For decades, Maple has been built around one of the world’s most powerful mathematics engines—helping students, educators, engineers, and researchers explore ideas, solve complex problems, and communicate mathematics clearly.

Maple 2026 builds on that foundation with major advances in the math engine, expanding the kinds of problems Maple can solve while improving reliability and performance.

At the same time, Maple 2026 introduces new AI-powered tools that help you work faster—finding commands, generating visualizations, explaining concepts, and helping you explore ideas. The key difference is that these tools sit on top of Maple’s math engine, so the results are grounded in real computation rather than guesswork.

If you’ve been following along with our recent Mathy teaser videos and sneak peek posts, you may already have seen hints of some of these features. Now I’m excited to finally share them in full.

One of the most exciting additions in Maple 2026 is the new AI Assistant.

AI tools are incredibly useful for exploring ideas, writing code, and learning new topics. But when the mathematics becomes more involved, relying on AI alone can be risky. The Maple AI Assistant brings those productivity benefits into Maple while keeping the mathematics grounded in Maple’s trusted computation engine.

You can ask the AI Assistant questions in natural language and have it help you:

• find Maple commands or formulas
• generate Maple code
• create visualizations
• explain mathematical concepts
• draft examples, worksheets, or reports

Because Maple performs the underlying computations where appropriate, the results are grounded in Maple’s powerful math engine. The AI Assistant becomes a productivity partner that helps you accomplish tasks in Maple faster and more easily, combining the flexibility of AI with mathematics you can trust.

Watch the AI Assistant in action.

Another feature I’m particularly excited about is Document Import.

Many of us have years of mathematical content stored in PDFs, lecture notes, journal articles, slides, or even handwritten pages. Traditionally these documents are static—you can read them, but you can’t interact with the mathematics inside them.

With Maple 2026, that changes.

Document Import allows Maple to convert many document formats—including PDFs, DOCX files, and presentations—into Maple worksheets where the mathematics becomes live and executable. 

The image below illustrates the transformation.

On the left (“Before”), scribbled handwritten notes from a Calculus III lecture were saved in a Word document. The notes include hand-drawn sketches, formulas, and written explanations.

After importing the document into Maple (“After”), the mathematical expressions were recognized and converted into live, editable Maple mathematics. The text was preserved, and the hand-drawn sketches were retained as images. The resulting worksheet supports evaluation, editing, and further computation.

Once imported, you can:

• evaluate expressions
• modify formulas
• extend derivations
• add visualizations
• explore variations of the mathematics

Instead of recreating examples from scratch, you can bring existing material directly into Maple and start exploring.

While the new AI features are exciting, the heart of Maple has always been its mathematics engine—and Maple 2026 delivers significant advances here.

One particularly notable improvement is Maple’s expanded ability to solve linear recurrence equations. Through improvements to the rsolve command and major extensions to the LREtools package, Maple can now solve dramatically more recurrence relations than before, including many third- and fourth-order cases that were previously beyond reach.

In fact, Maple can now fully solve over 94% of the 55,979 entries in the Online Encyclopedia of Integer Sequences (OEIS) that that can be shown to satisfy a linear recurrence relation. These advances reflect ongoing research into linear difference equations and their algorithmic implementation in Maple, continuing Maple’s long tradition of advancing the state of computer algebra.

Beyond recurrence solving, Maple 2026 includes many improvements across its core symbolic and numeric algorithms. Maple’s assumption system has been strengthened to improve reasoning under mathematical assumptions, and enhancements to the simplify, combine, and evalc commands allow Maple to produce more compact and mathematically natural forms for a wider range of expressions.

There are also improvements to Maple’s differential equation solvers, polynomial system solving, and numerical solving routines such as fsolve, along with updates to other foundational parts of the math library used throughout the system.

Taken together, these improvements expand the range of problems Maple can solve and improve the robustness, correctness, and efficiency of the results.

Maple has always offered extensive control over plotting options, but achieving consistent visual styling across multiple plots could require specifying many settings each time.

Maple 2026 introduces Plotting Themes, which allow you to define a plotting style once and apply it across many plots with a single option.

