Benchmark multilingüe de modelos de IA Compara LLMs líderes por idioma

Explora cómo rinden los principales modelos de lenguaje grandes (LLMs) en varios idiomas en el Multilingual Index de Artificial Analysis, incluido el benchmark Global-MMLU-Lite. Filtra por idioma y modelo, consulta los trade-offs entre precisión, velocidad y coste, y encuentra el mejor LLM para tu caso de uso multilingüe.

Para detalles sobre datasets y metodología, consulta la página de FAQ.

Resumen

Artificial Analysis Multilingual Index

Higher is better
Reasoning models are indicated by a lightbulb icon

An index assessing multilingual performance in general reasoning across multiple languages. Results are computed across English, Chinese, Hindi, Spanish, French, Arabic, Bangla, Portuguese, Indonesian, Japanese, Swahili, German, Korean, Italian, Yoruba, Burmese. See Multilingual Intelligence Index methodology for further details.

Multilingual Index por idioma (normalizado)

Las puntuaciones están normalizadas por idioma entre todos los modelos evaluados: el verde representa la puntuación más alta para ese idioma y el rojo la más baja.
Reasoning models are indicated by a lightbulb icon

An index assessing multilingual performance in general reasoning across multiple languages. Results are computed across English, Chinese, Hindi, Spanish, French, Arabic, Bangla, Portuguese, Indonesian, Japanese, Swahili, German, Korean, Italian, Yoruba, Burmese. See Multilingual Intelligence Index methodology for further details.

Multilingual Index

Multilingual Index: media de todos los idiomas

Artificial Analysis Multilingual Index · Average across all languages · Higher is better
Reasoning models are indicated by a lightbulb icon

An index assessing multilingual performance in general reasoning across multiple languages. Results are computed across English, Chinese, Hindi, Spanish, French, Arabic, Bangla, Portuguese, Indonesian, Japanese, Swahili, German, Korean, Italian, Yoruba, Burmese. See Multilingual Intelligence Index methodology for further details.

Multilingual Index: media vs. velocidad de salida

Artificial Analysis Multilingual Index · Output speed: output tokens per second
Most attractive quadrant
Reasoning models are indicated by a lightbulb icon

There is a trade-off between model quality and output speed, with higher intelligence models typically having lower output speed.

An index assessing multilingual performance in general reasoning across multiple languages. Results are computed across English, Chinese, Hindi, Spanish, French, Arabic, Bangla, Portuguese, Indonesian, Japanese, Swahili, German, Korean, Italian, Yoruba, Burmese. See Multilingual Intelligence Index methodology for further details.

Tokens per second received while the model is generating tokens (ie. after first chunk has been received from the API for models which support streaming).

Multilingual Index: media vs. precio

Artificial Analysis Multilingual Index · Average across all languages
Most attractive quadrant
Reasoning models are indicated by a lightbulb icon

While higher intelligence models are typically more expensive, they do not all follow the same price-quality curve.

An index assessing multilingual performance in general reasoning across multiple languages. Results are computed across English, Chinese, Hindi, Spanish, French, Arabic, Bangla, Portuguese, Indonesian, Japanese, Swahili, German, Korean, Italian, Yoruba, Burmese. See Multilingual Intelligence Index methodology for further details.

Price per token, shown in USD per million tokens. Price is a blend of cache hit, input, and output token prices using the selected ratio (default 7:2:1 cache-input-output).

Global-MMLU-Lite

Global-MMLU-Lite multilingüe: media

Average across all languages · Higher is better
Reasoning models are indicated by a lightbulb icon

A multilingual version of Massive Multitask Language Understanding, evaluated across multiple languages. Tests general knowledge and reasoning ability in areas like science, humanities, mathematics and more. See methodology for further details.

Precios

Pricing: Cache Hit, Input, and Output

Price (USD per M Tokens)
Reasoning models are indicated by a lightbulb icon

Price per token for cached prompts (previously processed), typically offering a significant discount compared to regular input price, represented as USD per million tokens. The values shown here are the cache hit price; cache write and cache storage are billed separately and vary by provider — see "Cache pricing by provider" for detail.

Price per token included in the request/message sent to the API, represented as USD per million Tokens.

The blended cache price shown here uses cache hit price only. Other caching costs differ by provider:

  • Anthropic: charges a separate cache write fee, with different rates for 5-minute and 1-hour TTLs (1-hour TTL is more expensive).
  • Google (Vertex/Gemini): charges a per-hour cache storage fee in addition to cache hit pricing. Some providers also use tiered pricing for prompts above 200K tokens.
  • OpenAI, DeepSeek, others: typically charge only cache hit pricing with no write or storage fee.

See Prompt Caching for the full breakdown.

Price per token generated by the model (received from the API), represented as USD per million Tokens.

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Velocidad y latencia

Output Speed

Output tokens per second · Higher is better
Reasoning models are indicated by a lightbulb icon

Tokens per second received while the model is generating tokens (ie. after first chunk has been received from the API for models which support streaming).

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Latency: Time To First Answer Token

Seconds to first answer token received · Accounts for reasoning model 'thinking' time
Reasoning models are indicated by a lightbulb icon

Time to first answer token received, in seconds, after API request sent. For reasoning models, this includes the 'thinking' time of the model before providing an answer. For models which do not support streaming, this represents time to receive the completion.

End-to-End Response Time

Seconds to output 500 tokens, including reasoning model 'thinking' time · Lower is better
Reasoning models are indicated by a lightbulb icon

Seconds to receive a 500 token response. Key components:

  • Input time: Time to receive the first response token
  • Thinking time (only for reasoning models): Time reasoning models spend outputting tokens to reason prior to providing an answer. Amount of tokens based on the average reasoning tokens across a diverse set of 60 prompts (methodology details).
  • Answer time: Time to generate 500 output tokens, based on output speed

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).