  As large language models (LLMs) continue to advance, evaluating their
comprehensive capabilities becomes significant for their application in various
fields. This research study comprehensively evaluates the language, vision,
speech, and multimodal capabilities of GPT-4o. The study employs standardized
exam questions, reasoning tasks, and translation assessments to assess the
model's language capability. Additionally, GPT-4o's vision and speech
capabilities are tested through image classification and object recognition
tasks, as well as accent classification. The multimodal evaluation assesses the
model's performance in integrating visual and linguistic data. Our findings
reveal that GPT-4o demonstrates high accuracy and efficiency across multiple
domains in language and reasoning capabilities, excelling in tasks that require
few-shot learning. GPT-4o also provides notable improvements in multimodal
tasks compared to its predecessors. However, the model shows variability and
faces limitations in handling complex and ambiguous inputs, particularly in
audio and vision capabilities. This paper highlights the need for more
comprehensive benchmarks and robust evaluation frameworks, encompassing
qualitative assessments involving human judgment as well as error analysis.
Future work should focus on expanding datasets, investigating prompt-based
assessment, and enhancing few-shot learning techniques to test the model's
practical applicability and performance in real-world scenarios.
