AI models becoming multimodal
1. OpenAI’s GPT-5 — The Benchmark of Intelligence OpenAI’s GPT-5 is widely seen as the flagship of large language models (LLMs). It’s a massive leap from GPT-4 — faster, sharper, and deeply context-aware. What is hybrid reasoning architecture that is strong in GPT-5 is that it is able to combine neRead more
1. OpenAI’s GPT-5 — The Benchmark of Intelligence
OpenAI’s GPT-5 is widely seen as the flagship of large language models (LLMs). It’s a massive leap from GPT-4 — faster, sharper, and deeply context-aware.
What is hybrid reasoning architecture that is strong in GPT-5 is that it is able to combine neural creativity (narrating, brain-storming) with symbolic logic (structured reasoning, math, coding). It also has multi-turn memory, i.e., it remembers things from long conversations and adapts to user tone and style.
What it is capable of:
- Write or code entire computer programs
- Parse papers or research papers in numerous languages
- Understand and generate images, charts, diagrams
- Talk to real-world applications with autonomous “AI agents”
GPT-5 is not only a text model — it’s turning into a digital co-worker who can build your tastes, assist workflows, and even start projects.
2. Anthropic Claude 3.5 — The Empathic Thinker
Anthropic’s Claude 3.5 family is famous for ethics-driven alignment and human-like conversation. Claude responds in a voice that feels serene, emotionally smart, and thoughtful — built to avoid bias and misinformation.
What the users love most is the way Claude “thinks out loud”: it exposes its thought process, so users believe in its conclusions.
Strengths in its core:
- Fantastic grasp of long, complicated texts (over 200K tokens)
- Very subtle summarizing and research synthesis
- Emotionally intelligent voice highly suitable for education, therapy, and HR use
Claude 3.5 has made itself the “teacher” of AI models — intelligent, patient, and thoughtful.
3. Google DeepMind Gemini 2 — The Multimodal Genius
Google’s Gemini 2 (and Pro) is the future of multimodal AI. Trained on text, video, audio, and code, Gemini can look at a video, summarize it, explain what’s going on, and even offer suggestions for editing — all at once.
It also works perfectly within Google’s ecosystem, driving YouTube analysis, Google Workspace, and Android AI assistants.
Key features:
- Real-time visual reasoning and voice comprehension
- Integrated search and citation capabilities for accuracy of fact-checking
- High-order math and programming strength through AlphaCode 3 foundation
Gemini 2 breaks the barrier between search engine and thinking friend, arguably the most general-purpose model ever developed.
4. Mistral Large — The Open-Source Giant
Among open-source configurations, Mistral is the rockstar of today. Its Mistral Large model competes against closed-shop behemoths like GPT-5 in reason and speed but is open-source to be extended by developers.
This openness has forced innovation for startups and research institutions that cannot afford the cost of Big Tech’s closed APIs.
Why it matters:
- Open weights enable transparency and customization
- Lean and efficient — fits on local hardware
- Used extensively all over Europe for sovereign data AI initiatives
Mistral’s philosophy is simple: exchange intelligence, not behind corporate paywalls.
5. Meta LLaMA 3 — Researcher Favorite
Meta’s LLaMA 3 series (especially the 70B and 400B versions) has revolutionized open-source AI. It is heavily fine-tuned, so organizations can fine-tune private versions on their data.
Much of the next-generation AI assistants and agents are developed on top of LLaMA 3 due to its scalability and open licensing.
Standout features:
- Better multilingual performance
- Efficient reasoning and code generation
- Huge open ecosystem sustained by Meta’s developer community
LLaMA 3 symbolizes the democratization of intelligence — showing that open models can compete with giants.
6. xAI’s Grok 3 — The Real-Time Social AI
Elon Musk’s xAI is building up Grok further, now owned by X (formerly Twitter). Grok 3 can consume real-time streams of information and deliver responses with instant knowledge of news articles, social causes, and cultural phenomena.
Less scholarly oriented than GPT-5 or Claude, the strength of Grok is the immediacy aspect — one of the rare AIs linked to the constantly moving heart of the internet.
Why it excels:
- Real-time access to the X platform
- Brave, talkative nature
- Xiexiexie for content creation, trending, and online conversation
7. Yi Large & Qwen 2 — Asia’s AI Young Talents
China has revolutionized AI with models like Yi Large (by 01.AI) and Qwen 2 (by Alibaba). They are multimodal and multilingual, and trained on immense differences in culture and language.
They are revolutionizing the face of the Asian AI market by facilitating native language processing for Mandarin, Hindi, Japanese, and beyond.
Why they matter:
- Conquering world language barriers
- Enabling easier local application of AI
- Competition on a global level with efficiency and affordability
The Bigger Picture: Collaboration, Not Competition
Competition to develop the most powerful AI is not dumb brute strength — it is all about trust, usability, and availability.
