本文主要是介绍独家整理，OpenAI 57个模型(含旧模型)最新的价格及限速标准，同步官方更新，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
一、说明
OpenAI官方没有清晰地给出目前全部可用模型的价格，我整理收集了相关数据，制作了这个价格表。同时还根据不同的账号等级，给出了不同模型的限速标准，希望能给有需要的朋友起些作用。
二、在线版本（实时更新）
我也做了个在线版本，加了些辅助功能，查看起来很方便了。
这个版本会根据官方的变化，实时更新，有需要的朋友可以关注下。
最新最全 ChatGPT OpenAI 模型model价格及限速表，https://gptbill.lonlie.cn/pricinglimits.html
效果如下：
三、价格及限速表
不多说，直接上表格。
表格说明：
- 下表中Input、Output、Training的都是每1K tokens的费用，单位：美元
- 不同账号有对应的账号等级(Free、Tier 1、Tier 2、Tier 3、Tier 4、Tier 5)，模型的限速与账号等级挂钩
- RPM：每分钟的请求次数限制
- RPD：每天的请求次数限制
- TPM：每分钟的token量限制
- TPD：每天的token量限制
| 分类 | 模型 | 价格 | 速率限制(按账号等级划分) | |||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Input(1K tokens) | Output(1K tokens) | Training(1K tokens) | Free | Tier 1 | Tier 2 | Tier 3 | Tier 4 | Tier 5 | ||||||||||||||||||||
| RPM | RPD | TPM | TPD | RPM | RPD | TPM | TPD | RPM | RPD | TPM | TPD | RPM | RPD | TPM | TPD | RPM | RPD | TPM | TPD | RPM | RPD | TPM | TPD | |||||
| Chat | gpt-3.5-turbo | 0.0015 | 0.002 | - | 3 | 200 | 40000 | - | 500 | 10000 | 60000 | - | 3500 | - | 80000 | - | 5000 | - | 160000 | - | 10000 | - | 1000000 | - | 10000 | - | 2000000 | - |
| gpt-3.5-turbo-0301 | 0.0015 | 0.002 | - | 3 | 200 | 40000 | - | 500 | 10000 | 60000 | - | 3500 | - | 80000 | - | 5000 | - | 160000 | - | 10000 | - | 1000000 | - | 10000 | - | 2000000 | - | |
| gpt-3.5-turbo-0613 | 0.0015 | 0.002 | - | 3 | 200 | 40000 | - | 500 | 10000 | 60000 | - | 3500 | - | 80000 | - | 5000 | - | 160000 | - | 10000 | - | 1000000 | - | 10000 | - | 2000000 | - | |
| gpt-3.5-turbo-1106 | 0.001 | 0.002 | - | 3 | 200 | 40000 | - | 500 | 10000 | 60000 | - | 3500 | - | 80000 | - | 5000 | - | 160000 | - | 10000 | - | 1000000 | - | 10000 | - | 2000000 | - | |
| gpt-3.5-turbo-16k | 0.003 | 0.004 | - | 3 | 200 | 40000 | - | 500 | 10000 | 60000 | - | 3500 | - | 80000 | - | 5000 | - | 160000 | - | 10000 | - | 1000000 | - | 10000 | - | 2000000 | - | |
| gpt-3.5-turbo-16k-0613 | 0.003 | 0.004 | - | 3 | 200 | 40000 | - | 500 | 10000 | 60000 | - | 3500 | - | 80000 | - | 5000 | - | 160000 | - | 10000 | - | 1000000 | - | 10000 | - | 2000000 | - | |
| gpt-3.5-turbo-instruct | 0.0015 | 0.002 | - | 3 | 200 | 150000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | |
| gpt-3.5-turbo-instruct-0914 | 0.0015 | 0.002 | - | 3 | 200 | 150000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | |
| gpt-4 | 0.03 | 0.06 | - | x | x | x | x | 500 | 10000 | 10000 | - | 5000 | - | 40000 | - | 5000 | - | 80000 | - | 10000 | - | 300000 | - | 10000 | - | 300000 | - | |
| gpt-4-0314 | 0.03 | 0.06 | - | x | x | x | x | 500 | 10000 | 10000 | - | 5000 | - | 40000 | - | 5000 | - | 80000 | - | 10000 | - | 300000 | - | 10000 | - | 300000 | - | |
| gpt-4-0613 | 0.03 | 0.06 | - | x | x | x | x | 500 | 10000 | 10000 | - | 5000 | - | 40000 | - | 5000 | - | 80000 | - | 10000 | - | 300000 | - | 10000 | - | 300000 | - | |
| gpt-4-1106-preview | 0.01 | 0.03 | - | x | x | x | x | 500 | 10000 | 150000 | 500000 | 5000 | - | 300000 | 1500000 | 5000 | - | 300000 | 5000000 | 10000 | - | 450000 | - | 10000 | - | 600000 | - | |
