GPT-4o To Reason Thru AV, Text In Real Time

GPT-4o (“o” for “omni”) is a step towards much more natural human-computer interaction. It can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time in a conversation;


FinTech BizNews Service

Mumbai, May 14, 2024: OpenAI has unveiled new AI model. OpenAI has released GPT-4o to all users for free. 

OpenAI has made a very big announcement: 

We’re announcing GPT-4o, our new flagship model that can reason across audio, vision ( AV), and text in real time.

GPT-4o (“o” for “omni”) is a step towards much more natural human-computer interaction—it accepts as input any combination of text, audio, and image and generates any combination of text, audio, and image outputs. It can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time (opens in a new window) in a conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models.

OpenAI is an AI research and deployment company. Its mission is to ensure that artificial general intelligence benefits all of humanity.

Model capabilities

Prior to GPT-4o, you could use Voice Mode to talk to ChatGPT with latencies of 2.8 seconds (GPT-3.5) and 5.4 seconds (GPT-4) on average. To achieve this, Voice Mode is a pipeline of three separate models: one simple model transcribes audio to text, GPT-3.5 or GPT-4 takes in text and outputs text, and a third simple model converts that text back to audio. This process means that the main source of intelligence, GPT-4, loses a lot of information—it can’t directly observe tone, multiple speakers, or background noises, and it can’t output laughter, singing, or express emotion.


With GPT-4o, we trained a single new model end-to-end across text, vision, and audio, meaning that all inputs and outputs are processed by the same neural network. Because GPT-4o is our first model combining all of these modalities, we are still just scratching the surface of exploring what the model can do and its limitations.

Model evaluations

As measured on traditional benchmarks, GPT-4o achieves GPT-4 Turbo-level performance on text, reasoning, and coding intelligence, while setting new high watermarks on multilingual, audio, and vision capabilities.


Improved Reasoning 

GPT-4o sets a new high-score of 88.7% on 0-shot COT MMLU (general knowledge questions). All these evals were gathered with our new simple evals(opens in a new window) library. In addition, on the traditional 5-shot no-CoT MMLU, GPT-4o sets a new high-score of 87.2%. (Note: Llama3 400b(opens in a new window) is still training)


Graph Test 2

Audio ASR performance - GPT-4o dramatically improves speech recognition performance over Whisper-v3 across all languages, particularly for lower-resourced languages.


gpt-40-08 light

Audio translation performance - GPT-4o sets a new state-of-the-art on speech translation and outperforms Whisper-v3 on the MLS benchmark.


M3Exam Zero-Shot Results

M3Exam - The M3Exam benchmark is both a multilingual and vision evaluation, consisting of multiple-choice questions from other countries’ standardized tests that sometimes include figures and diagrams. GPT-4o is stronger than GPT-4 on this benchmark across all languages. (We omit vision results for Swahili and Javanese, as there are only 5 or fewer vision questions for these languages.


Vision understanding evals

Vision understanding evals - GPT-4o achieves state-of-the-art performance on visual perception benchmarks. All vision evals are 0-shot, with MMMU, MathVista, and ChartQA as 0-shot CoT.


Language tokenization

These 20 languages were chosen as representative of the new tokenizer's compression across different language families


Gujarati 4.4x fewer tokens (from 145 to 33)

હેલો, મારું નામ જીપીટી-4o છે. હું એક નવા પ્રકારનું ભાષા મોડલ છું. તમને મળીને સારું લાગ્યું!


Telugu 3.5x fewer tokens (from 159 to 45)

నమస్కారము, నా పేరు జీపీటీ-4o. నేను ఒక్క కొత్త రకమైన భాషా మోడల్ ని. మిమ్మల్ని కలిసినందుకు సంతోషం!


Tamil 3.3x fewer tokens (from 116 to 35)

வணக்கம், என் பெயர் ஜிபிடி-4o. நான் ஒரு புதிய வகை மொழி மாடல். உங்களை சந்தித்ததில் மகிழ்ச்சி!


Marathi 2.9x fewer tokens (from 96 to 33)

नमस्कार, माझे नाव जीपीटी-4o आहे| मी एक नवीन प्रकारची भाषा मॉडेल आहे| तुम्हाला भेटून आनंद झाला!


Hindi 2.9x fewer tokens (from 90 to 31)

नमस्ते, मेरा नाम जीपीटी-4o है। मैं एक नए प्रकार का भाषा मॉडल हूँ। आपसे मिलकर अच्छा लगा!


Urdu 2.5x fewer tokens (from 82 to 33)

ہیلو، میرا نام جی پی ٹی-4o ہے۔ میں ایک نئے قسم کا زبان ماڈل ہوں، آپ سے مل کر اچھا لگا!


Arabic 2.0x fewer tokens (from 53 to 26)

مرحبًا، اسمي جي بي تي-4o. أنا نوع جديد من نموذج اللغة، سررت بلقائك!


Persian 1.9x fewer tokens (from 61 to 32)

سلام، اسم من جی پی تی-۴او است. من یک نوع جدیدی از مدل زبانی هستم، از ملاقات شما خوشبختم!