Themes make it easy to maintain consistent visual styles in worksheets, teaching materials, reports, and publications, while still allowing individual plots to override specific options when needed.

The image below shows an example of creating and applying a custom plotting theme. 

Maple continues to be widely used in classrooms around the world, and Maple 2026 includes several improvements designed to support teaching and learning.

The Check My Work system has been enhanced so Maple can recognize a wider variety of valid student solution steps and provide more accurate feedback.

Maple 2026 also improves the generation of similar practice problems, making it easier to create variations of a problem while preserving its mathematical structure.

In addition, Maple’s step-by-step solutions have been expanded to support more types of expressions, helping students better understand the reasoning behind the mathematics they’re learning.

Maple 2026 also introduces improvements for developers building advanced applications, along with performance enhancements across the system.

One particularly interesting addition is the new VectorSearch package, which implements a vector database directly inside Maple.

If you’re not familiar with vector databases, one way to think about them is through recommendation systems like Netflix or Spotify. Each movie or song can be represented by a vector containing thousands of numbers describing its characteristics—things like genre, pacing, or mood. When you watch something, the system finds other items whose vectors are closest to it, which is how recommendations are generated.

With the new VectorSearch package, Maple can store thousands (or more) of vectors and efficiently find the ones most similar to a given vector. This makes it easier to build applications involving machine learning, data analysis, and modern AI workflows directly in Maple.

Maple 2026 also delivers significant performance improvements. For example, operations involving quantities with units have been greatly optimized—some computations now run over 90 times faster, making Maple even more efficient for engineering and scientific workflows.

Maple 2026 also expands the benefits available through the Maplesoft Elite Maintenance Program (EMP). The new benefits include access to additional Maplesoft products and services:

• Maple Learn, the online environment for teaching and learning mathematics
• Maple Calculator Premium, bringing the power of Maple to your phone with full access to features like Solution Steps and Check My Work
• Maple MCP, which allows you to connect Maple’s math engine to external AI tools so they can produce mathematical results you can trust

These additions extend Maple beyond the desktop, giving users powerful tools for learning, teaching, and exploring mathematics across web and mobile platforms, as well as through integrations with external AI tools.

This post only scratches the surface of what’s new in Maple 2026. There are many more improvements across the math library, programming tools, and performance.

To learn more about all the new features and enhancements in Maple 2026, visit the What’s New in Maple page on our website.

If one of our posts showed up in your social media feed recently, you may have found yourself staring at a giant maple leaf with feet and thinking, “Wait… who (or what) is that?” you’re not alone. 

Yes, that big, cheerful leaf you’ve been seeing is very real. 
And yes, they have a name. 

Meet Mathy. 

We officially introduced Mathy to the world a couple of weeks ago at JMM 2026 in Washington, DC, but their story actually started much earlier. 

Mathy was originally created by one of our developers, Marek Krzeminski, a few years ago as a fun internal character. Over time, they quietly became our in-office, local mathscot, popping up as mini 3D-printed Mathys around the office and even as a custom emoji someone created. 

Then, sometime last year, someone had what can only be described as a bold idea: 

What if we brought Mathy to life? 

And just like that, the giant maple leaf went from concept to costume. 

Mathy is fun, curious, and a little playful. That’s very intentional. That’s what math should feel like. 

We believe math matters. We also believe math should be approachable, joyful, and a place where curiosity is rewarded. Mathy reminds us, and hopefully others, that math doesn’t have to be intimidating. It can be fun, and it can inspire awe. 

I’ll be honest. When we decided to bring Mathy to JMM, I was a little nervous. Conferences are busy, serious places. Would people really want to interact with a seven-foot-tall maple leaf? 

As it turns out, yes. Very much yes. 

Researchers (from postdocs to seasoned academics), educators, and undergraduate and graduate students all stopped, smiled, laughed, and asked for photos. At one point, people were actually lining up to take pictures with Mathy.

Let’s just say: Mathy was a hit. 

How tall is Mathy? 
About 7 feet. They are hard to miss. 

What does Mathy love (besides math)? 
Dancing. Very much dancing. 
You can see for yourself here: Mathy's got moves!