| gpt-4-1106-vision-preview | 0.01 | 0.03 | x | x | x | x | 80 | 500 | 10000 | - | 100 | 1000 | 20000 | - | 120 | 1500 | 40000 | - | 150 | 2000 | 150000 | - | 200 | 10000 | 300000 | - | ||
| gpt-4-32k | 0.06 | 0.12 | - | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | |
| gpt-4-32k-0314 | 0.06 | 0.12 | - | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | |
| gpt-4-32k-0613 | 0.06 | 0.12 | - | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | |
| gpt-4-vision-preview | 0.01 | 0.03 | - | x | x | x | x | 80 | 500 | 10000 | - | 100 | 1000 | 20000 | - | 120 | 1500 | 40000 | - | 150 | 2000 | 150000 | - | 200 | 10000 | 300000 | - | |
| Image | dall-e-2(256x256) | 0.016/张 | - | - | 3 | 200 | - | - | 5 | - | - | - | 50 | - | - | - | 100 | - | - | - | 100 | - | - | - | 500 | - | - | - |
| dall-e-2(512x512) | 0.018/张 | - | - | 3 | 200 | - | - | 5 | - | - | - | 50 | - | - | - | 100 | - | - | - | 100 | - | - | - | 500 | - | - | - | |
| dall-e-2(1024x1024) | 0.02/张 | - | - | 3 | 200 | - | - | 5 | - | - | - | 50 | - | - | - | 100 | - | - | - | 100 | - | - | - | 500 | - | - | - | |
| dall-e-3(Standard、1024x1024) | 0.04/张 | - | - | 1 | 200 | - | - | 5 | - | - | - | 7 | - | - | - | 7 | - | - | - | 15 | - | - | - | 50 | - | - | - | |
| dall-e-3(Standard、1024x1792) | 0.08/张 | - | - | 1 | 200 | - | - | 5 | - | - | - | 7 | - | - | - | 7 | - | - | - | 15 | - | - | - | 50 | - | - | - | |
| dall-e-3(Standard、1792x1024) | 0.08/张 | - | - | 1 | 200 | - | - | 5 | - | - | - | 7 | - | - | - | 7 | - | - | - | 15 | - | - | - | 50 | - | - | - | |
| dall-e-3(HD、1024x1024) | 0.08/张 | - | - | 1 | 200 | - | - | 5 | - | - | - | 7 | - | - | - | 7 | - | - | - | 15 | - | - | - | 50 | - | - | - | |
| dall-e-3(HD、1024x1792) | 0.12/张 | - | - | 1 | 200 | - | - | 5 | - | - | - | 7 | - | - | - | 7 | - | - | - | 15 | - | - | - | 50 | - | - | - | |
| dall-e-3(HD、1792x1024) | 0.12/张 | - | - | 1 | 200 | - | - | 5 | - | - | - | 7 | - | - | - | 7 | - | - | - | 15 | - | - | - | 50 | - | - | - | |
| Audio | whisper-1 | $0.006/分钟 | - | - | 3 | 200 | - | - | 50 | - | - | - | 50 | - | - | - | 100 | - | - | - | 100 | - | - | - | 500 | - | - | - |
| tts-1 | 0.015 | - | - | 3 | 200 | 150000 | - | 50 | - | - | - | 50 | - | - | - | 100 | - | - | - | 100 | - | - | - | 500 | - | - | - | |
| tts-1-1106 | 0.015 | - | - | 3 | 200 | 150000 | - | 50 | - | - | - | 50 | - | - | - | 100 | - | - | - | 100 | - | - | - | 500 | - | - | - | |
| tts-1-hd | 0.03 | - | - | 3 | 200 | 150000 | - | 3 | - | - | - | 5 | - | - | - | 7 | - | - | - | 10 | - | - | - | 20 | - | - | - | |
| tts-1-hd-1106 | 0.03 | - | - | 3 | 200 | 150000 | - | 3 | - | - | - | 5 | - | - | - | 7 | - | - | - | 10 | - | - | - | 20 | - | - | - | |
| FineTuning | babbage-002 | 0.0016 | 0.0016 | 0.0004 | 3 | - | 150000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - |
| davinci-002 | 0.012 | 0.012 | 0.006 | 3 | - | 150000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | |
