ChatGPT-3.5 Overview

Updated 12 November 2025

ChatGPT-3.5 is an autoregressive transformer model characterized by a 175–200 billion parameter scale, multi-stage training, and improved zero- and few-shot generalization.
It achieves high performance in automated reasoning and coding tasks, with LeetCode success rates of up to 92% for easy and 51% for hard problems, while exhibiting challenges in deep logical complexity.
Its deployment across education, legal, healthcare, and translation domains highlights notable potential, yet requires structured mitigation to address context overload and domain-specific errors.

ChatGPT-3.5 is an autoregressive LLM developed by OpenAI, representing an evolution of the GPT series and distinguished by enhanced instruction-following, conversational fluency, and improved zero- and few-shot generalization. Architecturally, it continues the decoder-only transformer paradigm with extensive pre-training on web-scale corpora and subsequent reinforcement learning from human feedback. Recent empirical assessments interrogate its technical proficiency across code generation, reasoning, education, healthcare, mathematical problem-solving, and domain adaptation tasks, revealing notable trade-offs and deployment considerations.

1. Architectural Foundations and Training Regimen

ChatGPT-3.5 employs a transformer decoder stack—multi-head self-attention, feed-forward layers, and rotary position embeddings (RoPE)—with a parameter scale of approximately 175–200 billion. Training proceeds in three stages: (1) unsupervised language modeling over diverse Internet corpora; (2) supervised fine-tuning (SFT) via curated prompt–response pairs, optimizing causal cross-entropy loss $L_\text{CE}(\theta) = -\sum_t \log p_\theta(x_t\,|\,x_{<t})$ ; (3) RLHF using a separate reward model $r_\phi(y)$ , with the generation policy updated by a proximal policy optimization objective

$\underset{\theta}{\text{maximize}}\,\, \mathbb{E}_{y \sim p_\theta}[r_\phi(y)] - \beta\, \text{KL}(p_\theta\,\|\,p_\text{ref})$

where $\beta$ regulates divergence from the initial supervised policy (Bahrini et al., 2023).

2. Automated Reasoning and Coding Abilities

Extensive benchmarking on LeetCode (algorithmic coding tasks), StackOverflow Q&A, and multi-language code synthesis yields the following observations:

On LeetCode (n=1,475 problems; Python): success rates are 92% (easy), 79% (medium), 51% (hard) (Li et al., 12 Nov 2024). Prompt engineering amplifies gains, with chain-of-thought (CoT) boosting easy problems by 29%, and explicit failed-test feedback yielding up to 60% improvement on medium tasks. Model upgrade to GPT-4 provides a further 33–58% boost, especially on hard problems.
Across 10 programming languages and four software domains, the overall code execution success rate is 45.8%, peaking at 81.5% for Julia and 72% for R, with C++ notably lowest (7.3%). Complex game environments and statically typed languages present challenges, as does "ethical reasoning" variability across runs (Buscemi, 2023).
Human comparison studies on StackOverflow Q&A indicate ChatGPT-3.5 yields superior or preferred answers in 75% of direct comparisons and 68% of developer-blind surveys, mainly for readability and informativeness, but is less successful in deep codebase maintenance (46% full-code revision success) (Kabir et al., 2023).

Problem Domain	Success Rate / Accuracy	Noted Weaknesses
LeetCode Easy	92%	Context loss, rare corner cases
LeetCode Medium	79%	Complex recursion, DP, SQL tasks
LeetCode Hard	51%	Higher logical complexity, DP
Multi-language (Julia)	81.5% execution success	-
Multi-language (C++)	7.3% execution success	Compilation failures, pointer logic
StackOverflow Q&A	75% preferred/favored	Weak context tracking, domain specifics

3. Mathematical and Logical Reasoning

Evaluation on math and logic tasks highlights both strengths and persistent gaps:

On curated mathematical and logic questions, ChatGPT-3.5 attains 53.3% accuracy on novel problems and 42.2% on published ones—substantially below ChatGPT-4 and Google Bard (for online-available problems) (Plevris et al., 2023).
Cross-language assessments in math (25 problems/4 languages) show 76% accuracy in English, declining sharply in Hindi (32%), Marathi (28%), and Gujarati (20%) (Sathe et al., 18 May 2024). Chain-of-thought prompting increases English accuracy (+16 points, up to 92%) but offers negligible improvement in regional Indian languages.
Error patterns include digit-level arithmetic slips, misinterpretation of problem context, and over-literal parsing. Inconsistencies abound; repeated prompts yield conflicting answers up to two-thirds of the time.