Russian 1.7x fewer tokens (from 39 to 23) 

Привет, меня зовут GPT-4o. Я — новая языковая модель, приятно познакомиться!


Korean 1.7x fewer tokens (from 45 to 27)

안녕하세요, 제 이름은 GPT-4o입니다. 저는 새로운 유형의 언어 모델입니다, 만나서 반갑습니다!


Vietnamese 1.5x fewer tokens (from 46 to 30)

Xin chào, tên tôi là GPT-4o. Tôi là một loại mô hình ngôn ngữ mới, rất vui được gặp bạn!


Chinese 1.4x fewer tokens (from 34 to 24)



Japanese 1.4x fewer tokens (from 37 to 26)



Turkish 1.3x fewer tokens (from 39 to 30)

Merhaba, benim adım GPT-4o. Ben yeni bir dil modeli türüyüm, tanıştığımıza memnun oldum!


Italian 1.2x fewer tokens (from 34 to 28)

Ciao, mi chiamo GPT-4o. Sono un nuovo tipo di modello linguistico, è un piacere conoscerti!


German 1.2x fewer tokens (from 34 to 29)

Hallo, mein Name is GPT-4o. Ich bin ein neues KI-Sprachmodell. Es ist schön, dich kennenzulernen.


Spanish 1.1x fewer tokens (from 29 to 26)

Hola, me llamo GPT-4o. Soy un nuevo tipo de modelo de lenguaje, ¡es un placer conocerte!


Portuguese 1.1x fewer tokens (from 30 to 27)

Olá, meu nome é GPT-4o. Sou um novo tipo de modelo de linguagem, é um prazer conhecê-lo!


French 1.1x fewer tokens (from 31 to 28)

Bonjour, je m'appelle GPT-4o. Je suis un nouveau type de modèle de langage, c'est un plaisir de vous rencontrer!


English 1.1x fewer tokens (from 27 to 24)

Hello, my name is GPT-4o. I'm a new type of language model, it's nice to meet you!


Model safety and limitations

GPT-4o has safety built-in by design across modalities, through techniques such as filtering training data and refining the model’s behavior through post-training. We have also created new safety systems to provide guardrails on voice outputs.


We’ve evaluated GPT-4o according to our Preparedness Framework and in line with our voluntary commitments. Our evaluations of cybersecurity, CBRN, persuasion, and model autonomy show that GPT-4o does not score above Medium risk in any of these categories. This assessment involved running a suite of automated and human evaluations throughout the model training process. We tested both pre-safety-mitigation and post-safety-mitigation versions of the model, using custom fine-tuning and prompts, to better elicit model capabilities.


GPT-4o has also undergone extensive external red teaming with 70+ external experts in domains such as social psychology, bias and fairness, and misinformation to identify risks that are introduced or amplified by the newly added modalities. We used these learnings to build out our safety interventions in order to improve the safety of interacting with GPT-4o. We will continue to mitigate new risks as they’re discovered.


We recognize that GPT-4o’s audio modalities present a variety of novel risks. Today we are publicly releasing text and image inputs and text outputs. Over the upcoming weeks and months, we’ll be working on the technical infrastructure, usability via post-training, and safety necessary to release the other modalities. For example, at launch, audio outputs will be limited to a selection of preset voices and will abide by our existing safety policies. We will share further details addressing the full range of GPT-4o’s modalities in the forthcoming system card.


Through our testing and iteration with the model, we have observed several limitations that exist across all of the model’s modalities, a few of which are illustrated below.

We would love feedback to help identify tasks where GPT-4 Turbo still outperforms GPT-4o, so we can continue to improve the model. 


Model availability

GPT-4o is our latest step in pushing the boundaries of deep learning, this time in the direction of practical usability. We spent a lot of effort over the last two years working on efficiency improvements at every layer of the stack. As a first fruit of this research, we’re able to make a GPT-4 level model available much more broadly. GPT-4o’s capabilities will be rolled out iteratively (with extended red team access starting today). 


GPT-4o’s text and image capabilities are starting to roll out today in ChatGPT. We are making GPT-4o available in the free tier, and to Plus users with up to 5x higher message limits. We'll roll out a new version of Voice Mode with GPT-4o in alpha within ChatGPT Plus in the coming weeks.

Developers can also now access GPT-4o in the API as a text and vision model. GPT-4o is 2x faster, half the price, and has 5x higher rate limits compared to GPT-4 Turbo. We plan to launch support for GPT-4o's new audio and video capabilities to a small group of trusted partners in the API in the coming weeks.



