u/AdGlittering2629

Hot take after comparing GPT-5.5 vs Opus 4.7 for a few days:

GPT-5.5 feels like the smarter general brain.

Opus 4.7 feels like the more specialized “work machine.”

Especially for:

• long coding sessions
• huge context windows
• persistent agent workflows

But the pricing dynamics on Opus 4.7 are kinda wild once token usage scales.

I think we’re entering a phase where the best AI model isn’t just about intelligence anymore.

Now it’s:

• context reliability
• orchestration
• operational cost
• long-session stability

Which honestly feels more important for real production systems anyway.

Interested to hear which one people are actually sticking with for daily use.

After testing Opus 4.7 against Opus 4.6 and GPT-5.5, I think the comparison is becoming less about benchmark scores and more about operational behavior.

GPT-5.5 still feels strongest for:

• generalized reasoning
• ambiguity handling
• structured outputs
• instruction stability

But Opus 4.7 seems optimized around:

• long-context retention
• agent workflows
• codebase navigation
• multi-step execution chains

The interesting part is that Opus 4.7 doesn’t necessarily “feel smarter” in short conversations.

It feels more optimized for systems that stay alive for a long time.

That’s a very different direction from earlier model generations.

Also noticing significantly higher effective token usage during larger tasks compared to older Opus versions.

Anyone else seeing similar behavior in production workflows?

Different teams were using different platforms, workflows didn’t connect properly, and we kept building manual workarounds around the software instead of the software adapting to the business.

That’s what pushed us into custom application development.

Not talking about huge enterprise systems: even small internal tools can make a massive difference when they’re built around actual workflows.

A few things we noticed after moving toward custom solutions:

• Less dependency on 5-6 disconnected SaaS tools
• Better automation between departments
• Faster reporting and approvals
• Easier scaling for niche processes
• Lower long-term operational friction

The surprising part was that many businesses don’t actually need “more software.”
They need fewer tools that fit better.

Curious how others here approach this:

Would you rather keep stacking SaaS subscriptions or invest in custom internal systems once operations become complex enough?