When OpenAI introduced GPT-3.5 in late 2022, it marked a clear leap forward from GPT-3 — more capable, faster, and better suited for practical applications. For a while, it was the default choice for many teams experimenting with generative AI, especially when performance needed to be balanced with affordability.
Since then, GPT-3.5 has been pushed out of the spotlight. First by ChatGPT-4, which raised the bar for performance, and then by ChatGPT-4o-mini, which quietly claimed its crown as the most budget-friendly option around.
So why is this GPT model still maintained? What is it capable of and does it still make sense to use it?
If you’re planning a GPT integration, this article will walk you through the current state of GPT-3.5 model.
Table of Contents
What is GPT-3.5 and can you still use it?
GPT-3.5 is a language model developed by OpenAI and released in November 2022. As the name suggests, it was a transitional step between GPT-3 and the more advanced GPT-4. While its exact size was never confirmed, estimates place it somewhere between 154 and 175 billion parameters — though OpenAI hasn’t shared official specs for any of the models in this series.
These days, when people mention GPT-3.5, they’re usually talking about GPT-3.5 Turbo — a faster and cheaper variant that quickly became the standard. And while newer models like GPT-4 and GPT-4o-mini are already out in the wild, 3.5 hasn’t been retired. Why? Because by the time the newer versions launched, many developers had already built products around it. Shutting it down would’ve broken those apps and workflows.
Faced with the choice between keeping the original GPT-3.5 or Turbo, OpenAI went with the obvious pick. Both are built on the same architecture, but the latter is more efficient and cost-effective — so that’s the version they kept. Today, it is still available via API, offering developers a reliable, lower-cost model to power their apps, tools, and services.
So, yes, you can still use GPT-3.5 in your project, however, there might be better options.
Core specs of GPT-3.5
Before you dive into building with GPT-3.5, it helps to know exactly what you’re working with.
| Property | Details |
| Release date | November 2022 for basic GPT-3.5 and March 2023 for GPT-3.5 Turbo |
| Model type | Large Language Model (LLM) |
| Parameters | Estimated ~154-175 billion |
| Context window | 16,384 tokens (≈ 12,000 words) |
| Modality | Text in / Text out only |
| Current API name | gpt-3.5-turbo |
| Fine-tuning support | Yes – available from August 2023. |
| Pricing | Input: $0.50 / 1M tokensOutput: $1.50 / 1M tokens |
| Status | Actively supported by OpenAI (budget/legacy model) |
If you’re looking for a well-documented, low-friction model that gets the job done without overcomplicating things, GPT-3.5 Turbo continues to be a smart pick.
Capabilities and limitations of GPT-3.5 Turbo
Here’s a full overview of what GPT-3.5 Turbo can and cannot do, based on OpenAI’s current model feature availability:
| Area | What it can do | What it cannot do |
| Modalities | Text in / text out – plain-text prompts and answers. | Image generation or editingSpeech-to-text (transcription)Text-to-speechMachine translation |
| Core interactions | Chat completion – remembers earlier turns and replies in context.Batch processing (one job with thousands of prompts run offline). | — |
| Built-in natural language processing tools | — | Embeddings – turns text into number vectors for similarity search.Content moderation – auto-flags unsafe or disallowed text. |
| Safety & compliance | — | Content moderation – automatic policy flagging. |
| Developer niceties | Fine-tuning.Function calling – triggers backend functions (e.g., getWeather) to integrate with external systems.• Structured outputs – enforces specific formats like JSON or XML for easier parsing. | Streaming responses – token-by-token delivery for faster user interaction.Model distillation – creates smaller, cheaper versions of the model for efficiency.Predicted outputs – previews likely responses to improve interaction flow. |
While GPT-3.5 Turbo works well for standard text generation tasks, it’s not equipped for multimodal or tool-enhanced use cases. That’s a major difference compared to newer models like GPT-4o, which can process and respond with images, audio, and more.
Want to compare GPT-3.5 specs with other AI models? Check out our article: https://neoteric.eu/blog/6-main-differences-between-llama2-gpt35-and-gpt4/
So… why is GPT-3.5 still around?
Let’s address the obvious. GPT-3.5 is not the best model OpenAI offers. It’s not the most powerful, accurate, or affordable anymore. Still, it hasn’t been deprecated, and plenty of companies continue to rely on it. Why?
Because it’s already there. Over time, many teams have built their infrastructure around GPT-3.5, including custom workflows, internal tools, and fine-tuned versions tailored to specific business needs. Even though newer models now support fine-tuning too, these existing setups often perform reliably and consistently.
Replacing them would mean retraining models, rewriting prompts, and rebuilding integrations, and in many cases, the effort simply doesn’t pay off. When predictable behavior and stability matter more than capabilities, GPT-3.5 continues to be a dependable choice.
Where GPT-3.5 still makes sense – practical use cases
GPT-3.5 wouldn’t be your first pick today, not with faster and cheaper models around. But if the development team already knows it well and has systems built around it, there are still cases where it makes perfect sense. Like these:
1. AI-powered personal assistants (for internal or customer use)
An assistant powered by GPT-3.5 is well suited for simple tasks like basic customer service, summarizing documents, or helping with scheduling.
But don’t expect it to handle long, complex conversations.
2. Text classification and tagging
From routing support tickets to tagging documents by topic, industry, or intent. GPT-3.5 handles structured classification tasks well. It doesn’t need multimodal awareness or creative generation. What matters here is speed, volume handling, and consistent output.
3. Product- or service-specific Q&A bots
If you want a chatbot that answers questions about your pricing, shipping policies, account settings, or service offerings, GPT-3.5 is good enough.
With proper prompting (or basic fine-tuning), it performs well in narrow domains where consistency and responsiveness are more valuable than advanced reasoning.
4. Content generation at scale (but not for high-stakes copy)
GPT-3.5 is well suited for producing large volumes of straightforward content such as product descriptions, onboarding messages, support templates, or internal documentation.
When speed and quantity matter more than creative flair or strategic nuance, it provides reliable and consistent output that can be easily reviewed and refined by human editors.
If you’re already invested in GPT-3.5, these are the areas where it can still hold its ground.
But if you’re starting fresh, it’s worth exploring the capabilities of newer models before making your decision. They might handle your task faster, better or cheaper.
We put them head-to-head so you don’t have to. Check out our comparison article: GPT-4o vs GPT-4 vs GPT-3.5.
Summary and Key Takeaways
GPT-3.5 is no longer the default recommendation for new generative AI projects — and it shouldn’t be. More powerful, cheaper models like GPT-4o-mini now offer better performance across most categories. But that doesn’t mean GPT-3.5 is obsolete.
It’s still actively supported by OpenAI, and in many scenarios, it’s more than good enough. Its predictable behavior, fine-tuning support, and low operational overhead make it a reliable choice for specific, well-scoped tasks, especially when you’re optimizing for stability and reliability rather than raw capability.
If you’re building an AI solution that:
- Doesn’t require multimodal inputs like images or audio,
- prioritizes speed and cost over cutting-edge reasoning,
- depends on consistent, reproducible outputs, or
- relies on a previously fine-tuned GPT-3.5 setup,
then GPT-3.5 still deserves a spot on your shortlist.
It’s not about chasing the most advanced model. It’s about choosing the one that actually fits the job.
