Multimodal AI systems process text, code, audio, image and video within one model instead of stitching together separate tools for each format. OpenAI's GPT-4o, announced May 13, 2024, trains a single neural network end-to-end across text, vision and audio, replacing a three-model voice pipeline that lost tone and speaker information. Google's Gemini, announced December 6, 2023, was pre-trained natively across modalities from the start. Both companies say the approach aids complex reasoning, though OpenAI initially limited GPT-4o's audio and video features to a small group of trusted API partners.
What Is Multimodal AI, and How Does It Differ From Traditional Pipelines?
Multimodal AI refers to a system built to generalize across and combine different types of information — including text, code, audio, image and video — rather than handling each format with a separate toolCITE:E1.
Google says it built Gemini "from the ground up to be multimodal," meaning the model can generalize and seamlessly understand, operate across, and combine text, code, audio, image and videoCITE:E1. Google made that design goal explicit in its official announcement dated December 6, 2023CITE:E1.
The contrast with a non-unified approach is concrete: before GPT-4o, OpenAI's Voice Mode ran as a pipeline of three separate models — one transcribed audio to text, GPT-3.5 or GPT-4 processed that text and produced text output, and a third model converted the text back into audioCITE:E2. OpenAI states that because the main source of intelligence in that pipeline only ever read text, it could not directly observe tone, multiple speakers, or background noise, and could not output laughter, singing, or express emotionCITE:E2. That gap is why a text-only, pipeline-based setup loses non-textual information that a model designed to be multimodal from the outset is meant to retain.
Why Build One End-to-End Model Instead of Chaining Multiple Models?
OpenAI trained GPT-4o as a single new model end-to-end across text, vision and audio, so one neural network processes every input and outputCITE:E5.
"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," OpenAI said in its May 13, 2024 announcementCITE:E5. This directly answers the information loss described in the earlier three-model voice pipeline: collapsing transcription, reasoning and synthesis into one system removes the intermediate step where tone and speaker information were previously discardedCITE:E2CITE:E5.
How Do GPT-4o and Gemini Represent Different Multimodal Approaches?
GPT-4o accepts any combination of text, audio, image and video as input and generates any combination of text, audio and image as output, while Gemini was pre-trained natively across modalities from the start and later fine-tunedCITE:E3CITE:E4.
| Model | Company | Input modalities | Output modalities | Design approach | Announced |
|---|
| GPT-4o | OpenAI | Text, audio, image, video | Text, audio, image | Single model trained end-to-end across text, vision, audioCITE:E5 | 2024-05-13CITE:E3 |
| Gemini | Google | Text, code, audio, image, video | Not specified in the cited evidence | Pre-trained natively on different modalities from the start, then fine-tuned with additional multimodal dataCITE:E4 | 2023-12-06CITE:E1 |
Notably, GPT-4o's output set does not include video generation, only text, audio and imageCITE:E3. Google describes its own approach in similar terms of built-in design: "We designed Gemini to be natively multimodal, pre-trained from the start on different modalities. Then we fine-tuned it with additional multimodal data to further refine its effectiveness," the company saidCITE:E4.
What Practical Advantage Does Multimodal Reasoning Provide?
Google says Gemini's multimodal reasoning capabilities can help make sense of complex written and visual informationCITE:E6.
In its December 6, 2023 announcement, Google stated: "Gemini 1.0's sophisticated multimodal reasoning capabilities can help make sense of complex written and visual information"CITE:E6. This framing links back to the model's native multimodal pretraining described above — the ability to combine text and visual inputs is presented as a direct outcome of that design choiceCITE:E4CITE:E6.
Why Is There a Gap Between Announced Capabilities and What's Actually Available?
OpenAI said at GPT-4o's launch that its new audio and video capabilities would go first to a small group of trusted partners in the API, not to all users immediatelyCITE:E7.
"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," OpenAI said in the same May 13, 2024 announcement that described the end-to-end unified modelCITE:E7. The statement was published alongside, not separately from, the architecture claim, meaning the single-model design and the limited rollout were disclosed togetherCITE:E5CITE:E7.
What This Means
Across the two companies' own statements, a consistent pattern emerges: both frame multimodality as something built into the model from the start rather than added afterward — Google through "natively multimodal" pretraining and OpenAI through a "single model end-to-end" trained across text, vision and audioCITE:E1CITE:E4CITE:E5. That architectural claim is distinct from availability: OpenAI's own announcement that introduced the unified model also disclosed that its audio and video capabilities would initially reach only a small group of trusted API partners, not the full user baseCITE:E7. Read together, the evidence supports a technical claim about how the models are built, but not a claim about how broadly their newest capabilities were accessible at launch.