After months of using Claude Sonnet 4.5 with GitHub Copilot Pro+, I've switched to Opus 4.5 which just achieved a perfect 100% score on SvelteBench. Here's my recommended setup for SvelteKit developers and how I'm building an AI-powered Agile assistant called Apollo.
I've been building a lot with SvelteKit lately, and like many developers in 2025, I rely heavily on AI coding assistants to boost my productivity. For the past several months, I've been using GitHub Copilot Pro+ with Claude Sonnet 4.5 as my primary model—and it's been fantastic. But just recently, something even better came along.
If you're building with SvelteKit and want to know which AI model will serve you
best, check out
SvelteBench—a
comprehensive benchmark that tests how well different LLMs understand and
generate SvelteKit code. It covers everything from basic reactivity (counter,
derived, effect) to more advanced concepts like snippets and props.
I'd been watching this benchmark for a while. Sonnet 4.5 was doing really well, and I thought it was at over 99% at some point—though the benchmark may have been updated or the model may have changed slightly. Either way, it was consistently one of the top performers.
Then Claude Opus 4.5 dropped.
When I checked SvelteBench after the Opus 4.5 release, I was genuinely surprised to see it hit 100% across every single test:
This is remarkable. Even the inspect test, which the benchmark notes has
"known correctness issues," Opus 4.5 handles flawlessly. For comparison, Sonnet
4.5 still performs excellently but shows some variation in the inspect test
(40% Pass@1). The difference is subtle but meaningful when you're writing a lot
of Svelte code.
If you're serious about SvelteKit development and want the best AI assistance available, here's my recommendation:
The Pro+ tier gives you access to the most capable models, including Claude Opus 4.5. While the regular Copilot is good, Pro+ with Opus 4.5 is on another level for framework-specific code generation. The $40/month is absolutely worth it if you're coding professionally.
Beyond just using AI for code generation, I've found that combining GitHub Issues with proper Agile/SCRUM methodology dramatically improves project organization. Structure your issues as user stories:
As a [user type], I want [some goal], So that [some reason].
This format isn't just good for human collaboration—it's also excellent context for AI assistants. When your issues are well-structured, AI tools can better understand what you're trying to accomplish.
Organize your issues into GitHub Projects with proper sprint planning. The combination of well-written user stories and organized project boards creates a powerful workflow that AI assistants can tap into.
Speaking of AI and Agile methodology, I've actually been building something to help bridge this gap. Apollo is an AI-powered voice/text chatbot that acts as an Agile/SCRUM assistant. It can:
You can check out Apollo at apollo.starspace.group, and the source code is available at github.com/starspacegroup/apollo.
The idea is to make project management as seamless as having a conversation. Instead of context-switching between coding and project management tools, you can just tell Apollo what needs to happen, and it handles the GitHub integration for you.
The AI coding landscape is evolving rapidly. Just when I thought Sonnet 4.5 was the peak for SvelteKit development, Opus 4.5 came along and proved there's still room to improve. If you're building with SvelteKit in 2025:
The combination of a 100%-accurate AI model and well-organized Agile workflows is a game-changer for productivity. Happy coding! 🚀