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

Los 5 mejores modelos de IA para tareas en bengalí son Gemini 3.1 Pro Preview, Claude Opus 4.6 (max), GPT-5 (high), Gemini 3 Pro Preview (high) y GPT-5.1 (high). Logran las puntuaciones de razonamiento en bengalí 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 Bengalí

#1
Gemini 3.1 Pro PreviewGemini 3.1 Pro Preview
92#2
Claude Opus 4.6 (Adaptive Reasoning, Max Effort)Claude Opus 4.6 (max)
92#3
GPT-5 (high)GPT-5 (high)
91#4
Gemini 3 Pro Preview (high)Gemini 3 Pro Preview (high)
91#5
GPT-5.1 (high)GPT-5.1 (high)
91

Aspectos destacados

Inteligencia

Multilingual Index: Bangla · 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 Bengalí

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: idioma Bengalí vs. precio

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.

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.

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).

Multilingual Index: idioma Bengalí 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.

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.

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).

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).

Multilingual Index: idioma Bengalí vs. ventana de contexto

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.

Maximum number of combined input & output tokens. Output tokens commonly have a significantly lower limit (varied by model).

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

Global-MMLU-Lite

Global-MMLU-Lite multilingüe: idioma Bengalí

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.

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).