.17$10$3.96
165.0≈ ×1.27$10$4.31
163.6≈ ×1.27$10$4.31
Claude Opus 5Anthropic
162.7≈ ×1.27$5$2.15
162.5×1.17$30$11.88
162.0×1.17$2$0.792
159.3×1.17$2$0.792
GPT-5.5OpenAI
159.3×1.17$5$1.98
159.1×1.17$30$11.88
158.3≈ ×1.27$5$2.15
Gemini 3.7 FlashGoogle DeepMind
157.7×1.17$0.75$0.297
Kimi K3Moonshot AI
157.7≈ ×1.27$3$1.29
Gemini 3.8 FlashGoogle DeepMind
157.1×1.17$0.75$0.297
156.9×1.19$1.25$0.505
GPT-5.4OpenAI
156.9×1.17$2.50$0.99

Cost to read 1,000 pages of text (about 300,000 words in English) written in Spanish, as input. Each model gets the factor measured on its company’s latest public tokenizer, so treat it as an estimate: newer models may use a different tokenizer. ≈ marks companies that publish no tokenizer (average of measured families).

Best value for Spanish

Strong models (overall index 140 or more), cheapest first for processing Spanish text.

Models built for Spanish

Specialised models are not always available through the big APIs above, but they are worth testing, especially for local dialects and cultural knowledge.

  • ALIA · Salamandra ↗
    Barcelona Supercomputing Center
    Open models from Spain’s public ALIA initiative, trained with a large share of Spanish and of Catalan, Basque and Galician.
  • Latam-GPT ↗
    CENIA (Chile)
    Open model for Latin America, led from Chile with partners across the region and trained on regional data to better reflect local Spanish and culture.
  • RigoChat ↗
    IIC
    Open chat model from a Madrid research institute, tuned to follow instructions in Spanish.

Independent Spanish leaderboards

These projects test models directly in Spanish. Use them alongside the overall scores above.

  • La Leaderboard ↗
    SomosNLP · Hugging Face
    Open leaderboard of language models in Spanish and the other languages of Spain and Latin America.
  • ODESIA Leaderboard ↗
    UNED
    Compares models on equivalent tasks in Spanish and English to measure the gap between the two languages.
  • IberBench ↗
    IberBench
    Evaluates models on tasks in the languages of the Iberian Peninsula and Latin America, including regional varieties of Spanish.

Tips for using AI in Spanish

  1. Name the variety. Spanish from Spain, Mexico, Argentina or Colombia differs in vocabulary and grammar (vosotros or ustedes, "vos" in Argentina): tell the model who you are writing for.
  2. Set the register. Say whether the model should use "tú" or "usted"; many models default to "tú", which can sound too casual in formal or customer-facing content.
  3. Watch for anglicisms. Translated or generated Spanish can sound unnatural ("aplicar a un trabajo" instead of "solicitar un trabajo"): ask for natural, idiomatic Spanish and proofread.
  4. Budget for extra tokens. Depending on the model, the same content costs 17% to 45% more in Spanish than in English: use the table above or the token counter to estimate.
  5. Check accents and punctuation. Generated text sometimes drops accents or the opening ¿ and ¡ marks, especially when the instructions are in English: review anything you publish.

How we measured

We wrote the same four texts (a news item, a customer email, a technical explanation and travel tips) in 15 languages, then counted the tokens that each public tokenizer produces. Quality scores come from Epoch AI and are measured mostly in English, so always test the finalists on your own Spanish content.

Get a personal recommendationCount the tokens of your own text