As a baseline, here’s the AI-generated image from two years ago, created with DALL-E 3.

The original DALL-E 3 photo looks kind of crazy when you look back. I would never garnish a drink with a grapefruit wedge; it would have been a peel.

To remind you of the cocktail recipe for The Merry Manhattan:

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The Merry Manhattan

Recipe by Hans Scharler

Prep time

5

minutes

The Merry Manhattan is a festive twist on the classic Manhattan cocktail, perfect for holiday celebrations. This elegant drink features a rich amber hue, achieved by blending rye whiskey with sweet vermouth. The traditional flavor is enhanced with a unique addition of smoked cherries, adding a subtle, smoky sweetness. A sprig of rosemary infuses the cocktail with a fragrant, herbaceous aroma, invoking the essence of winter. The drink is served in a rocks glass containing a large ice chunk. The finishing touch is a gracefully twisted grapefruit peel, adding a citrusy zing and completing the cocktail’s holiday charm.

Ingredients

• 1 oz Carpano Antica Formula Vermouth

• 2 oz Whistle Pig Rye Whiskey

• 2 dashes Sour Cherry Bitters

• 2 dashes Peychaud’s Bitters

• Fresh cherries (for smoking)

• Fresh rosemary (for smoking)

• Grapefruit peel (for garnish)

• Ice

Directions

• Prepare the Smoke:
  Gather a few fresh cherries and a sprig of rosemary.
  Using a kitchen torch, gently torch the rosemary and cherries until they start to smoke. Be careful not to burn them.
  Immediately cover the smoking rosemary and cherries with a rocks glass to trap the smoke inside. Let it sit for a minute to infuse the glass with the smoky aroma.
• In a mixing glass, combine 1 oz of Vermouth and 2 oz of Rye Whiskey.
• Add two dashes each of Sour Cherry Bitters and Peychaud’s Bitters.
• Fill the mixing glass with ice and stir well to chill and dilute the cocktail.
• Add an ice chunk to the smoked rocks glass.
• Strain the stirred cocktail into the smoked rocks glass.
• Finish and Garnish:
  Take a grapefruit peel and express (squeeze) its oils over the drink.
  Use the grapefruit peel as a garnish.
  Use one of the smoked cherries as a garnish.
  Use the sprig of rosemary as a garnish.

Notes

• Enjoy the smoked cherries as a treat, or save them for a future cocktail garnish.

Here’s the first try with Nano Banna Pro. I gave it the recipe card along with the prompt to create a realistic photo for a Christmas or New Year’s party setting.

I am still not in love with the photos. They are better for sure. Let me try a new approach. I am going to just send a link to the blog post and ask for the photos again.

This time it was much better. I like that it used a fancy cocktail cherry, like a Luxardo cherry, instead of one with a stem.

I found this an interesting way to visualize how a drink can come together. I like inventing my own cocktails for parties. This gives me a way to experiment with the visual presentation.

Now, Nano Banana Pro has way more capability than DALL-E 3 had, so I can do more things. I can make process diagrams for the recipe card. Let’s try that.

Now, we are talking.

A few takeaways:

• Models are improving rapidly.
• You can do something new with Generative AI models.
• And, the power of AND. I started with a photo, then pivoted to a recipe card diagram. I could keep anding. I could make it a YouTube video script. I could make it a series of cocktails. This is the most critical takeaway for 2026. It is not just about doing one thing more efficiently; it is about doing more things than you could before.

Buckle up. You might need a drink in 2026.

Gemini 3 Pro

Google went a different direction. Gemini 3 Pro is all about reasoning, multimodal inputs, and that million-token context window.

The benchmark numbers are wild. It hit 91.9% on GPQA Diamond. On ARC-AGI-2, the abstract reasoning benchmark, it scored 31.1% (and up to 45% in Deep Think mode). That is a huge leap over previous models. On LMArena it took the top ELO spot.

If your work is heavy on reasoning, vision, video, or you need to throw massive context at a problem, Gemini 3 Pro is built for that.

Claude Opus 4.5

Anthropic announced Opus 4.5 on November 24, 2025. They are calling it the best model in the world for coding, agents, and computer use. Bold claim.

On their internal engineering benchmarks, Opus 4.5 scored higher than any human candidate ever on their take-home exam. It also delivers higher pass rates on tests while using up to 65% fewer tokens than Sonnet 4.5. That efficiency piece matters if you are building agents that run for hours.

The pitch is clear: if you care about code, automation, and not burning through tokens, Opus 4.5 is the one.

How They Compare

Opus 4.5 is aimed at engineers building agents and writing code. It is optimized for efficiency. Gemini 3 Pro is aimed at everything else: reasoning, multimodal, long context, general purpose.

Both are frontier models. Both are available on major clouds and APIs. The honest answer is you might end up using both depending on the task.

The Real Point

The meta-point is not which model wins. The point is that two frontier models landed in the same week, both pushing hard on different axes. Reasoning, coding, agents, vision, efficiency. The pace of improvement is nuts.

If you are building with AI right now, the table is full. Pick your model, match it to your task, and start experimenting. There has never been a better time.

What excites me about Claude Opus 4.5?

While I was writing this post, a friend texted me to ask what excited me about Claude Opus 4.5. I spent a couple of hours with it today, and I would have to say… tool search.

Tool search lets Claude discover and load tools on-demand instead of pre-loading every definition at the start.

Here is how it works. You provide a catalog of tools to the API with names, descriptions, and input schemas. You mark most of them with defer_loading: true, which means they stay out of the model’s context until needed. Then you include a tool-search tool in the list. When Claude needs a new capability, it searches, finds the right tool, and only then does that tool get loaded into context.

There are two pain points this solves… expensive tokens and picking the right tool.

When you load dozens of tools upfront, the definitions alone eat up thousands of tokens. That is space you could be using for reasoning, tool outputs, and user messages. With tool search, you load only the search tool and maybe three to five relevant definitions. The overhead drops significantly.

With large libraries of tools, the model can struggle to pick the right one, especially when names or parameters are similar. The search step narrows the candidates to tools that actually match the task.

I typed these words into a text box: Elephants Wear Fancy Hats to a Party

This is the video that was produced two minutes later.

Try the latest AI models from Google at https://gemini.google.com/.

What are you experimenting with?