ヒンディー語 - AIモデルベンチマーク 多言語LLMの性能を比較

ヒンディー語のタスクで上位5つのAIモデルは、Gemini 3.1 Pro Preview、Claude Opus 4.6 (max)、Gemini 3 Pro Preview (high)、Claude Opus 4.5、GPT-5 (high)です。Artificial Analysis Multilingual Indexで、ヒンディー語の推論スコアが最も高いモデルです。

対応するすべての言語で性能を比較するには、多言語AIモデルベンチマークのページをご覧ください。

🇮🇳 ヒンディー語の上位モデル

#1
Gemini 3.1 Pro PreviewGemini 3.1 Pro Preview
94#2
Claude Opus 4.6 (Adaptive Reasoning, Max Effort)Claude Opus 4.6 (max)
92#3
Gemini 3 Pro Preview (high)Gemini 3 Pro Preview (high)
91#4
Claude Opus 4.5 (Non-reasoning)Claude Opus 4.5
91#5
GPT-5 (high)GPT-5 (high)
91

ハイライト

知能

Multilingual Index: Hindi · Higher is better

速度

Output tokens per second · Higher is better

料金

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

Multilingual Index

Multilingual Index：ヒンディー語

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：ヒンディー語と料金

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：ヒンディー語と出力速度

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：ヒンディー語とコンテキストウィンドウ

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

多言語Global-MMLU-Lite：ヒンディー語

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.

料金

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.

速度と遅延

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).