---
title: GPT-3.5 Turbo 16k — Long Context LLM | ModelsLab
description: Access OpenAI GPT-3.5 Turbo 16k API for 16k token context. Generate summaries of 20-page docs. Try this reliable alternative now.
url: https://modelslab.com/openai-gpt-35-turbo-16k
canonical: https://modelslab.com/openai-gpt-35-turbo-16k
type: website
component: Seo/ModelPage
generated_at: 2026-04-23T05:10:55.683829Z
---

Available now on ModelsLab · Language Model

OpenAI: GPT-3.5 Turbo 16k
16k Context Power
---

[Try OpenAI: GPT-3.5 Turbo 16k](/models/open_router/openai-gpt-3.5-turbo-16k) [API Documentation](https://docs.modelslab.com)

Handle Massive Contexts
---

16k Tokens

### Four Times Context

Process 20 pages or 12k+ words in one request with OpenAI: GPT-3.5 Turbo 16k.

Chat Optimized

### Turbo Chat Endpoint

Use Chat Completions API for natural language and code with openai gpt 3.5 turbo 16k.

Cost Effective

### Affordable Long Input

$0.003/1k input tokens for OpenAI: GPT-3.5 Turbo 16k alternative.

Examples

See what OpenAI: GPT-3.5 Turbo 16k can create
---

Copy any prompt below and try it yourself in the [playground](/models/open_router/openai-gpt-3.5-turbo-16k).

Doc Summary

“Summarize this 14-page technical report on machine learning algorithms, highlighting key methods, performance metrics, and future research directions. Extract main findings and provide a structured outline.”

Code Review

“Review this 10k token Python codebase for a web scraper. Identify bugs, suggest optimizations, and propose refactoring for better modularity and error handling.”

Essay Analysis

“Analyze this 15-page essay on climate change policy. Extract arguments, evidence, counterpoints, and rate overall persuasiveness on a 1-10 scale with justifications.”

Contract Parse

“Parse this 12k word legal contract. List all clauses, obligations, timelines, penalties, and flag ambiguous terms needing clarification.”

For Developers

A few lines of code.
16k chats. Chat endpoint.
---

ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.

- **Serverless:** scales to zero, scales to millions
- **Pay per token,** no minimums
- **Python and JavaScript SDKs,** plus REST API

[API Documentation ](https://docs.modelslab.com)

PythonJavaScriptcURL

Copy

```
<code>import requests

response = requests.post(
    "https://modelslab.com/api/v7/llm/chat/completions",
    json={
  "key": "YOUR_API_KEY",
  "prompt": "",
  "model_id": ""
}
)
print(response.json())</code>
```

FAQ

Common questions about OpenAI: GPT-3.5 Turbo 16k
---

[Read the docs ](https://docs.modelslab.com)

### What is OpenAI: GPT-3.5 Turbo 16k?

It is a chat-optimized model with 16k token context window, four times larger than standard GPT-3.5 Turbo. Supports ~20 pages of text per request. Uses Chat Completions API only.

### How does openai gpt 3.5 turbo 16k differ from GPT-3.5 Turbo?

Offers 16k input context vs 4k, at double the price. Chat-only, no completions endpoint. Ideal for long documents.

### Is OpenAI: GPT-3.5 Turbo 16k API deprecated?

Deprecated since 2023; newer gpt-3.5-turbo defaults to 16k context. Legacy access for existing users only. Use current models for consistency.

### What is pricing for OpenAI: GPT-3.5 Turbo 16k model?

$0.003 per 1k input tokens, $0.004 per 1k output. Twice the cost of base model for extended context.

### Can OpenAI: GPT-3.5 Turbo 16k alternative handle code?

Yes, generates and understands code via chat API. Optimized for natural language too. Reliable for most tasks except vision.

### Why use openai gpt 3.5 turbo 16k api?

For speed, affordability, and massive context in chats. Excels over GPT-4 in context size at lower cost. Great for summarization and analysis.

Ready to create?
---

Start generating with OpenAI: GPT-3.5 Turbo 16k on ModelsLab.

[Try OpenAI: GPT-3.5 Turbo 16k](/models/open_router/openai-gpt-3.5-turbo-16k) [API Documentation](https://docs.modelslab.com)

---

*This markdown version is optimized for AI agents and LLMs.*

**Links:**
- [Website](https://modelslab.com)
- [API Documentation](https://docs.modelslab.com)
- [Blog](https://modelslab.com/blog)

---
*Generated by ModelsLab - 2026-04-23*