다국어 AI 모델 벤치마크 언어별 주요 LLM 비교

Global-MMLU-Lite 벤치마크를 포함한 Artificial Analysis Multilingual Index에서 주요 대규모 언어 모델(LLM)이 여러 언어에 걸쳐 어떤 성능을 보이는지 살펴보세요. 언어와 모델로 필터링하고 정확도, 속도, 비용의 상충 관계를 확인해 다국어 사용 사례에 가장 적합한 LLM을 찾을 수 있습니다.

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개요

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.

언어별 Multilingual Index(정규화)

테스트한 모든 모델에서 언어별 점수를 정규화합니다. 초록색은 해당 언어의 최고 점수, 빨간색은 최저 점수를 나타냅니다.
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: 전체 언어 평균

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: 평균과 출력 속도

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.

Multilingual Index: 평균과 가격

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.

Global-MMLU-Lite

다국어 Global-MMLU-Lite: 평균

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.

가격

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.

속도 및 지연 시간

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).