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Also known as: gpt-3.5-turbo, text-davinci-003
An OpenAI language model series offering strong general capabilities, instruction following, and efficient chat-oriented performance.
DESCRIPTION
GPT-3.5 is a widely adopted series that improved instruction following and chat reliability over GPT-3. It powers many assistants, classification pipelines, and content generation workflows due to cost-performance balance. It supports function calling, retrieval-augmented workflows, and safety tooling. Many systems now migrate to newer models but still use GPT-3.5 for latency- and cost-sensitive tasks.
ALTERNATIVE DEFINITIONS
OpenAI
GPT-3.5 Turbo is optimized for chat but works well for traditional completions tasks.
Notes chat optimization while remaining capable for general completion tasks.
OpenAI
Instruction-following models are trained to follow natural language instructions, enabling more useful and reliable behavior.
Explains the instruction-following paradigm that underpins GPT-3.5’s behavior.
RELATED TERMS
GPT-3 GPT-4 Instruction Tuning Chat Completion API RLHF Token Limits Zero-shot Learning Few-shot Prompting Safety Filters Function Calling
COMPETING TERMS
Llama 2 Claude 3 Haiku
KNOWLEDGE AREAS
AI Data Science MLOps Platform & Tooling
TARGET AUDIENCE
AI Engineer Data Scientist MLOps Engineer AI Product Manager Software Developer
EXTERNAL SOURCES
TOP QUESTIONS
What is GPT-3.5 best suited for?
Cost-efficient chat, summarization, classification, and structured extraction with reasonable instruction following.
Category: UseCases
How does GPT-3.5 differ from GPT-3?
It improves instruction adherence, context handling, and chat capabilities while maintaining competitive pricing.
Category: Comparison
What are common mistakes with GPT-3.5?
Overreliance without evaluation, not constraining outputs, and insufficient grounding lead to hallucinations.
Category: Mistakes
How do I reduce hallucinations with GPT-3.5?
Use retrieval augmentation, schema validation, and step-wise prompting; implement human review for critical tasks.
Category: HowTo
What are GPT-3.5 alternatives?
Open models like Llama 2 and proprietary models such as Claude 3 Haiku can be viable substitutes.
Category: Alternatives
How should I evaluate GPT-3.5?
Use domain-specific golden sets, measure accuracy and consistency, and track cost/latency trade-offs.
Category: Validation
Is GPT-3.5 still relevant?
Yes, for budget-sensitive or low-latency use cases even as newer models surpass it in capability.
Category: Relevancy
Can GPT-3.5 handle function calling?
Yes, it supports structured tool invocation with specified argument schemas to integrate external systems.
Category: Features
What’s the biggest misunderstanding about GPT-3.5?
That it cannot be reliable; with grounding, constraints, and evaluation, it performs robustly for many applications.
Category: Misunderstanding