Claude Opus 4.7 is Anthropic’s most advanced generally available AI model, built for complex reasoning and agentic coding. It handles long-running tasks, follows instructions precisely, verifies outputs, and delivers high-quality results across coding, research, and workflows.
First impression was very, very positive! As I was preparing for my launch yesterday, it pretty much saved the day! It caught errors that 4.6 was ignoring for long time, helped me design some really valuable scripts and designed some really cool graphics & flows for me.
Maybe I'm just hyped and excited, but I felt like I couldn't do it without this. Came exactly on the right time!
The jump from Opus 4 to 4.7 in agentic coding is massive. I've been using Claude Code daily and the difference in how it handles multi-file refactors and complex debugging chains is night and day. The extended thinking really shines when you give it architectural decisions to reason through.
I tried a quick brainstorm on some strategic direction, but didn't really like the response. It was not challenging me, even with explicit instructions to do so. Curious to what others are experiencing. Could it be that this model is even more tailored to e.g., coding than Opus 4.6?
I’ve been using Claude pretty regularly for coding and problem solving, and one thing I’ve really appreciated is how well it handles longer, more complex tasks compared to most tools.
There have been quite a few times where I didn’t have to keep re-explaining context, which made a big difference when working through multi-step problems. Curious how much further 4.7 pushes this, especially around maintaining context and reasoning across longer workflows. Excited to try it out.
the agentic coding benchmarks look wild — curious how it handles really long-horizon tasks in practice vs. the SWE-bench numbers. anyone tried it on multi-hour agent workflows yet?
Multiple people here mention token consumption being brutal. What's the rough token count on a typical multi-file refactor compared to Opus 4? Trying to figure out if the quality jump justifies the cost jump before committing to it for longer sessions.