이탈리아어 언어 - AI 모델 벤치마크 다국어 LLM 성능 비교

이탈리아어 언어 작업의 상위 AI 모델 5개는 Gemini 3.1 Pro Preview, Gemini 3 Pro Preview (high), Claude Opus 4.6 (max), Qwen3.5 397B A17B, Claude Opus 4.5입니다. Artificial Analysis Multilingual Index에서 이탈리아어 언어 추론 점수가 가장 높은 모델입니다.

지원되는 모든 언어의 성능을 비교하려면 전체 다국어 AI 모델 벤치마크 페이지를 확인하세요.

🇮🇹 이탈리아어 언어 상위 모델

#1
Gemini 3.1 Pro PreviewGemini 3.1 Pro Preview
95#2
Gemini 3 Pro Preview (high)Gemini 3 Pro Preview (high)
94#3
Claude Opus 4.6 (Adaptive Reasoning, Max Effort)Claude Opus 4.6 (max)
94#4
Qwen3.5 397B A17B (Reasoning)Qwen3.5 397B A17B
93#5
Claude Opus 4.5 (Reasoning)Claude Opus 4.5
93

주요 내용

지능

Multilingual Index: Italian · Higher is better

속도

Output tokens per second · Higher is better

가격

USD per 1M tokens (blended) · Lower is better

Multilingual Index

Multilingual Index: 이탈리아어 언어

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

Based on the Global-MMLU-Lite evaluation, assessing general reasoning performance in a single language. Results are computed exclusively within the selected language. See methodology for further details.

Multilingual Index: 이탈리아어 언어와 가격

Artificial Analysis Multilingual Index · USD per 1M tokens (blended)
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.

Multilingual Index: 이탈리아어 언어와 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: 이탈리아어 언어와 컨텍스트 창

Artificial Analysis Multilingual Index · Context window: tokens limit
Most attractive quadrant
Reasoning models are indicated by a lightbulb icon

Based on the Global-MMLU-Lite evaluation, assessing general reasoning performance in a single language. Results are computed exclusively within the selected language. See methodology for further details.

Global-MMLU-Lite

다국어 Global-MMLU-Lite: 이탈리아어 언어

Multilingual Global-MMLU-Lite · 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).