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Comparative Analysis Based on DeepSeek, ChatGPT, and Google Gemini: Features, Techniques, Performance, Future Prospects

Anichur Rahman, Shahariar Hossain Mahir, Md Tanjum An Tashrif, Airin Afroj Aishi, Md Ahsan Karim, Dipanjali Kundu, Tanoy Debnath, Md. Abul Ala Moududi, MD. Zunead Abedin Eidmum

arXiv:2503.04783v1cs.CLcs.CR

TL;DR

This study examines the strengths, weaknesses, and trade-offs of DeepSeek, ChatGPT, and Gemini across accuracy, reasoning, programming, and response generation. It compares their architectures and applications, finding distinct strengths in efficiency, speed, and multimodal data handling alongside ongoing concerns about bias, hallucinations, and energy use.

  • Problem

    The study investigates how DeepSeek, ChatGPT, and Gemini compare across accuracy, logical and numerical reasoning, programming ability, response generation, and underlying architecture.

  • Method

    The paper conducts a comparative analysis of the three models, examining their architectures, methods, applications, and domain-specific mechanisms.

  • Results

    Tests suggest DeepSeek R1 excels in efficiency, ChatGPT in speed, and Gemini in handling diverse data formats, while DeepSeek achieves high performance with fewer resources.

  • Takeaways & Limitations

    The findings indicate that the models have distinct strengths and trade-offs, and that combining their capabilities could offer a more balanced solution.

  • Takeaways & Limitations

    The models depend heavily on training-data quality and quantity, with poor or biased data potentially producing inaccurate or unfair outcomes.

Abstract

from arXiv · show

Nowadays, DeepSeek, ChatGPT, and Google Gemini are the most trending and exciting Large Language Model (LLM) technologies for reasoning, multimodal capabilities, and general linguistic performance worldwide. DeepSeek employs a Mixture-of-Experts (MoE) approach, activating only the parameters most relevant to the task at hand, which makes it especially effective for domain-specific work. On the other hand, ChatGPT relies on a dense transformer model enhanced through reinforcement learning from human feedback (RLHF), and then Google Gemini actually uses a multimodal transformer architecture that integrates text, code, and images into a single framework. However, by using those technologies, people can be able to mine their desired text, code, images, etc, in a cost-effective and domain-specific inference. People may choose those techniques based on the best performance. In this regard, we offer a comparative study based on the DeepSeek, ChatGPT, and Gemini techniques in this research. Initially, we focus on their methods and materials, appropriately including the data selection criteria. Then, we present state-of-the-art features of DeepSeek, ChatGPT, and Gemini based on their applications. Most importantly, we show the technological comparison among them and also cover the dataset analysis for various applications. Finally, we address extensive research areas and future potential guidance regarding LLM-based AI research for the community.

I. INTRODUCTION

The paper compares DeepSeek, ChatGPT, and Gemini by examining their architectures, applications, data, performance, and future research directions. It combines systematic paper selection with technical comparison and benchmarking across reasoning, accuracy, and domain-specific capabilities.

  • The study develops a comprehensive comparison of DeepSeek, ChatGPT, and Gemini across architectures, training methods, and domain-specific applications.
  • Its analysis includes technical comparison, standardized benchmarking, data selection, and discussion of limitations and future opportunities.
  • The review is organized around methods, state-of-the-art features, comparative performance, datasets, performance analysis, and future directions.
  • The paper uses PRISMA-guided literature selection and excludes studies lacking methodological soundness, comparative scope, or real-world applicability.
  • The research question examines how the models differ in accuracy, reasoning, programming, response generation, usability, efficiency, and real-task performance.

B. Paper Collection

The paper collection follows a transparent PRISMA-based process that screens literature for comparative, methodologically sound, and practically relevant evidence. It also aligns datasets and evaluation settings with each model’s design and specialization.

  • 59 articles were selected after 127 screened papers and 86 full-text eligibility assessments.
  • Eligible studies addressed model architecture, comparative performance, domain-specific tasks, evaluation metrics, or real-world application issues.
  • General-purpose and domain-specific datasets included Common Crawl, WebText, BooksCorpus, PubMed, arXiv, GitHub, and Stack Overflow.
  • DeepSeek used curated medical, legal, and financial corpora, ChatGPT used general web, dialogue, and code data, and Gemini used multimodal data.
  • Evaluation covered quantitative and logical reasoning with MMLU and coding proficiency with HumanEval.
  • Experiments used standardized computing conditions, fixed input lengths, batch sizes, memory allocations, and scalability tests.

