GPT-3.5 Turbo is OpenAI's cost-optimized model designed for high-throughput, latency-sensitive workloads. Released as the engine behind the original ChatGPT, it processes text quickly and affordably, making it the default choice for applications that need to call the model thousands of times per day without the cost profile of GPT-4 variants.
It handles routine language tasks well — summarization, classification, extraction, simple Q&A, and basic code generation — but is outperformed by later models on complex reasoning, nuanced instruction compliance, and long-form accuracy. Its low per-token cost and predictable behavior keep it relevant for production pipelines where simple tasks at scale are the primary requirement.
Key Features
Very low per-token cost relative to GPT-4 class models
Fast inference suitable for latency-sensitive real-time applications
Reliable for classification, extraction, and short-form summarization
Function calling support for structured tool-use pipelines
Broad availability with extensive third-party library support
Consistent behavior for well-defined, constrained prompt patterns
Ideal Use Cases
High-volume content classification and tagging pipelines
Automated customer support responses for simple, repetitive queries
Data extraction and transformation at scale from structured text
Chatbot backends where cost-per-session is a primary constraint
Rapid prototyping and experimentation before graduating to larger models
Example Prompts for GPT-3.5 Turbo
Technical Specifications
| Provider | OpenAI |
| Category | Text |
| Modality | Text -> Text |
| Context Window | 16K tokens |
| Training Cutoff | September 2021 |
Frequently Asked Questions
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