Idioma Hindi - Benchmark de modelos de IA Compara rendimiento LLM multilingüe

Los 5 mejores modelos de IA para tareas en hindi son Gemini 3.1 Pro Preview, Claude Opus 4.6 (max), Gemini 3 Pro Preview (high), Claude Opus 4.5 (Non-reasoning) y GPT-5 (high). Logran las puntuaciones de razonamiento en hindi más altas en el Artificial Analysis Multilingual Index.

Para comparar el rendimiento en todos los idiomas compatibles, consulta la página completa del benchmark multilingüe de modelos de IA.

🇮🇳 Mejores modelos para Hindi

#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 (Non-reasoning)
91#5
GPT-5 (high)GPT-5 (high)
91

Aspectos destacados

Inteligencia

Multilingual Index: Hindi · Higher is better

Velocidad

Output tokens per second · Higher is better

Precio

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

Multilingual Index

Multilingual Index: idioma Hindi

Artificial Analysis Multilingual Index · Higher is better

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: idioma Hindi vs. precio

Artificial Analysis Multilingual Index · USD per 1M tokens (blended)
Most attractive quadrant

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

Multilingual Index: idioma Hindi vs. velocidad de salida

Artificial Analysis Multilingual Index · Output speed: output tokens per second
Most attractive quadrant

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

Multilingual Index: idioma Hindi vs. ventana de contexto

Artificial Analysis Multilingual Index · Context window: tokens limit
Most attractive quadrant

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

Global-MMLU-Lite multilingüe: idioma Hindi

Multilingual Global-MMLU-Lite · Higher is better

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)

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.

Velocidad y latencia

Output Speed

Output tokens per second · Higher is better

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

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

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).