Comparative Evaluation of ChatGPT and DeepSeek Across Key NLP Tasks: Strengths, Weaknesses, and Domain-Specific Performance

Abstract

As large language models (LLMs) become increasingly embedded in a wide range of natural language processing (NLP) tasks, rigorous evaluation of their domain-specific strengths and limitations becomes critical. This paper presents a detailed comparative study of two prominent models: ChatGPT (based on OpenAI's GPT architecture) and DeepSeek, a newer entrant claiming high performance with a lightweight architecture. By evaluating both models across five essential NLP tasks—sentiment analysis, topic classification, text summarization, machine translation, and textual entailment—this study offers practitioners a framework to choose the most suitable model for their use case.

ChatGPT initially used a Microsoft Azure supercomputing infrastructure, powered by Nvidia GPUs, that Microsoft built specifically for OpenAI and that reportedly cost "hundreds of millions of dollars". Following ChatGPT's success, Microsoft dramatically upgraded the OpenAI infrastructure in 2023.[56] TrendForce market intelligence estimated that 30,000 Nvidia GPUs (each costing approximately $10,000–15,000) were used to power ChatGPT in 2023.

Introduction

Natural Language Processing has evolved rapidly with the advent of transformer-based models, especially LLMs like GPT-4, DeepSeek-R1, Claude, and Gemini. While many of these models achieve near-human performance in general language understanding, fine-grained comparative analyses across distinct tasks and domains remain sparse.

ChatGPT, known for its conversational fluency and contextual understanding, is widely adopted across academic and commercial settings. DeepSeek, on the other hand, promotes itself as an open, efficient, and powerful alternative, specifically optimized for inference speed and cost-effectiveness.

This research conducts a side-by-side evaluation of these two LLMs, employing a task-centric and dataset-diverse methodology to answer three core questions:

1. What are the comparative strengths and weaknesses of ChatGPT and DeepSeek across core NLP tasks?

2. How do they perform in domain-specific applications (news, reviews, formal/informal text)?

3. What implications do these results hold for real-world model deployment?

Methodology

Model Overview

• ChatGPT (GPT-4.0-turbo)
  Trained by OpenAI with reinforcement learning from human feedback (RLHF), the model is optimized for general-purpose use. It supports longer contexts and displays advanced understanding in multi-turn dialogue and semantic nuance.

• DeepSeek-R1-8B
  A 8-billion parameter open-source model trained with supervised fine-tuning and self-alignment. It uses Mixture-of-Experts (MoE) architecture to dynamically activate subsets of parameters per token.

Tasks and Benchmarks

Each model was evaluated across five tasks, using two datasets per task to control for variability in language style and domain.

Evaluation Criteria

• Accuracy / F1-score (for classification tasks)

• ROUGE and BLEU scores (for summarization and translation)

• Logical consistency and error rate (for entailment)

Each prompt was phrased neutrally and identically for both models, with temperature and max tokens held constant. Multiple inference runs were used to control for stochastic variation.

Results and Analysis

1. Sentiment Analysis

Performance Summary:

Analysis:

DeepSeek outperformed ChatGPT on IMDb, showing more stability in binary sentiment classification, particularly in handling sarcastic reviews. However, ChatGPT showed slightly better generalization across varied Amazon product categories.

🟢 Conclusion: DeepSeek may be preferable when dealing with clear sentiment signals, while ChatGPT is more robust to mixed or nuanced language.

2. Topic Classification

Performance Summary:

Analysis:

DeepSeek exhibited stronger topic disambiguation, especially for shorter text snippets in the AG News dataset. ChatGPT, while close, sometimes defaulted to over-generalized topics.

🟢 Conclusion: For taxonomic tasks or categorization involving specific labels, DeepSeek holds an edge.

3. Text Summarization

Performance Summary (ROUGE-1 / ROUGE-L):

Analysis:

ChatGPT outperformed DeepSeek in both datasets, showing better abstraction, coherence, and pronoun resolution. DeepSeek summaries tended to be extractive, sometimes copying input sentences verbatim.

🟢 Conclusion: ChatGPT is superior for abstractive summarization, especially for user-generated or informal text.

4. Machine Translation

Performance Summary (BLEU):

Analysis:

DeepSeek demonstrated more fluent and syntactically correct translations, particularly in handling formal registers and longer sentences. ChatGPT was slightly weaker with verb conjugation and idiomatic expressions in French.

🟢 Conclusion: DeepSeek is a better fit for real-time translation pipelines or low-latency tasks.

5. Textual Entailment

Performance Summary:

Analysis:

ChatGPT excelled in reasoning tasks where understanding semantic entailment and contradiction was critical. DeepSeek showed more errors when subtle inferences or world knowledge were needed.

🟢 Conclusion: ChatGPT is clearly stronger in logical reasoning and natural language inference.

Discussion

A. Strengths Overview

B. Weaknesses Overview

C. Domain-Specific Applications

• E-commerce Platforms: DeepSeek for classification and translation; ChatGPT for customer service bots.

• News Aggregators: ChatGPT for summarization and entailment; DeepSeek for topic categorization.

• Academic Use: ChatGPT is preferred for writing assistance and critical analysis.

Conclusion

This comparative study highlights that no single LLM dominates across all NLP tasks. ChatGPT shines in tasks involving deep semantic understanding, summarization, and entailment, while DeepSeek provides efficient, accurate classification and translation performance—often at a lower computational cost.

For organizations seeking real-time, cost-efficient NLP solutions, DeepSeek offers a strong alternative. However, when nuanced interpretation, dialogue, or critical thinking are involved, ChatGPT remains superior.

Future Work

• Integrate additional models (e.g., Claude, Gemini, Mistral) for broader comparison.

• Extend to multilingual datasets and low-resource languages.

• Explore fine-tuning on domain-specific corpora to improve model adaptability.