Anthropic just released Opus 4.6, an update to their most powerful model. On paper, the specs are impressive: adaptive thinking with four reasoning levels, 1 million token context, and pricing that's competitive with GPT 5.2.
But benchmarks and spec sheets don't tell the real story. What matters is what the model can actually build.
After watching extensive tests of Opus 4.6, I saw something that genuinely surprised me — and it says a lot about where AI capabilities are going.
Let's get the technical stuff out of the way:
Benchmarks show Opus 4.6 is neck-and-neck with GPT 5.2 Codecs. Not a massive leap, but solid improvements.
But the real test is what happens when you ask it to build something complex.
The tests covered browser OS simulation, 3D printing, multimodal coding, and several games. Here's what stood out.
Opus 4.6 built a browser-based operating system called "Novos" with:
It was competent. It worked. But we've seen similar results from other models. Nothing revolutionary here.
This was impressive. The simulation included:
What stood out was the bed slinger mechanic. Most models go for the simpler core XY design where the bed is static. Opus 4.6 understood the nuances of different printer types and implemented the less common, more complex option.
This wasn't just copying templates — it was understanding the domain.
This test gave Opus 4.6 a hand-drawn wireframe and asked it to create a portfolio website based on the design.
The result was... disappointing. It stuck too close to the wireframe, essentially turning the minimal sketch into a website without adding much visual flair or creativity.
The expectation was that it would take the wireframe as inspiration and create something impressive. Instead, it just followed the drawing too literally.
This is a reminder that AI still struggles with creative interpretation when given too specific a reference.
This was the standout result. Opus 4.6 created a flight combat simulator game that was genuinely impressive:
The fact that it added sound on its own is significant. The prompt didn't ask for sound — the model decided that would make the game better and implemented it.
This is the kind of proactive design decision that separates good AI from great AI.
The drum kit simulator test asks for either 3D assets or 2D photorealistic assets. Opus 4.6 went with 2D photorealistic and nailed it:
The reviewer said this was "one of the most realistic Phil Collins tests that we've ever performed on this channel" and that the sounds were fantastic.
Again, it went with 2D instead of 3D, but the execution was so good that it didn't matter. It prioritized quality over showing off 3D capabilities.
This is the result that genuinely impressed me — and it should impress anyone paying attention to AI coding capabilities.
Opus 4.6 was asked to create a self-contained C++ skateboarding game with:
The result was phenomenal:
The reviewer said this was "hands down the best result" they'd received for this test, which they had only run with pro-tier subscription models (Gemini 3 Pro DeepThink and GPT 5.2 Pro).
Let that sink in: Opus 4.6, in a standard tier, produced better results than the expensive pro tiers of other models.
Let's talk about what these capabilities actually mean for companies and developers.
The skateboarding game was generated in a single shot. No iteration. No debugging. Just "here's the prompt, here's the game, it works."
This is the holy grail of AI coding — not just assisting developers, but generating complete, functional applications that work out of the box.
For businesses, this means:
The flight simulator adding sound effects is a big deal. The model realized that a combat game without sound is less immersive and implemented it on its own.
This is the shift from "AI does exactly what you tell it" to "AI understands what you're building and adds what makes sense."
For product development, this is huge. You're not just getting the minimum viable implementation — you're getting thoughtful features that improve the user experience.
Opus 4.6 is competitive with GPT 5.2 on benchmarks. In some tests, it performed better than pro-tier versions of other models.
This means businesses have real options. You're not locked into one provider. You can choose based on:
The monopoly on frontier capabilities is breaking.
The 3D printer simulation showed that AI can now understand and simulate complex real-world systems with nuance. It didn't just create a generic printer — it understood the differences between bed slinger and core XY designs and chose appropriately.
This opens up applications in:
The drum kit and flight simulator both featured audio. We're seeing AI models start to understand that modern applications aren't just visual — they're multi-sensory experiences.
For game development and interactive media, this is crucial. Sound design is hard to get right, and AI is starting to understand what makes audio feel natural and immersive.
Let's be real about what Opus 4.6 can't do yet.
The portfolio test shows that when given too specific a reference, AI struggles to add creative value. It sticks too literally to the input instead of interpreting it and elevating it.
This is where human designers still have an edge — taking a concept and transforming it into something exceptional.
The Python FPS game initially spawned the player in a locked room, making it impossible to play. It required a second prompt to fix the spawn point and open the center structure.
Even in impressive results, there can be bugs or design flaws that require human intervention.
While the skateboarding game was impressive, it's worth noting that the reviewer had personal experience with skateboarding games from their youth. They knew what good controls felt like and could appreciate the mechanics.
AI can build functional applications, but truly exceptional products still require domain expertise to understand what "good" means.
We're moving toward a future where AI can generate entire applications — not just code snippets or components, but complete, functional systems.
Opus 4.6 isn't there yet, but the skateboarding game shows we're getting close. 1,950 lines of clean, error-free C++ code generated in a single shot is unprecedented.
For businesses, this changes the economics of software development:
The competitive advantage shifts from who can code fastest to who can direct AI most effectively.
If you're running a business that builds software or digital products:
Don't just use AI for code snippets or simple scripts. Give it complex, open-ended tasks like "build a skateboarding game" and see what happens.
AI isn't replacing developers — it's amplifying them. Figure out how to combine AI's generation capabilities with human expertise in design, UX, and domain knowledge.
The difference between good and great AI outputs comes down to how you prompt. Learn to give enough direction without constraining creativity.
Even impressive results like the FPS game had bugs. Design your workflow to handle rapid iteration: generate, test, refine, repeat.
Opus 4.6 is more than just incremental improvements. The fact that it can generate a complex, functional C++ game in a single shot — and that it's better than what pro-tier models from other companies can do — tells us something important.
AI coding capabilities are accelerating faster than most people realize. We're not far from the point where "I'll have the AI build that" isn't a joke — it's a viable approach to software development.
The businesses that figure out how to leverage this first will have a massive advantage. Not because they have better AI — everyone has access to the same models — but because they've built the workflows and expertise to direct AI effectively.
The skateboarding game is impressive. But what's more impressive is what it represents about where AI is going.
Want to leverage AI coding capabilities for your business? That's what we do at Medianeth. We help companies figure out how to use AI not just as a tool, but as a force multiplier. Let's talk about what you're building.
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With 8+ years building software from the Philippines, Jomar has served 50+ US, Australian, and UK clients. He specializes in construction SaaS, enterprise automation, and helping Western companies build high-performing Philippine development teams.
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