惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

Microsoft Azure Blog
Microsoft Azure Blog
博客园_首页
Forbes - Security
Forbes - Security
WordPress大学
WordPress大学
P
Proofpoint News Feed
T
Threat Research - Cisco Blogs
L
LINUX DO - 热门话题
L
Lohrmann on Cybersecurity
Spread Privacy
Spread Privacy
D
Darknet – Hacking Tools, Hacker News & Cyber Security
大猫的无限游戏
大猫的无限游戏
博客园 - 三生石上(FineUI控件)
P
Privacy International News Feed
A
About on SuperTechFans
T
Tailwind CSS Blog
I
InfoQ
S
Securelist
云风的 BLOG
云风的 BLOG
罗磊的独立博客
Recent Announcements
Recent Announcements
T
The Exploit Database - CXSecurity.com
B
Blog RSS Feed
V
Visual Studio Blog
Know Your Adversary
Know Your Adversary
The GitHub Blog
The GitHub Blog
Jina AI
Jina AI
腾讯CDC
Cyberwarzone
Cyberwarzone
有赞技术团队
有赞技术团队
AWS News Blog
AWS News Blog
博客园 - 【当耐特】
cs.AI updates on arXiv.org
cs.AI updates on arXiv.org
F
Full Disclosure
S
Secure Thoughts
博客园 - 司徒正美
J
Java Code Geeks
Y
Y Combinator Blog
Google Online Security Blog
Google Online Security Blog
GbyAI
GbyAI
N
News and Events Feed by Topic
Help Net Security
Help Net Security
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
Project Zero
Project Zero
T
Tenable Blog
cs.CV updates on arXiv.org
cs.CV updates on arXiv.org
T
Tor Project blog
MyScale Blog
MyScale Blog
Scott Helme
Scott Helme
小众软件
小众软件
K
Kaspersky official blog

OpenAI News

Using custom GPTs ChatGPT for customer success teams Applications of AI at OpenAI Research with ChatGPT Analyzing data with ChatGPT Financial services Responsible and safe use of AI Writing with ChatGPT ChatGPT for research Creating images with ChatGPT Personalizing ChatGPT ChatGPT for finance teams Getting started with ChatGPT Working with files in ChatGPT ChatGPT for sales teams Prompting fundamentals ChatGPT for managers Using projects in ChatGPT ChatGPT for marketing teams Brainstorming with ChatGPT AI fundamentals ChatGPT for operations teams Healthcare Our response to the Axios developer tool compromise Using skills OpenAI Full Fan Mode Contest: Terms & Conditions CyberAgent moves faster with ChatGPT Enterprise and Codex The next phase of enterprise AI Introducing the Child Safety Blueprint Introducing the OpenAI Safety Fellowship Industrial policy for the Intelligence Age OpenAI acquires TBPN Codex now offers more flexible pricing for teams Gradient Labs gives every bank customer an AI account manager OpenAI raises $122 billion to accelerate the next phase of AI Helping disaster response teams turn AI into action across Asia STADLER reshapes knowledge work at a 230-year-old company Inside our approach to the Model Spec Introducing the OpenAI Safety Bug Bounty program Helping developers build safer AI experiences for teens Update on the OpenAI Foundation Powering Product Discovery in ChatGPT Creating with Sora Safely How we monitor internal coding agents for misalignment OpenAI to acquire Astral Introducing GPT-5.4 mini and nano OpenAI Japan announces Japan Teen Safety Blueprint to put teen safety first Equipping workers with insights about compensation Why Codex Security Doesn’t Include a SAST Report Designing AI agents to resist prompt injection From model to agent: Equipping the Responses API with a computer environment Rakuten fixes issues twice as fast with Codex Wayfair boosts catalog accuracy and support speed with OpenAI Improving instruction hierarchy in frontier LLMs New ways to learn math and science in ChatGPT OpenAI to acquire Promptfoo Codex Security: now in research preview How Descript engineers multilingual video dubbing at scale How Balyasny Asset Management built an AI research engine Reasoning models struggle to control their chains of thought, and that’s good Introducing GPT-5.4 GPT-5.4 Thinking System Card Ensuring AI use in education leads to opportunity VfL Wolfsburg turns ChatGPT into a club-wide capability OpenAI and NORAD team up to bring new magic to “NORAD Tracks Santa” Accenture and OpenAI accelerate enterprise AI success OpenAI takes an ownership stake in Thrive Holdings to accelerate enterprise AI adoption What to know about a recent Mixpanel security incident Expanding data residency access to business customers worldwide Our approach to mental health-related litigation Inside JetBrains—the company reshaping how the world writes code Introducing shopping research in ChatGPT How GPT-5 helped mathematician Ernest Ryu solve a 40-year-old open problem OpenAI and Foxconn collaborate to strengthen U.S. manufacturing across the AI supply chain Disrupting malicious uses of AI: June 2025 Creating websites in minutes with AI Website Builder Addendum to OpenAI o3 and o4-mini system card: OpenAI o3 Operator OpenAI Deutschland Shipping code faster with o3, o4-mini, and GPT-4.1 Introducing Stargate UAE New tools and features in the Responses API Introducing Codex Addendum to o3 and o4-mini system card: Codex AI powers Expedia’s marketing evolution Strengthening America’s AI leadership with the U.S. National Laboratories Introducing ChatGPT Gov Operator System Card Computer-Using Agent Introducing Operator Bertelsmann powers creativity and productivity with OpenAI Trading Inference-Time Compute for Adversarial Robustness Announcing The Stargate Project Stargate Infrastructure The power of personalized AI Delivering LLM-powered health solutions Increasing accuracy of pediatric visit notes Practices for Governing Agentic AI Systems Superalignment Fast Grants Weak-to-strong generalization Partnership with Axel Springer to deepen beneficial use of AI in journalism
GPT-4o mini: advancing cost-efficient intelligence
2024-07-18 · via OpenAI News
OpenAI

