Claude Code: connect to a local model when your quota runs out

https://boxc.net/blog/2026/claude-code-connecting-to-local-models-when-your-quota-runs-out/

by fugu2 • 6 months ago

386 193 6 months ago

boxc.net

baalimago• 6 months ago

Or better yet: Connect to some trendy AI (or web3) company's chatbot. It almost always outputs good coding tips

alexhans5• 6 months ago

Useful tip.

From a strategic standpoint of privacy, cost and control, I immediately went for local models, because that allowed to baseline tradeoffs and it also made it easier to understand where vendor lock-in could happen, or not get too narrow in perspective (e.g. llama.cpp/open router depending on local/cloud [1] ).

With the explosion of popularity of CLI tools (claude/continue/codex/kiro/etc) it still makes sense to be able to do the same, even if you can use several strategies to subsidize your cloud costs (being aware of the lack of privacy tradeoffs).

I would absolutely pitch that and evals as one small practice that will have compounding value for any "automation" you want to design in the future, because at some point you'll care about cost, risks, accuracy and regressions.

[1] https://alexhans.github.io/posts/aider-with-open-router.html

[2] https://www.reddit.com/r/LocalLLaMA

mogoman• 6 months ago

can you recommend a setup with ollama and a cli tool? Do you know if I need a licence for Claude if I only use my own local LLM?

alexhans5• 6 months ago

What are your needs/constraints (hardware constraints definitely a big one)?

The one I mentioned called continue.dev [1] is easy to try out and see if it meets your needs.

Hitting local models with it should be very easy (it calls APIs at a specific port)

[1] https://github.com/continuedev/continue

drifkin• 6 months ago

we recently added a `launch` command to Ollama, so you can set up tools like Claude Code easily: https://ollama.com/blog/launch

tldr; `ollama launch claude`

glm-4.7-flash is a nice local model for this sort of thing if you have a machine that can run it

w4yai• 6 months ago

You must try GLM4.7 and KimiK2.5 !

I also highly suggest OpenCode. You'll get the same Claude Code vibe.

If your computer is not beefy enough to run them locally, Synthetic is a bless when it comes to providing these models, their team is responsive, no downtime or any issue for the last 6 months.

Full list of models provided : https://dev.synthetic.new/docs/api/models

Referal link if you're interested in trying it for free, and discount for the first month : https://synthetic.new/?referral=kwjqga9QYoUgpZV

cyanydeez• 6 months ago

I think control should be top of the list here. You're talking about building work flows, products and long term practices around something that's inherently non-deterministic.

And the probability that any given model you use today is the same as what you use tomorrow is doubly doubtful:

1. The model itself will change as they try to improve the cost-per-test improves. This will necessarily make your expectations non-deterministic.

2. The "harness" around that model will change as business-cost is tightened and the amount of context around the model is changed to improve the business case which generates the most money.

Then there's the "cataclysmic" lockout cost where you accidently use the wrong tool that gets you locked out of the entire ecosystem and you are black listed, like a gambler in vegas who figures out how to count cards and it works until the house's accountant identifies you as a non-negligible customer cost.

It's akin to anti-union arguments where everyone "buying" into the cloud AI circus thinks they're going to strike gold and completely ignores the fact that very few will and if they really wanted a better world and more control, they'd unionize and limit their illusions of grandeur. It should be an easy argument to make, but we're seeing about 1/3 of the population are extremely susceptible to greed based illusions.,

alexhans5• 6 months ago

You're right. Control is the big one and both privacy and cost are only possible because you have control. It's a similar benefit to the one of Linux distros or open source software.

The rest of your points are why I mentioned AI evals and regressions. I share your sentiment. I've pitched it in the past as "We can’t compare what we can’t measure" and "Can I trust this to run on its own?" and how automation requires intent and understanding your risk profile. None of this is new for anyone who has designed software with sufficient impact in the past, of course.

Since you're interested in combating non-determinism, I wonder if you've reached the same conclusion of reducing the spaces where it can occur and compound making the "LLM" parts as minimal as possible between solid deterministic and well-tested building blocks (e.g. https://alexhans.github.io/posts/series/evals/error-compound... ).

dec0dedab0de• 6 months ago

Most Anti-Union arguments I have heard have been about them charging too much in dues, union leadership cozying up to management, and them acting like organized crime doing things like smashing windows of non-union jobs. I have never heard anyone be against unions because they thought they would make it rich on their own.

lancekey2• 6 months ago

Can you say a bit more about evals and your approach?

alexhans5• 6 months ago

High level, the approach is:

- I'm pain point driven:

  - I can't compare what I can't measure. 

  - I can't trust to run this "AI" tool to run on its own

- That's automation, which is about intentionality (can I describe what I want?) and risk profile understanding (What's the blast radius/worst that could happen)

Then I treat it as if it was an Integration Test/Test Driven Development exercise of sorts.

- I don't start designing an entire cloud infrastructure.

- I make sure the "agent" is living in the location where the users actually live so that it can be the equivalent of an extra paid set of hands.