| gpt-3.5-turbo-0613 | 0.003 | 0.006 | 0.008 | 3 | - | 40000 | - | 500 | - | 60000 | - | 500 | - | 60000 | - | 5000 | - | 160000 | - | 5000 | - | 160000 | - | 5000 | - | 160000 | - | |
| gpt-3.5-turbo-1106 | 0.003 | 0.006 | 0.008 | 3 | - | 40000 | - | 500 | - | 60000 | - | 500 | - | 60000 | - | 5000 | - | 160000 | - | 5000 | - | 160000 | - | 5000 | - | 160000 | - | |
| Text | ada | 0.0004 | 0.0004 | - | 3 | 200 | 150000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - |
| babbage | 0.0005 | 0.0005 | - | 3 | 200 | 150000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | |
| babbage-002 | 0.0004 | 0.0004 | - | 3 | 200 | 150000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | |
| code-search-ada-code-001 | 0.004 | 0.004 | - | 3 | 200 | 150000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | |
| code-search-ada-text-001 | 0.004 | 0.004 | - | 3 | 200 | 150000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | |
| code-search-babbage-code-001 | 0.005 | 0.005 | - | 3 | 200 | 150000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | |
| code-search-babbage-text-001 | 0.005 | 0.005 | - | 3 | 200 | 150000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | |
| curie | 0.002 | 0.002 | - | 3 | 200 | 150000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | |
| davinci | 0.02 | 0.02 | - | 3 | 200 | 150000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | |
| davinci-002 | 0.002 | 0.002 | - | 3 | 200 | 150000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | |
| text-search-ada-doc-001 | 0.004 | 0.004 | - | 3 | 200 | 150000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | |
| text-search-ada-query-001 | 0.004 | 0.004 | - | 3 | 200 | 150000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | |
| text-search-babbage-doc-001 | 0.005 | 0.005 | - | 3 | 200 | 150000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | |
| text-search-babbage-query-001 | 0.005 | 0.005 | - | 3 | 200 | 150000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | |
| text-search-curie-doc-001 | 0.02 | 0.02 | - | 3 | 200 | 150000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | |
| text-search-curie-query-001 | 0.02 | 0.02 | - | 3 | 200 | 150000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | |
| text-search-davinci-doc-001 | 0.2 | 0.2 | - | 3 | 200 | 150000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | |
| text-search-davinci-query-001 | 0.2 | 0.2 | - | 3 | 200 | 150000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | |
| text-similarity-ada-001 | 0.004 | 0.004 | - | 3 | 200 | 150000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | |
| text-similarity-babbage-001 | 0.005 | 0.005 | - | 3 | 200 | 150000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | |
| text-similarity-curie-001 | 0.02 | 0.02 | - | 3 | 200 | 150000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | |
| text-similarity-davinci-001 | 0.2 | 0.2 | - | 3 | 200 | 150000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - | 3000 | - | 250000 | - |
这篇关于独家整理，OpenAI 57个模型(含旧模型)最新的价格及限速标准，同步官方更新的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