Does Mathy talk? 
You bet they do. 

Now that Mathy has officially been introduced to the world, you’ll be seeing them more often on social media, at events, and in a few other fun places we’re cooking up. 

So if you spot a giant maple leaf dancing, waving, or talking math, now you know who they are. 

If you spot Mathy, don’t be shy, say hi. 

Over the past year, I have spent a lot of time talking to educators, researchers, and engineers about AI. The feeling is almost universal: it is impressive, it is helpful, but you should absolutely not trust it with your math even if it sounds confident.

That tension between how capable AI feels and how accurate it actually is has been on my mind for months. AI is not going away. The challenge now is figuring out how to make it reliable.

That is where Maple MCP comes in.

Maple MCP (Model Context Protocol) connects large language models like ChatGPT, Claude, Cohere, and Perplexity to Maple’s world-class math engine.

When your AI encounters math, your AI can turn to Maple to handle the computation so the results are ones you can actually trust.

It is a simple idea, but an important one: Maple does the math and the AI does the talking. Instead of guessing, the AI can be directed to call on Maple whenever accuracy matters.

Model Context Protocol (MCP) is an emerging open standard that allows AI systems to connect to external tools and data sources. It gives language models a structured way to request computations, pass inputs, and receive reliable outputs, rather than trying to predict everything in text form.

Here is a high-level view of how MCP fits into the broader ecosystem:

MCP lets an AI system connect securely to specialized services, like Maple, that provide capabilities the model does not have on its own.

If you want to learn more about the MCP standard, the documentation is a great starting point: Model Context Protocol documentation

Here is a glimpse of what happens when Maple joins the conversation:

Depending on the prompt, Maple MCP can evaluate expressions symbolically or numerically, execute Maple code, expand or factor expressions, integrate or solve equations, and even generate interactive visualizations. If you ask for an exploration or an activity, it can create a Maple Learn document with the parameters and sliders already in place.

As an example of how this plays out in practice, I asked Maple MCP:

“I'd like to create an interactive math activity in Maple that allows my students to explore the tangent of a line for the function f(x) = sin(x) + 0.5x for various values of x.”

It generated a complete Maple Learn activity that was ready to use and share. You can open the interactive version here: interactive tangent line activity .

In full disclosure, I did have to go back and forth a bit to get the exact results I wanted, mostly because my prompt wasn’t very specific, but the process was smooth, and I know it will only get better over time.

What is exciting is that this does not replace the LLM; it complements it. The model still explains, reasons, and interacts naturally. Maple simply steps in to do the math—the part AI cannot reliably do on its own.

We have opened the Maple MCP public beta, and I would love for you to try it.

Sign up today and we will send you everything you need to get started!

When we think about AI, most of us picture tools like ChatGPT or Gemini. However, the reality is that AI is already built into the tools we use every day, even something as familiar as a web search. And if AI is everywhere, then so are its mistakes.

A Surprising Answer from Google

Recently, I was talking with my colleague Paulina, Senior Architect at Maplesoft, who also manages the team that creates all the Maple Learn content. We were talking about Google’s AI Overview, and I said I liked it because it usually seemed accurate. She disagreed, saying she’d found plenty of errors. Naturally, I asked for an example.

Her suggestion was simple: search “is x + y a polynomial.”

So I did. Here’s what Google’s AI Overview told me:

“No, x + y is not a polynomial”

My reaction? HUH?!

The explanation correctly defined what a polynomial is but still failed to recognize that both x and y each have an implicit exponent of 1. The logic was there, but the conclusion was wrong.

Using It in the Classroom

This makes a great classroom example because it’s quick and engaging. Ask your students first whether x + y is a polynomial, then show them the AI result. The surprise sparks discussion: why does the explanation sound right but end with the wrong conclusion?

In just a few minutes, you’ve not only reviewed a basic concept but also reinforced the habit of questioning answers even when they look authoritative.

Why This Matters

As I said in a previous post, the real issue isn’t the math slip, it’s the habit of accepting answers without questioning them. It’s our responsibility to teach students how to use these tools responsibly, especially as AI use continues to grow. Critical thinking has always mattered, and now it’s essential.