# GPT-3.5 Turbo

GPT-3.5 Turbo brought ChatGPT-class conversational AI to the API for the first time, priced at a fraction of earlier GPT-3.5 variants.

- **Model ID:** `openai/gpt-3.5-turbo`
- **Type:** chat
- **Providers:** openai
- **Context window:** 16,385
- **Maximum output tokens:** 4,096
- **Pricing:** $0.5/1M input tokens, $1.5/1M output tokens
- **Canonical page:** https://vercel.com/ai-gateway/models/gpt-3.5-turbo

## Supported parameters

Detailed capability metadata has not been reported for this model.

## Example

```ts
import { streamText } from 'ai'

const result = streamText({
  model: 'openai/gpt-3.5-turbo',
  prompt: 'Why is the sky blue?'
})
```

## About

GPT-3.5 Turbo launched on March 1, 2023 alongside the Whisper API as OpenAI opened its ChatGPT capability to developers through a dedicated API endpoint. At introduction it cost substantially less than the existing GPT-3.5 models, making it the first model to put conversational AI within budget for production-scale products.

The model uses a conversational message format where developers supply a list of messages with roles (`system`, `user`, `assistant`) rather than a single prompt string. This chat-native interface made it straightforward to build multi-turn experiences, chatbots, and instruction-following assistants without extra prompt engineering to simulate turns.

Over subsequent months OpenAI expanded the model: a 16K context variant arrived to handle roughly 20 pages of text in one request, and the standard model received a price reduction together with improved function-calling capabilities and better steerability. It remains widely deployed for workloads where conversational throughput and cost efficiency outweigh the need for frontier reasoning depth.

## What to consider

If you run high message volumes and per-token cost is your dominant concern, GPT-3.5 Turbo remains one of the most economical options for straightforward conversational workloads.

## When to use

### Best For

- Customer support bots and FAQ systems where fast, on-topic replies matter more than complex reasoning
- Summarization pipelines that process large numbers of documents at low cost
- Draft generation for emails, support tickets, or templated content at volume
- Multi-turn chat interfaces in consumer or internal tools where latency and price sensitivity are high
- Lightweight classification or intent detection tasks embedded in larger automation pipelines

### Consider Alternatives When

- The task requires multi-step logical reasoning, math, or code generation where GPT-4-class accuracy is necessary
- Your prompt regularly exceeds the context window and you need the full 1M-token range of GPT-4.1
- You need native vision or audio input processing
- Instruction adherence on complex, structured tasks is critical and errors are costly

## Best for

- **Customer support bots:** FAQ systems where fast, on-topic replies matter more than complex reasoning
- **Summarization pipelines:** Processing large numbers of documents at low cost
- **Draft generation:** Emails, support tickets, or templated content at volume
- **Multi-turn chat:** Consumer or internal tools where latency and price sensitivity are high
- **Lightweight classification:** Intent detection tasks embedded in larger automation pipelines

## Consider alternatives

- **Advanced reasoning needed:** Multi-step logical reasoning, math, or code generation where GPT-4-class accuracy is necessary
- **Very long prompts:** Your prompt regularly exceeds the context window and you need the full 1M-token range of GPT-4.1
- **Multimodal input:** You need native vision or audio input processing
- **Strict instruction adherence:** Complex, structured tasks where errors are costly

## Frequently asked questions

### What API format does GPT-3.5 Turbo use?

It uses the Chat Completions API format. You send an array of messages with roles (`system`, `user`, `assistant`) rather than a raw completion prompt string.

### How does GPT-3.5 Turbo differ from GPT-3.5 Turbo Instruct?

GPT-3.5 Turbo is built for the Chat Completions endpoint and conversational multi-turn use. GPT-3.5 Turbo Instruct targets the legacy Completions endpoint and is optimized for single-turn instruction tasks using a prompt-response format.

### What context window does GPT-3.5 Turbo support?

The current model supports a context window of 16.4K tokens, suitable for multi-turn conversations and moderate-length document tasks.

### Is GPT-3.5 Turbo suitable for function calling?

Yes. OpenAI added function-calling support, enabling developers to define external functions the model can invoke, making it viable for agentic and tool-use workflows within its capability tier.

### How does AI Gateway handle authentication for GPT-3.5 Turbo?

AI Gateway accepts a single API key or OIDC token for all requests. You don't embed OpenAI credentials in your application; AI Gateway routes and authenticates on your behalf.

### Can I use GPT-3.5 Turbo for batch summarization pipelines?

Yes. Its combination of low per-token cost, fast response times, and context window of 16.4K tokens makes it well-suited for pipelines that process many documents in parallel.

### What are typical latency characteristics?

This page shows live throughput and time-to-first-token metrics measured across real AI Gateway traffic.

## Links

- [Model page](https://vercel.com/ai-gateway/models/gpt-3.5-turbo)
- [AI Gateway documentation](https://vercel.com/docs/ai-gateway)
- [Provider model documentation](https://platform.openai.com/docs/models/gpt-3-5)
- [Provider pricing](https://platform.openai.com/docs/pricing)


If you encounter incorrect information, can’t find what you need, need a capability Vercel doesn’t currently offer, or have any other feedback to report, please do it by following the instructions at https://vercel.com/feedback.md.