4. Domain Adaptation: Education, Legal, Healthcare, and Translation

Education: Fine-tuning ChatGPT-3.5 on domain-specific student response datasets (science open-response scoring) yields mean accuracy of 0.915, exceeding BERT's 0.838 by 9.1% (Latif et al., 2023). Gains are pronounced in multi-class and unbalanced-label assessment tasks; only hundreds to thousands of in-domain examples are required for >90% accuracy.

Legal NLP: In zero-shot classification on the LexGLUE benchmark, ChatGPT-3.5 achieves micro-F1 scores up to 70.1% (LEDGAR), averaging 49%. While markedly exceeding random baselines ( $\sim$ 4.8%), this remains ~30 points below smaller fine-tuned LegalBERT models. Larger label sets (EURLEX) and long inputs reduce performance; template variation causes 2-point F1 swings (Chalkidis, 2023).

Medical QA and Triage: On USMLE Step 3 questions, accuracy drops from 72.1% to 68.9% (multiple-choice) and 61.5% to 44.3% (open-ended) when small talk is interleaved—a statistically significant effect for open questions ( $p=0.01$ ), not for MC ( $p=0.67$ ). The impairment is attributed to context overload and noise from irrelevant tokens (Safrai et al., 2023). In outpatient triage, internal consistency is moderate (59.6%), but completeness rate (probabilities, urgency) is high (83.3%), suggesting value for rapid decision support but risk of variable recommendations (Liu et al., 27 Apr 2024).

Translation: For Japanese–English translation, document-level prompts outperform sentence-level. ChatGPT-3.5 scores lower total MQM error than ChatGPT-4 (84.5 vs. 95.83), indicating higher accuracy (fewer omissions/mistranslations) but lower fluency. Automatic metrics (BLEU/COMET/DA-BERT) place ChatGPT-3.5 as competitive with leading commercial systems; enhanced prompts yield no conclusive improvement over simple ones (Sutanto et al., 9 Oct 2025).

5. Application-Specific Limitations and Emergent Strengths

Affective Computing: Zero-shot performance is robust for sentiment analysis (80.54% accuracy on Twitter140), opinion extraction (91.04%), toxic detection (87.37%), and suicide tendency (89.46%) (Amin et al., 2023). GPT-3.5 approaches RoBERTa performance on explicit sentiment/emotion but fails to surpass simple RNNs on engagement or subjectivity (≈52–60%), demonstrating a lack of world knowledge and subtle social-signal comprehension.

Monte Carlo Simulation and Data Generation: In IRT-aligned data generation, ChatGPT-3.5 algorithms realize unidimensionality and local independence, but default code often neglects strict parameter-range enforcement, leading to estimation bias and elevated RMSE compared to expert code. Integration as a “force-multiplier” for routine simulation is recommended, conditioned on human oversight and explicit code guardrails (Gurdil et al., 28 Jan 2024).

6. Opportunities and Threats in Deployment

Across domains, ChatGPT-3.5 exhibits strong deployment potential for automatic grading, real-time technical support, rapid code prototyping, and medical triage. Its weaknesses—context overload, stochastic inconsistency, lack of self-verification, and susceptibility to prompt-induced noise—necessitate application-layer mitigation: domain-specific fine-tuning, iterative feedback, structured prompting, and rigorous human supervision. The model’s broader exposure raises risks of misinformation, privacy leakage, and biased or unvetted analytics in business, government, and healthcare (Bahrini et al., 2023).

7. Future Directions

Research accentuates the following development and integration priorities:

Structural prompt engineering (e.g., chain-of-thought, failure-driven feedback) to augment LLM reasoning and correctness, primarily in low and medium-complexity tasks.
Expanding cross-lingual training corpora and tailoring reasoning templates for non-English contexts, to counter marked drops in regional language performance.
Hybrid workflows: combining model output with static analysis, retrieval augmentation, and expert feedback loops to enhance code and content reliability.
Investigating model specialization and domain-oriented pretraining (e.g., legal, healthcare, education) to mitigate observed deficits in highly technical or nuanced applications.

Collectively, ChatGPT-3.5 demonstrates wide-ranging proficiency and rapid-adaptation capacity, but its limitations—context stochastics, cross-lingual and domain-specific errors, and prompt sensitivity—reinforce the requirement for structured deployment and robust governance frameworks before full-scale integration in critical workflows.