B. After Releasing Modern Generative Artificial Intelligence (AI) Tools

Modern generative AI tools advanced NLP by producing coherent, context-rich outputs, with DeepSeek, ChatGPT, and Gemini emphasizing different architectural strengths. Their evolution reflects a shift from static representations toward specialized, conversational, and multimodal systems.

  • DeepSeek R1 uses Mixture-of-Experts activation to improve compute efficiency and support domain-specific applications.
  • ChatGPT combines a dense transformer with RLHF to support rapid, fluent, context-aware dialogue.
  • Gemini processes text, code, and visual data through a multimodal transformer for generative and cross-modal tasks.
  • The models exhibit distinct strengths: DeepSeek emphasizes efficiency and domain robustness, ChatGPT conversational agility, and Gemini multimodal integration.
  • Early word representations: Early Word2Vec and GloVe models used static representations, limiting context-dependent meaning.
  • Early word representations: FastText extended embeddings with subword information, improving handling of unusual and morphologically complex words.

2) Contextualized Language Models (2018-2019):

Contextualized and transformer-based models addressed limitations of static embeddings and expanded language-model capabilities. This progression led from bidirectional understanding and autoregressive generation to large, adaptable generative systems such as ChatGPT and DeepSeek.

  • Contextualized Language Models (2018-2019): ELMo generated context-dependent word representations with deep bidirectional LSTMs, while ULMFit demonstrated transfer learning for downstream NLP tasks.
  • Transformer-based Pre-trained Models (2018-2019): BERT used bidirectional attention with masked language modeling and next sentence prediction, but was not designed for text generation.
  • Transformer-based Pre-trained Models (2018-2019): GPT-1 used autoregressive decoding and BooksCorpus training to generate coherent text, but its 117 million parameters limited generalization.
  • Transformer-based Pre-trained Models (2018-2019): GPT-2 introduced zero-shot learning with 1.5 billion parameters, while T5 framed NLP tasks as text-to-text problems.
  • Transformer-based Pre-trained Models (2018-2019): GPT-3 scaled to 175 billion parameters and demonstrated few-shot and zero-shot learning, influencing commercial systems such as ChatGPT.
  • Generative Pre-trained Models: Contemporary generative models are pretrained on large text datasets and then adjusted for particular applications.
  • Generative Pre-trained Models: ChatGPT emphasizes RLHF-based conversational text generation, whereas DeepSeek R1 uses sparse MoE activation for computational efficiency.

3) Gemini (Google DeepMind):

The study compares DeepSeek, ChatGPT, and Gemini by architecture, computational demands, and application-oriented capabilities. Gemini emphasizes multimodal processing, while DeepSeek and ChatGPT emphasize efficient domain-specific reasoning and broad text-based performance, respectively.

  • Performance comparison: Gemini’s multimodal advantages are accompanied by optimization issues in applications that are solely text-based compared with ChatGPT.Handling varied data types also requires substantial computational power, although its architecture minimizes requirements for multimodal integration.
  • DeepSeek: DeepSeek’s Mixture-of-Experts architecture activates only part of its parameters during inference, increasing efficiency and reducing training costs.This makes it suitable for scalable, resource-constrained applications and large-scale deployments.
  • ChatGPT: ChatGPT’s dense transformer enables strong performance across diverse NLP tasks, supporting versatile and dependable text-based applications.The trade-off is substantial computational resource requirements during training and higher operational expenses.
  • Gemini architecture: Gemini uses a multimodal transformer design to process and generate text, pictures, audio, and video.Its architecture integrates multiple data formats within one framework.
  • Performance comparison: DeepSeek supports logical reasoning and problem solving, while targeted parameter activation improves performance in specific applications.Its efficiency-oriented design is associated with domain-focused use cases.
  • Gemini capabilities: Gemini excels at multimodal data processing and production for multimedia content creation and cross-modal analysis.These applications require comprehensive interpretation and generation across text, graphics, audio, and video.

V. POPULAR DATASETS FOR DEVELOPING THE DEEPSEEK, CHATGPT, AND GEMENI APPLICATIONS

DeepSeek, ChatGPT, and Gemini share Transformer foundations but differ in architecture, objectives, modalities, and dataset composition. Their training data and model-specific objectives are presented as factors relevant to application strengths and weaknesses.

