Model Cost Profile

OpenAI: GPT-3.5 Turbo (older v0613)

Developer: openai

Pricing updated Mar 11, 2026

Input rank: #244Output rank: #221

Live Pricing

Input: $1.00

Output: $2.00

Pricing via OpenRouter API ยท Last synced Mar 11, 2026

OpenAI's GPT-3.5 Turbo (older v0613) is designed for applications requiring natural language understanding and generation, making it suitable for chatbots, content creation, and customer support automation. With a context window of 4095 tokens, teams can efficiently manage longer conversations and complex prompts, enhancing user engagement and response accuracy. Pricing implications include an input cost of $1.00 per million tokens and an output cost of $2.00 per million tokens, which teams should consider when budgeting for high-volume usage scenarios.

๐Ÿ”ง Tool Calling๐Ÿ“‹ Structured Output

Context Window

4,095

Tokens

Input Price / 1M

$1.00

Prompt tokens

Output Price / 1M

$2.00

Completion tokens

Intelligence (MMLU)

Benchmark Pending

Massive Multitask Language Understanding

Price History

OpenAI: GPT-3.5 Turbo (older v0613) Pricing Trend

Input / 1M tokens0.0%Output / 1M tokens0.0%
Mar 7 โ€” Mar 11
$1.00$1.50$2.00Mar 7Mar 8Mar 9Mar 10Mar 11

Current Input / 1M

$1.00

Current Output / 1M

$2.00

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FAQ

Common pricing and benchmark questions for OpenAI: GPT-3.5 Turbo (older v0613).

How much does OpenAI: GPT-3.5 Turbo (older v0613) cost per 1M input tokens?

OpenAI: GPT-3.5 Turbo (older v0613) input pricing is $1.00 per 1M tokens based on the latest synced provider data.

How much does OpenAI: GPT-3.5 Turbo (older v0613) cost per 1M output tokens?

OpenAI: GPT-3.5 Turbo (older v0613) output pricing is $2.00 per 1M tokens based on the latest synced provider data.

What context window does OpenAI: GPT-3.5 Turbo (older v0613) support?

OpenAI: GPT-3.5 Turbo (older v0613) supports a context window of 4,095 tokens.

How can I compare OpenAI: GPT-3.5 Turbo (older v0613) with cheaper alternatives?

Use the comparison links on this page to open direct model-vs-model pricing and benchmark pages, then evaluate monthly spend projections for your workload.