The world of mechanical engineering is at a crossroads. On one side, general AI models like GPT-known as LLMs (Large Language Models)-offer broad knowledge, fast responses, and accessibility. On the other side, a new generation of specialized AI models-LMMs (Large Mechanical Models)-is emerging. These models are purpose-built for engineers, with CAD understanding, standards compliance, and secure integration into design workflows.

So the question every engineering leader is asking today is: What type of AI truly fits the needs of mechanical engineers-LLMs or LMMs?

LLMs: Breadth Without Depth

Generalist LLMs such as GPT bring undeniable advantages:

• Speed and fluency – great for drafting reports, emails, or proposals.
  
  

• Breadth of knowledge – useful for brainstorming or exploring unfamiliar concepts.
  
  

• Accessibility – easy to use and deploy across an organization.
  
  

But as outlined in the LLMs vs LMMs White Paper (Aug 2025), their limitations are critical in engineering:

• Unreliable accuracy – answers may “sound correct” but lack validation.
  
  

• No CAD or PDM integration – workflows remain disconnected.
  
  

• No multimodality – LLMs cannot reason over geometry, materials, or physics.
  
  

• Data risk – sensitive IP can be exposed when using shared models.
  
  

LLMs are powerful general-purpose assistants-but they fall short as trusted engineering copilots.

LMMs: Depth, Context, and Security

LMMs (Large Mechanical Models) are a different story. Purpose-built for engineering, they deliver:

• Domain-specific accuracy – trained on engineering-grade datasets.
  
  

• Source transparency – answers backed by standards and references.
  
  

• CAD & workflow integration – context-aware design suggestions.
  
  

• Engineering multimodality – reasoning over 3D geometry, constraints, and physics.
  
  

• Enterprise-grade IP protection – ensuring proprietary designs stay secure.
  
  

Their limitations? Narrower scope. LMMs are optimized for mechanical engineering only. But in that scope, the value is unmatched .

Leo: The Pioneer of LMMs

Leo is the world’s first Large Mechanical Model built specifically for mechanical engineers. Unlike generic LLMs, Leo doesn’t just “understand text”-it reads CAD files, analyzes assemblies, and runs physics-based calculations.

And most importantly: Leo is already protected by multiple patents.

In recent announcements, the Leo team shared:

“Just like GPT takes words and turns them into stories, Leo takes mechanical parts and assembles them into systems that actually make sense.”

Leo’s patents cover its proprietary technology for automated CAD assembly generation. This means the model can not only answer questions or find parts-it can design entire systems automatically.

For example, an engineer will soon be able to say:
“Design a suspension system for the 500-kg car I’m working on, following our company’s guidelines.”
Leo will run the calculations, select the correct parts, and generate the full CAD assembly.

Today, Leo already saves engineers five hours per week on tedious searches and manual mates. Soon, it will be assembling manufacturable designs like motorcycle suspensions or medical implants in minutes.

Patents such as Computerized System and Method for 3D CAD Design Generation (US Patent No. 18/907,937) position Leo not just as a participant in the AI revolution, but as its pioneer in mechanical engineering.

LLM vs LMM: When to Use Each

The LLMs vs LMMs White Paper makes the recommendation clear:

• Use LLMs for brainstorming, proposals, and broad knowledge discovery.
  
  

• Use LMMs for engineering design, standards compliance, and critical decisions where accuracy and IP protection matter.
  
  

The future will not be one or the other-it will be both.

The Outlook: Human + AI Engineering

Looking ahead, the white paper highlights four trends for the next 3–5 years:

• Standardization – benchmarks to measure AI accuracy in engineering.
  
  

• Multimodality – integrating text, geometry, simulation, and IoT data.
  
  

• Enterprise adoption – LMMs moving from pilots to widespread deployment.
  
  

• Human + AI collaboration – engineers remain in control, AI accelerates iteration.
  
  

Conclusion

LLMs have changed the way we learn and communicate. But LMMs like Leo are changing the way engineers design and build.

With patents granted, CAD integration, and uncompromising IP security, Leo is setting a new standard for what AI can achieve in mechanical engineering.

For engineering leaders, the question isn’t “Should we adopt AI?” but rather: Which AI is right for each task?

Those who understand the difference between LLMs and LMMs-and adopt both strategically-will lead the next wave of innovation in product design.

Ready to Experience Leo AI?

👉 Book a demo and see how Leo can streamline your engineering work > https://bit.ly/3Jr9MdU

Want to Stay Ahead in AI for Mechanical Engineering?

👉 Join the MI Community - a global space where mechanical engineers discover new AI tools, share real-world workflows, and connect >  https://mi.community/