  • Shared architectural foundation: DeepSeek, ChatGPT, and Gemini share a Transformer foundation with attention, positional encoding, and feed-forward components.The models retain a common sequence-modeling architecture while adding model-specific mechanisms.
  • Dataset composition: Training datasets differ in source, modality mix, proportions, curation, and fine-tuning strategy across the three models.The paper directs readers to comparative dataset tables covering these differences.
  • DeepSeek: DeepSeek adds domain-specific objective weighting and selectively activated expert heads for relevant domain corpora.Its loss combines negative log-likelihood with a domain term scaled by β.
  • ChatGPT: ChatGPT incorporates human-feedback rewards into its training objective and filters harmful or low-quality tokens through attention masks.The parameter α controls the influence of human feedback.
  • Gemini: Gemini uses cross-attention to fuse text, code, and image representations and combines language, code, and image training objectives.The coefficients γ1 and γ2 weight the code and image tasks.

VI. RESULT AND PERFORMANCE ANALYSIS OF DEEPSEEK, CHATGPT, AND GEMENI

The study compares leading language models across multiple evaluation tasks and reports that DeepSeek is a competitive contender. Its evaluation covers reasoning, coding, multilingual understanding, and other benchmarked capabilities.

  • Comparative evaluation: The study conducts a panoptic evaluation of multiple leading models across different performance dimensions.The evaluated set includes DeepSeek, Gemini, GPT, Claude, Llama, Qwen, and OpenAI reasoning models.
  • DeepSeek performance: DeepSeek is reported as a competitive contender with strong results in reasoning, coding, and multilingual understanding.The passage presents this as the study’s model-level performance characterization.

A. Experimental Setup

The experimental setup evaluates models with independent single queries and 1,000-token prompts, then compares quality and reasoning across several benchmarks. The measures cover general knowledge, scientific reasoning, mathematics, and programming.

  • Query setup: Each model is tested independently with a single-query setup and a 1,000-token input prompt.The configuration is intended to compare response time, coherence, and output quality without concurrent-request interference.
  • Overall quality: The Artificial Analysis Quality Index aggregates response accuracy, knowledge depth, and logical coherence across multiple benchmarks.The index is normalized for differential comparison among models.
  • Knowledge reasoning: MMLU evaluates factual recall, contextual reasoning, and complex question answering across diverse knowledge domains.Higher scores indicate stronger general-knowledge reasoning and inference accuracy.
  • Scientific reasoning: GPQA Diamond measures scientific reasoning in physics, mathematics, and engineering through multi-step problem-solving tasks.The benchmark combines symbolic logic with real-world scientific understanding.
  • Mathematical reasoning: MATH-500 assesses numerical reasoning through algebra, calculus, combinatorics, symbolic manipulation, equation solving, and logical deduction.The evaluation focuses on structured quantitative problem solving.
  • Programming evaluation: HumanEval is used to evaluate programming efficiency.The supplied passage identifies the benchmark but does not provide a result value.

5) Coding Evaluation:

The study evaluates coding alongside quantitative, scientific, multilingual, general, and reasoning capabilities using standardized benchmarks. It also highlights trade-offs involving cost, scalability, safety, multimodal integration, and energy use.

  • Coding Evaluation: HumanEval evaluates functionally correct code generation, including logical reasoning, syntax correctness, and functional efficiency.Higher scores indicate stronger algorithmic thinking and problem-solving skills.
  • Benchmark Coverage: The evaluation also covers quantitative reasoning with MATH-500, scientific reasoning with GPQA Diamond, and multilingual performance through the Artificial Analysis Multilingual Index.The multilingual index considers linguistic diversity, syntactic structure, semantic coherence, token-generation efficiency, latency, and blended cost efficiency.
  • Benchmark Coverage: General performance is compared across 10 industry-standard exams, while reasoning performance is assessed across 10 established reasoning benchmarks.The general exams include MMLU, SAT Math, LSAT Logical Reasoning, USMLE Step 1, GRE, GMAT, TOEFL, and ACT Composite; the reasoning set includes HellaSwag, CommonsenseQA, ARC-Challenge, and Winogrande.
  • Trade-offs: DeepSeek R1 achieves competitive reasoning at a training cost under $6 million, but the efficiency can coincide with slower responses and limited scalability under heavy demand.The passage frames performance, cost, response time, and scalability as a joint trade-off.
  • Trade-offs: The comparison identifies safety, fairness, transparency, multimodal reliability, computational overhead, latency, and environmental sustainability as continuing challenges.Multimodal systems may misrepresent simple visual details, while chain-of-thought techniques can add computational overhead and latency.

