No One Yet Has an Answer to Claude Code

I hate LinkedIn's post character limit and how they force relatively short brain dumps into an Article. Shave off 500 characters or create an Article. I don't need these kind of no win choices.

For power users deciding where to spend their LLM dollars, the choice has become surprisingly clear—and it’s not actually about which LLM performs best. While I’d probably pick Gemini 2.5 Pro as my preferred model in a head-to-head comparison (though I use tons of o3 and wouldn’t call Gemini definitively superior to Claude), the real differentiator isn’t the underlying language model at all.

It’s Claude Code, and Anthropic’s decision to include it with their monthly subscription plans.

The Economics Change Everything

Claude Code on a monthly plan represents a seismic shift in AI accessibility. Why? Because API access to frontier LLMs gets expensive fast with any serious usage. Claude Code delivers API-like functionality with what amounts to unlimited Sonnet access for $100/month, or unlimited Opus access for $200/month. They even just opened up Claude Code to the $20 pro plan, but don't count on that feeling anything like unlimited.

Yes, there are session limits on these plans that complicate the “unlimited” claim, and the token limits mean you won't be able to spin up multiple agents to crank away 24/7, but for most uses and users, it’s effectively unlimited—or at minimum, ridiculously cheaper than equivalent API costs.

None of the major competitors offer anything close to this value proposition. Sure, choosing Claude Code locks you into Anthropic’s ecosystem—you can’t use Roo Code or other coding assistants like you can with the API. But it’s a pretty compelling ecosystem to be locked into. Claude Code excels as an agentic coder, and crucially, it’s super flexible and can run in headless mode, making it easy to integrate into scripts, tools, and services.

Breaking Down Barriers

That flexibility opens up possibilities that were previously cost-prohibitive or cost-anxiety-producing. Take something I’ve wanted for a while: an AI agent that can operate a terminal autonomously. I want to say “debug this problem” or “set up this environment” and hand over terminal access until the task is complete.

Two major obstacles made this impractical before:

Security concerns: I don’t want sensitive data—tax documents, passwords, IP addresses, or countless other private information—leaving my local network and heading to a third party.

Cost anxiety: The per-task costs and unpredictable token usage of complex terminal tasks would have made me constantly second-guess whether any given task was worth potentially $3.50 or much more. I wouldn’t build something like this that I couldn’t use freely.

The Two-Piece Solution

Recent developments provide an elegant solution. The first piece is the newly released local LLM model, Devstral, a local model that’s the first to handle tool calling reasonably well. And at 22B parameters, it’s small enough to leave plenty of VRAM for usable context sizes. The catch? While competent at tool calling, it’s nowhere near frontier model performance.

Claude Code on the $100 monthly plan is the second piece. You can use the Claude Code CLI in headless mode as a high-level coordinator, sending it abstracted, specifics-free prompts and using its responses to guide the local model. The local LLM handles tool calling and low-level execution while the frontier model—without usage anxiety—handles brainstorming, planning, and strategic guidance.

This architecture keeps sensitive data local while leveraging frontier-model intelligence for coordination, all without the crushing costs that would make such a system impractical for most via traditional API access.

The Competitive Void

This is just one example of the freedom and power that Claude Code’s pricing model enables. The ability to use frontier AI capabilities without constantly calculating costs opens up entirely new categories of applications and experimentation.

And so far, none of the other major players have offered anything comparable. That’s a significant competitive advantage for Anthropic-one that goes far beyond model performance metrics and strikes at the heart of how power users actually want to use AI tools.

For power users, the choice is pretty clear in my mind: it’s not about finding the best model—any of the top models will get you there—it’s about finding the best model you can actually afford to use freely. Right now, that’s Anthropic's models via Claude Code, and the competition isn't even in the ballpark.