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GPT-3.5 Turbo Instruct

GPT-3.5 Turbo Instruct is an instruction-tuned completion model designed for the legacy Completions endpoint, offering a direct prompt-in, text-out format suited to few-shot tasks, templated generation, and workflows that predate the chat message structure.

index.ts
import { streamText } from 'ai'


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

  • What is the key difference between GPT-3.5 Turbo and GPT-3.5 Turbo Instruct?

    GPT-3.5 Turbo uses the Chat Completions endpoint with a messages array. GPT-3.5 Turbo Instruct uses the legacy Completions endpoint with a single prompt string, a fundamental structural difference that affects how you construct requests.

  • Does GPT-3.5 Turbo Instruct support function calling or JSON mode?

    No. Function calling and JSON mode are features of the Chat Completions API. GPT-3.5 Turbo Instruct targets the Completions endpoint and doesn't support these capabilities.

  • When would I choose the Completions format over Chat Completions?

    Use it when your prompt structure works best as a single string with few-shot examples inline, when you're maintaining an existing integration, or when a direct prompt-response contract is semantically simpler than a roles-based message list.

  • Is GPT-3.5 Turbo Instruct suitable for code completion features?

    Yes. Single-turn code completion and fill-in-the-middle tasks map naturally to the Completions format, and the model's instruction tuning makes it responsive to explicit directives within the prompt.

  • How do I access GPT-3.5 Turbo Instruct through AI Gateway?

    Authenticate with an AI Gateway API key or OIDC token and route requests to the AI Gateway endpoint specifying this model's slug. No direct OpenAI credentials are required in your application.

  • Can GPT-3.5 Turbo Instruct be used for multi-turn conversations?

    Technically you can simulate turns by concatenating prior exchanges into a single prompt string, but the chat-format models handle multi-turn context more naturally and efficiently.

  • What are typical latency characteristics?

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