Introducing our most cost-efficient small model

OpenAI is committed to making intelligence as broadly accessible as possible. Today, we're announcing GPT‑4o mini, our most cost-efficient small model. We expect GPT‑4o mini will significantly expand the range of applications built with AI by making intelligence much more affordable. GPT‑4o mini scores 82% on MMLU and currently outperforms GPT‑41 on chat preferences in LMSYS leaderboard(opens in a new window). It is priced at 15 cents per million input tokens and 60 cents per million output tokens, an order of magnitude more affordable than previous frontier models and more than 60% cheaper than GPT‑3.5 Turbo.

GPT‑4o mini enables a broad range of tasks with its low cost and latency, such as applications that chain or parallelize multiple model calls (e.g., calling multiple APIs), pass a large volume of context to the model (e.g., full code base or conversation history), or interact with customers through fast, real-time text responses (e.g., customer support chatbots). 

Today, GPT‑4o mini supports text and vision in the API, with support for text, image, video and audio inputs and outputs coming in the future. The model has a context window of 128K tokens, supports up to 16K output tokens per request, and has knowledge up to October 2023. Thanks to the improved tokenizer shared with GPT‑4o, handling non-English text is now even more cost effective.

GPT‑4o mini surpasses GPT‑3.5 Turbo and other small models on academic benchmarks across both textual intelligence and multimodal reasoning, and supports the same range of languages as GPT‑4o. It also demonstrates strong performance in function calling, which can enable developers to build applications that fetch data or take actions with external systems, and improved long-context performance compared to GPT‑3.5 Turbo.

GPT‑4o mini has been evaluated across several key benchmarks2.

Reasoning tasks: GPT‑4o mini is better than other small models at reasoning tasks involving both text and vision, scoring 82.0% on MMLU, a textual intelligence and reasoning benchmark, as compared to 77.9% for Gemini Flash and 73.8% for Claude Haiku.

Math and coding proficiency: GPT‑4o mini excels in mathematical reasoning and coding tasks, outperforming previous small models on the market. On MGSM, measuring math reasoning, GPT‑4o mini scored 87.0%, compared to 75.5% for Gemini Flash and 71.7% for Claude Haiku. GPT‑4o mini scored 87.2% on HumanEval, which measures coding performance, compared to 71.5% for Gemini Flash and 75.9% for Claude Haiku.  

Multimodal reasoning: GPT‑4o mini also shows strong performance on MMMU, a multimodal reasoning eval, scoring 59.4% compared to 56.1% for Gemini Flash and 50.2% for Claude Haiku.

As part of our model development process, we worked with a handful of trusted partners to better understand the use cases and limitations of GPT‑4o mini. We partnered with companies like Ramp(opens in a new window) and Superhuman(opens in a new window) who found GPT‑4o mini to perform significantly better than GPT‑3.5 Turbo for tasks such as extracting structured data from receipt files or generating high quality email responses when provided with thread history.

Safety is built into our models from the beginning, and reinforced at every step of our development process. In pre-training, we filter out(opens in a new window) information that we do not want our models to learn from or output, such as hate speech, adult content, sites that primarily aggregate personal information, and spam. In post-training, we align the model’s behavior to our policies using techniques such as reinforcement learning with human feedback (RLHF) to improve the accuracy and reliability of the models’ responses.

GPT‑4o mini has the same safety mitigations built-in as GPT‑4o, which we carefully assessed using both automated and human evaluations according to our Preparedness Framework and in line with our voluntary commitments. More than 70 external experts in fields like social psychology and misinformation tested GPT‑4o to identify potential risks, which we have addressed and plan to share the details of in the forthcoming GPT‑4o system card and Preparedness scorecard. Insights from these expert evaluations have helped improve the safety of both GPT‑4o and GPT‑4o mini.

Building on these learnings, our teams also worked to improve the safety of GPT‑4o mini using new techniques informed by our research. GPT‑4o mini in the API is the first model to apply our instruction hierarchy(opens in a new window) method, which helps to improve the model’s ability to resist jailbreaks, prompt injections, and system prompt extractions. This makes the model’s responses more reliable and helps make it safer to use in applications at scale.

We’ll continue to monitor how GPT‑4o mini is being used and improve the model’s safety as we identify new risks.

GPT‑4o mini is now available as a text and vision model in the Assistants API, Chat Completions API, and Batch API. Developers pay 15 cents per 1M input tokens and 60 cents per 1M output tokens (roughly the equivalent of 2500 pages in a standard book). We plan to roll out fine-tuning for GPT‑4o mini in the coming days.

In ChatGPT, Free, Plus and Team users will be able to access GPT‑4o mini starting today, in place of GPT‑3.5. Enterprise users will also have access starting next week, in line with our mission to make the benefits of AI accessible to all.

Over the past few years, we’ve witnessed remarkable advancements in AI intelligence paired with substantial reductions in cost. For example, the cost per token of GPT‑4o mini has dropped by 99% since text-davinci-003, a less capable model introduced in 2022. We’re committed to continuing this trajectory of driving down costs while enhancing model capabilities.

We envision a future where models become seamlessly integrated in every app and on every website. GPT‑4o mini is paving the way for developers to build and scale powerful AI applications more efficiently and affordably. The future of AI is becoming more accessible, reliable, and embedded in our daily digital experiences, and we’re excited to continue to lead the way.