GPT-4o contributions


Pre-training leads

Aidan Clark, Alex Paino, Jacob Menick

Post-training leads

Liam Fedus, Luke Metz

Architecture leads

Clemens Winter, Lia Guy

Optimization leads

Sam Schoenholz, Daniel Levy

Long-context lead

Nitish Keskar

Pre-training Data leads

Alex Carney, Alex Paino, Ian Sohl, Qiming Yuan

Tokenizer lead

Reimar Leike

Human data leads

Arka Dhar, Brydon Eastman, Mia Glaese

Eval lead

Ben Sokolowsky

Data flywheel lead

Andrew Kondrich

Inference leads

Felipe Petroski Such, Henrique Ponde de Oliveira Pinto

Inference Productionzation lead

Henrique Ponde de Oliveira Pinto

Post-training infrastructure leads

Jiayi Weng, Randall Lin, Youlong Cheng

Pre-training organization lead

Nick Ryder

Pre-training program lead

Lauren Itow

Post-training organization leads

Barret Zoph, John Schulman

Post-training program lead

Mianna Chen

Core contributors

Adam Lerer, Adam P. Goucher, Adam Perelman, Akila Welihinda, Alec Radford, Alex Borzunov, Alex Carney, Alex Chow, Alex Paino, Alex Renzin, Alex Tachard Passos, Alexi Christakis, Ali Kamali, Allison Moyer, Allison Tam, Amin Tootoonchian, Ananya Kumar, Andrej Karpathy, Andrey Mishchenko, Andrew Cann, Andrew Kondrich, Andrew Tulloch, Angela Jiang, Antoine Pelisse, Anuj Gosalia, Avi Nayak, Avital Oliver, Behrooz Ghorbani, Ben Leimberger, Ben Wang, Blake Samic, Brian Guarraci, Brydon Eastman, Camillo Lugaresi, Chak Li, Charlotte Barette, Chelsea Voss, Chong Zhang, Chris Beaumont, Chris Hallacy, Chris Koch, Christian Gibson, Christopher Hesse, Colin Wei, Daniel Kappler, Daniel Levin, Daniel Levy, David Farhi, David Mely, David Sasaki, Dimitris Tsipras, Doug Li, Duc Phong Nguyen, Duncan Findlay, Edmund Wong, Ehsan Asdar, Elizabeth Proehl, Elizabeth Yang, Eric Peterson, Eric Sigler, Eugene Brevdo, Farzad Khorasani, Francis Zhang, Gene Oden, Geoff Salmon, Hadi Salman, Haiming Bao, Heather Schmidt, Hongyu Ren, Hyung Won Chung, Ian Kivlichan, Ian O'Connell, Ian Osband, Ilya Kostrikov, Ingmar Kanitscheider, Jacob Coxon, James Crooks, James Lennon, Jason Teplitz, Jason Wei, Jason Wolfe, Jay Chen, Jeff Harris, Jiayi Weng, Jie Tang, Joanne Jang, Jonathan Ward, Jonathan McKay, Jong Wook Kim, Josh Gross, Josh Kaplan, Joy Jiao, Joyce Lee, Juntang Zhang, Kai Fricke, Kavin Karthik, Daniel LevinKenny Hsu, Kiel Howe, Kyle Luther, Larry Kai, Lauren Itow, Leo Chen, Lia Guy, Lien Mamitsuka, Lilian Weng, Long Ouyang, Louis Feuvrier, Lukas Kondraciuk, Lyric Doshi, Mada Aflak, Maddie Simens, Madeleine Thompson, Marat Dukhan, Marvin Zhang, Mateusz Litwin, Max Johnson, Mayank Gupta, Mia Glaese, Michael Janner, Michael Petrov, Michael Wu, Michelle Fradin, Michelle Pokrass, Miguel Oom Temudo de Castro, Mikhail Pavlov, Minal Khan, Mo Bavarian, Natalia Gimelshein, Natalie Staudacher, Nick Stathas, Nik Tezak, Nithanth Kudige, Noel Bundick, Ofir Nachum, Oleg Boiko, Oleg Murk, Olivier Godement, Owen Campbell-Moore, Philip Pronin, Philippe Tillet, Rachel Lim, Rajan Troll, Randall Lin, Rapha gontijo lopes, Raul Puri, Reah Miyara, Reimar Leike, Renaud Gaubert, Reza Zamani, Rob Honsby, Rohit Ramchandani, Rory Carmichael, Ruslan Nigmatullin, Ryan Cheu, Scott Gray, Sean Grove, Sean Metzger, Shantanu Jain, Shengjia Zhao, Sherwin Wu, Shuaiqi (Tony) Xia, Sonia Phene, Spencer Papay, Steve Coffey, Steve Lee, Steve Lee, Stewart Hall, Suchir Balaji, Tal Broda, Tal Stramer, Tarun Gogineni, Ted Sanders, Thomas Cunninghman, Thomas Dimson, Thomas Raoux, Tianhao Zheng, Tina Kim, Todd Underwood, Tristan Heywood, Valerie Qi, Vinnie Monaco, Vlad Fomenko, Weiyi Zheng, Wenda Zhou, Wojciech Zaremba, Yash Patil, Yilei, Qian, Yongjik Kim, Youlong Cheng, Yuchen He, Yuchen Zhang, Yujia Jin, Yunxing Dai, Yury Malkov

*Contributors listed in alphabetized order

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