Burmese Language - AI Models Benchmark Compare Multilingual LLM Performance

The top 5 AI models for Burmese language tasks are Gemini 3.1 Pro Preview, Gemini 3 Flash, Gemini 3 Pro Preview (high), Claude Opus 4.5, and Gemini 2.5 Pro. They achieve the highest Burmese language reasoning scores in the Artificial Analysis Multilingual Index.

To compare performance across all supported languages, see the full Multilingual AI Model Benchmark page.

🇲🇲 Top models for Burmese language

#1
Gemini 3.1 Pro PreviewGemini 3.1 Pro Preview
91#2
Gemini 3 Flash Preview (Reasoning)Gemini 3 Flash
90#3
Gemini 3 Pro Preview (high)Gemini 3 Pro Preview (high)
90#4
Claude Opus 4.5 (Reasoning)Claude Opus 4.5
89#5
Gemini 2.5 ProGemini 2.5 Pro
89

Highlights

Multilingual Index: Burmese · Higher is better
Output tokens per second · Higher is better
USD per 1M tokens (blended) · Lower is better

Multilingual Index

Multilingual Index: Burmese Language

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: Burmese Language vs. Price

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: Burmese Language vs. Output Speed

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: Burmese Language vs. Context Window

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

Multilingual Global-MMLU-Lite: Burmese Language

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.

Pricing

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.

Speed & Latency

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).

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