OGPT-3.5 Turbo InstructAPI

This model is a variant of GPT-3.5 Turbo tuned for instructional prompts and omitting chat-related optimizations.

4KContext window · tokens

$1.5Input price · per M tokens

$2Output price · per M tokens

OpenAIProvider

Prices update automatically — checked daily against provider list prices.

$1.63 per 1M tokens, assuming 3 input for 1 output the convention used by Artificial Analysis
cheaper than 57% of the 192 commercial models tracked here
30 of 88 OpenAI models tracked here, from cheapest

See open-source alternatives → Compare all model prices

Benchmarks & performance

Independent benchmark scores for GPT-3.5 Turbo Instruct, measured by Artificial Analysis. Higher is better.

Intelligence index3.2

Coding index10.7

GPQA29.7%

MMLU-Pro46.2%

MATH-50044.1%

💰 Blended price$0.75 / 1M tokens

📈 Value4.3 intelligence points per $

vs. models measured heretop 99%

Scores higher than 1% of the 273 models measured by Artificial Analysis and tracked here.
Models at this level cost $0.5 per 1M tokens (median of 10) — this one costs $1.63.
Cheaper and better on this index: GLM 5.3 Flash · Gemini 3.7 Flash · Gemini 3.7 Flash (batch) and 154 more

Benchmark data by Artificial Analysis

About this model

GPT-3.5 Turbo Instruct is a commercial AI model by OpenAI. The specifications below are tracked automatically: pricing is refreshed daily from public list prices, so the numbers on this page reflect the current cost of using the model through its API.

Frequently asked questions

What is GPT-3.5 Turbo Instruct?

GPT-3.5 Turbo Instruct is an AI language model from OpenAI. It is a proprietary model, available through an API. It scores 3.2 on the Artificial Analysis intelligence index.

Is GPT-3.5 Turbo Instruct free?

GPT-3.5 Turbo Instruct is not free: it costs $1.5 per million input tokens and $2 per million output tokens. Open-weight alternatives can be self-hosted at no per-token cost.

What is GPT-3.5 Turbo Instruct good at?

Independent benchmarks from Artificial Analysis give it GPQA 29.7%, MMLU-Pro 46.2%, MATH-500 44.1%. It is particularly used for code generation.