Each model brings something different to the table:
- GPT-5: reason and imagination
- Claude 3.5: morals and empathy
- Gemini 2: fact-checking anchorage and multimodality
- Mistral/LLaMA: open-mindedness and adaptability
Strength is not in a single model, but how they support and complement one another — building an ecosystem for AI whereby human beings are able to work with intelligence, not against it.
Last Thought
It’s not even “Which is the strongest model?” by 2025, but “Which model frees humans most?”
From writers and teachers to doctors and writers, these AI applications are becoming partners of progress, not just drivers of automation.
The greatest AI, ultimately, is one that makes us think harder, work smarter, and be human.
1. What Does "Multimodal" Actually Mean? "Multimodal AI" is just a fancy way of saying that the model is designed to handle lots of different kinds of input and output. You could, for instance: Upload a photo of a broken engine and say, "What's going on here?" Send an audio message and have it tranRead more
1. What Does “Multimodal” Actually Mean?
“Multimodal AI” is just a fancy way of saying that the model is designed to handle lots of different kinds of input and output.
You could, for instance:
It’s almost like AI developed new “senses,” so it could visually perceive, hear, and speak instead of reading.
2. How Did We Get Here?
The path to multimodality started when scientists understood that human intelligence is not textual — humans experience the world in image, sound, and feeling. Then, engineers began to train artificial intelligence on hybrid datasets — images with text, video with subtitles, audio clips with captions.
Neural networks have developed over time to:
These advances resulted in models that translate the world as a whole in, non-linguistic fashion.
3. The Magic Under the Hood — How Multimodal Models Work
It’s centered around something known as a shared embedding space.
Conceptualize it as an enormous mental canvas surface upon which words and pictures, and sounds all co-reside in the same space of meaning.
This is basically how it works in a grossly oversimplified nutshell:
So when you tell it, “Describe what’s going on in this video,” the model puts together:
That’s what AI does: deep, context-sensitive understanding across modes.
4. Multimodal AI Applications in the Real World in 2025
Now, multimodal AI is all around us — transforming life in quiet ways.
a. Learning
Students watch video lectures, and AI automatically summarizes lectures, highlights key points, and even creates quizzes. Teachers utilize it to build interactive multimedia learning environments.
b. Medicine
Physicians can input medical scans, lab work, and patient history into a single system. The AI cross-matches all of it to help make diagnoses — catching what human doctors may miss.
c. Work and Productivity
You have a meeting and AI provides a transcript, highlights key decisions, and suggests follow-up emails — all from sound, text, and context.
d. Creativity and Design
Multimodal AI is employed by marketers and artists to generate campaign imagery from text inputs, animate them, and even write music — all based on one idea.
e. Accessibility
For visually and hearing impaired individuals, multimodal AI will read images out or translate speech into text in real-time — bridging communication gaps.
5. Top Multimodal Models of 2025
Model Modalities Supported Unique Strengths:
GPT-5 (OpenAI)Text, image, soundDeep reasoning with image & sound processing. Gemini 2 (Google DeepMind)Text, image, video, code. Real-time video insight, together with YouTube & WorkspaceClaude 3.5 (Anthropic)Text, imageEmpathetic contextual and ethical multimodal reasoningMistral Large + Vision Add-ons. Text, image. ixa. Open-source multimodal business capability LLaMA 3 + SeamlessM4TText, image, speechSpeech translation and understanding in multiple languages
These models aren’t observing things happen — they’re making things happen. An input such as “Design a future city and tell its history” would now produce both the image and the words, simultaneously in harmony.
6. Why Multimodality Feels So Human
When you communicate with a multimodal AI, it’s no longer writing in a box. You can tell, show, and hear. The dialogue is richer, more realistic — like describing something to your friend who understands you.
That’s what’s changing the AI experience from being interacted with to being collaborated with.
You’re not providing instructions — you’re co-creating.
7. The Challenges: Why It’s Still Hard
Despite the progress, multimodal AI has its downsides:
Researchers are working day and night to develop transparent reasoning and edge processing (executing AI on devices themselves) to circumvent8. The Future: AI That “Perceives” Like Us
AI will be well on its way to real-time multimodal interaction by the end of 2025 — picture your assistant scanning your space with smart glasses, hearing your tone of voice, and reacting to what it senses.
Multimodal AI will more and more:
In effect, AI is no longer so much a text reader but rather a perceiver of the world.
Final Thought
The more senses that AI can learn from, the more human it will become — not replacing us, but complementing what we can do, learn, create, and connect.
Over the next few years, “show, don’t tell” will not only be a rule of storytelling, but how we’re going to talk to AI itself.
See less