GPT-3.5 Turbo 16k
OpenAI
At a glance
- Price per 1M tokens (input)
- $3.00
- Price per 1M tokens (output)
- $4.00
- Context
- 16,385 tokens
- Free access
- yes (see below)
Refreshed daily; data verified August 16, 2026. Published August 12, 2026.
The GPT-3.5 Turbo 16k model by OpenAI is designed for businesses and organizations that require advanced text generation capabilities. This model is well-suited for tasks such as content creation, language translation, and text summarization. What sets this model apart is its ability to understand and generate human-like language, making it a valuable tool for applications where natural language processing is critical. The vendor's approach focuses on providing a robust and flexible model that can be fine-tuned for specific use cases, allowing businesses to tailor the model to their unique needs. By leveraging this model, businesses can automate tasks, improve efficiency, and enhance customer engagement.
Specifications & pricing
| Input (per 1M tokens) | $3.00 |
|---|---|
| Output (per 1M tokens) | $4.00 |
| Context window | 16,385 tokens |
| Max output | 4,096 tokens |
| Capabilities | text |
LiteLLM community dataset (MIT), verified August 16, 2026. Official OpenAI pricing.
What GPT-3.5 Turbo 16k would cost on your workload — run it through the cost calculator →
Where to try GPT-3.5 Turbo 16k for free
- OpenAI offers a free chat — ChatGPT (free plan). A vendor's free chat may run a different model from the same family — the exact model is not guaranteed.
Frequently asked questions
What is this model good for+
This model is good for tasks that require advanced text generation, such as content creation, language translation, and text summarization, where the goal is to produce human-like language
How do I get started with this model+
To get started, you will need to integrate the model into your application or workflow, which may involve tool calling or using a software development kit, and then fine-tune the model for your specific use case
How does this model differ from other models in the line+
This model differs from other models in the line in its ability to understand and generate human-like language, making it a valuable tool for applications where natural language processing is critical, and its flexibility to be fine-tuned for specific use cases
What are the limitations of this model+
The limitations of this model include its potential to generate inconsistent or inaccurate text, and its reliance on high-quality training data to produce optimal results
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