Happy New Year. And yes — Happy New News.
While most of us were still easing back into work mode, Ollama quietly shipped an update that lets Claude Code run against local models. Not Claude’s own models, to be clear, but local or self-hosted LLMs that speak the same API language.
It’s one of those updates that looks small on the surface. A compatibility tweak. A few environment variables. Nothing flashy.
But step back for a moment, and the implications are bigger than they first appear.
What actually changed
Claude Code has always talked to Anthropic via the Messages API. What changed is that Ollama now implements that same API, meaning Claude Code can point somewhere other than Anthropic’s cloud.
In plain English:
Claude Code didn’t become local.
Ollama became fluent in Claude Code.
You can read the official documentation here:
Ollama: Using Claude-compatible APIs
And yes — it really is that simple from a setup point of view.
Let’s talk reality: local models are not magic
If you spend any time reading community threads around this release, the mood is… cautious.
The general feeling is that most local models struggle with complex tool-calling and don’t come close to Claude’s raw performance. Even on decent hardware, people report slower responses, higher memory pressure, and more fiddling than they’d like.
This isn’t surprising. Claude is very, very good at orchestration-heavy tasks. Local models are getting better, but they’re not there yet.
That said, there are bright spots.
Users with beefy machines — especially Macs with large unified memory — report a genuinely usable experience. You’ll need plenty of RAM or VRAM, and you’ll almost certainly need to benchmark a few models to find one that doesn’t disappoint. One commonly suggested option for tool use is Granite, though results vary.
Where local setups do shine is in two areas:
- Saving your precious Claude credits by offloading simpler or repetitive tasks
- Privacy, especially for sensitive codebases or internal tooling
Think of it less as a Claude replacement, and more as a pressure valve.
A quick demo, for the visually inclined
If you want to see this in action rather than read about it, this short demo is worth a watch:
It’s calm. Boring, even. Which is exactly what you want from infrastructure.
Why this matters more than it looks
Historically, Claude Code has been — arguably — the best code harness on the market.
Fast. Intuitive. Deeply contextual. Comfortable once you settle into it.
But it has also been tightly locked to Anthropic’s models.
At the same time, a growing group has been left standing outside the door:
- Developers running local inference (Ollama, GGUF, LM Studio)
- Enterprises with on-prem security requirements
- AI researchers needing model flexibility
- Startups building on open foundation models like DeepSeek, Qwen, or Mistral
These teams want the experience of Claude Code without being forced into a single vendor’s models.
Until now, that simply wasn’t possible.
This Ollama update doesn’t solve everything — but it cracks the door open.
It exposes a long-standing market gap:
There is no best-in-class AI coding harness that is simultaneously:
- IDE-like in productivity
- Model-agnostic
- Capable of agentic workflows, memory, and tool use
Claude Code proves the UI and workflow can exist. Ollama proves the backend doesn’t have to be singular.
That combination is quietly powerful.
The strategic takeaway
If you’re a decision maker, this isn’t about whether local LLMs beat Claude today. They don’t.
It’s about optionality.
- Offload the easy stuff locally
- Keep sensitive work close to home
- Reduce long-term dependency risk
- Preserve flexibility as models evolve
That’s a very 2026 way to think about AI.
So yes — Happy New Year.
And genuinely, Happy New News.
This one isn’t loud. But it’s the kind of change that keeps compounding.
Written for KiwiGPT.co.nz — Generated, Published and Tinkered with AI by a Kiwi