B. Future Opportunities

The paper describes future opportunities through reflective reasoning, open-source development, and model-specific applications. It presents DeepSeek, ChatGPT, and Gemini as complementary systems for technical, conversational, multimodal, and domain-specific work.

  • Future Opportunities: Refined chain-of-thought techniques are proposed to improve accuracy and clarity without introducing unacceptable latency.The proposal extends reflective thinking within LLM architectures.
  • Future Opportunities: Open-source initiatives such as DeepSeek R1, released under an MIT license, may support community refinement and lower-cost innovation.The paper connects this direction with possible hybrid systems combining complementary model capabilities.
  • DeepSeek: DeepSeek supports code generation, debugging, implementation, and specialized technical or scientific work with fewer resources.The cited applications include healthcare, finance, customer service, and education.
  • ChatGPT: ChatGPT supports writing, brainstorming, summarization, coding, tutoring, research, customer service, and coherent context preservation across exchanges.Its multilingual comprehension and generation capabilities broaden access to general and specialized inquiries.
  • Gemini: Gemini is designed to process and generate text, images, audio, video, and code, while Gemini 2.0 Flash Thinking explains answers to complex questions.These capabilities support multimodal interaction and user understanding.

B. Limitations •

The paper identifies limitations involving hallucinations, data quality, temporal awareness, access, computational requirements, modality coverage, and commonsense reasoning. These constraints vary across models and can affect reliability, applicability, and public access.

  • Reliability: DeepSeek, ChatGPT, and Gemini can generate hallucinations, especially when queried about topics beyond their training data.ChatGPT may produce plausible-sounding but incorrect or nonsensical answers.
  • Data: The models’ performance depends heavily on training-data quality and quantity, while poor or biased data can produce inaccurate or unfair outcomes.This limitation applies across the compared models.
  • Reliability: The models may fail to acknowledge temporal limitations, producing confident but incorrect responses about events beyond their training period.ChatGPT may also have outdated knowledge without web browsing.
  • Model-Specific Limitations: DeepSeek does not support image analysis and may have less general knowledge, less human-like creativity, and a smaller user community than competitors.Its strengths remain concentrated in specialized areas.
  • Model-Specific Limitations: Gemini may require significant computational resources, remains primarily accessible through Google Cloud platforms, and may lag in commonsense reasoning.Effective use can also require advanced coding and AI skills.

IX. DISCUSSION

The comparison identifies distinct strengths and trade-offs across DeepSeek R1, ChatGPT, and Google Gemini, while highlighting unresolved concerns about bias, hallucinations, and energy use. It concludes that combining their strengths in hybrid systems could support more balanced and sustainable AI development.

  • Model strengths and trade-offs: ChatGPT is characterized by fast responses and agile conversational abilities, although it can struggle with complex prompts.Its dense transformer framework is enhanced by reinforcement learning from human feedback.
  • Model strengths and trade-offs: DeepSeek R1 excels in efficiency by directing computation toward domain-specific queries through its mixture-of-experts architecture.This reduces costs while supporting stable performance in specialized medical and legal tasks.
  • Comparative findings: The models perform best in different areas: DeepSeek R1 in efficiency, ChatGPT in speed, and Gemini in diverse data-format handling.The comparison presents these capabilities as corresponding strengths rather than a single universal advantage.
  • Future direction: Bias, hallucinations, and energy use remain unresolved challenges, motivating the proposed direction toward hybrid systems that combine model strengths.The paper frames hybrid integration as a potential route toward more balanced and sustainable AI systems.
  • Model strengths and trade-offs: Gemini handles text, code, and visual inputs through a multimodal transformer, supporting creative content generation and cross-domain analysis.Its multimodal capabilities require considerable computational resources.
  • Study scope: The study compares DeepSeek, ChatGPT, and Gemini by examining their characteristics, methods, performance indicators, and future possibilities.Its conclusion reports DeepSeek’s specialized efficiency, ChatGPT’s flexibility, and Gemini’s multimodal processing.
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