Sprache: Bengalisch – Benchmark für KI-Modelle Mehrsprachige LLM-Leistung vergleichen

Die fünf besten KI-Modelle für Aufgaben in der Sprache Bengalisch sind Gemini 3.1 Pro Preview, Claude Opus 4.6 (max), GPT-5 (high), Gemini 3 Pro Preview (high) und GPT-5.1 (high). Sie erreichen im Artificial Analysis Multilingual Index die höchsten Werte für Schlussfolgern in Bengalisch.

Einen Leistungsvergleich über alle unterstützten Sprachen finden Sie auf der vollständigen Seite zum mehrsprachigen Benchmark für KI-Modelle.

🇧🇩 Führende Modelle für Bengalisch

#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

Wichtigste Ergebnisse

Intelligenz

Multilingual Index: Bangla · Higher is better

Geschwindigkeit

Output tokens per second · Higher is better

Preis

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

Multilingual Index

Multilingual Index: Sprache Bengalisch

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: Sprache Bengalisch vs. Preis

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: Sprache Bengalisch vs. Ausgabegeschwindigkeit

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: Sprache Bengalisch vs. Kontextfenster

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

Mehrsprachiges Global-MMLU-Lite: Sprache Bengalisch

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.

Preise

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.

Geschwindigkeit und Latenz

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).