- I ask questions or replicate user stories and use deterministic tests wherever I can. Don't just go for LLMaaJ. What's the simplest thing you can think of?

- The important thing is rapid iteration and control. Just like in a unit testing scenario it's not about just writing a 100 tests but the ones that qualitatively allow you to move as fast as possible.

- At this stage where the space is moving so fast and we're learning so much, don't assume or try to over-optimize places that don't hurt and instead think about minimalism, ease of change, parameterization and ease of comparison with other components that form "the black box" and with itself.

- Once you have the benchmarks that you want, you can decide things like pick the cheapest model/agent configuration that does the job within the acceptable timeframe.

Happy to go deeper on these. I have some practical/runnable samples/text I can share on the topic after the weekend. I'll drop a link here when it's ready

swyx2• 6 months ago

i mean the other obvious answer is to plug in to the other claude code proxies that other model companies have made for you:

https://docs.z.ai/devpack/tool/claude

https://www.cerebras.ai/blog/introducing-cerebras-code

or i guess one of the hosted gpu providers

if you're basically a homelabber and wanted an excuse to run quantized models on your own device go for it but dont lie and mutter under your own tin foil hat that its a realistic replacement

heyyeah4• 6 months ago

It's definitely a backup solution but even since I was drafting the blog, Qwen3 Coder Next was released. It's a functional stop gap if you want to keep things local. I try to be up front in the blog for people to "Reduce your expectations about speed and performance!"

(Also, I love your podcast!)

swyx2• 6 months ago

ha tyyy. sorry for not being thaat much of a /r/localllama guy

zingar2• 6 months ago

I guess I should be able to use this config to point Claude at the GitHub copilot licensed models (including anthropic models). That’s pretty great. About 2/3 of the way through every day I’m forced to switch from Claude (pro license) to amp free and the different ergonomics are quite jarring. Open source folks get copilot tokens for free so that’s another pro license I don’t have to worry about.

zingar2• 6 months ago

Update: I managed to do exactly this using the GitHub project that OP links to.

hkpatel3• 6 months ago

Openrouter can also be used with claude code. https://openrouter.ai/docs/guides/claude-code-integration

htsh• 6 months ago

thanks! came in here to ask this.

we can do much better with a cheap model on openrouter (glm 4.7, kimi, etc.) than anything that I can run on my lowly 3090 :)

parthsareen• 6 months ago

Also recently added ollama launch claude if you want to connect to cloud models from there :)

raw_anon_11113• 6 months ago

Or just don’t use Claude Code and use Codex CLI. I have yet to hit a quota with Codex working all day. I hit the Claude limits within an hour or less.

This is with my regular $20/month ChatGpT subscription and my $200 a year (company reimbursed) Claude subscription.

mercutio22• 6 months ago

Yeah, the generosity of Anthropic is vastly less than OpenAI. Which is, itself, much less than Gemini (I've never paid Google a dime, I get hours of use out of gemini-cli every day). I run out of my weekly quota in 2-3 days, 5-hour quota in ~1 hour. And this is 1-2 tasks at a time, using Sonnet (Opus gets like 3 queries before I've used my quota).

Right now OpenAI is giving away fairly generous free credits to get people to try the macOS Codex client. And... it's quite good! Especially for free.

I've cancelled my Anthropic subscription...

raw_anon_11113• 6 months ago

Hmm, I might have to try Gemini. Open AI, Claude and Gemini are all explicitly approved by my employer. Especially since we use GSuite anyway

throwa356262• 6 months ago

How recent is your information?

Google significantly reduced the free quota and removed pro models from gemini cli some 2-3 moths ago.

Also, Gemini models eat tokens like crazy. Something Codex and Code would do with 2K tokens takes Gemini 100K. Not sure why.

0xbadcafebee4• 6 months ago

You're getting downvoted because people here don't know that the specific agent you pick can pollute your context and waste your tokens. Claude's system prompt is enormous, to say nothing of things like context windows and hidden subagents.

raw_anon_11113• 6 months ago

I am using Codex-cli with my regular $20 a month ChatGPT subscription. Never once had to worry about tokens, request etc. I logged in with my regular ChatGPT account and didn’t have to use an API key

esafak3• 6 months ago

Or they could just let people use their own harnesses again...

usef-3• 6 months ago

That wouldn't solve this problem.

And they do? That's what the API is.

The subscription always seemed clearly advertised for client usage, not general API usage, to me. I don't know why people are surprised after hacking the auth out of the client. (note in clients they can control prompting patterns for caching etc, it can be cheaper)

esafak3• 6 months ago

End users -- people who use harnesses -- have subscriptions so that makes no sense. General API usage is for production.

wkirby• 6 months ago

My experience thus far is that the local models are a) pretty slow and b) prone to making broken tool calls. Because of (a) the iteration loop slows down enough to where I wander off to do other tasks, meaning that (b) is way more problematic because I don't see it for who knows how long.

This is, however, a major improvement from ~6 months ago when even a single token `hi` from an agentic CLI could take >3 minutes to generate a response. I suspect the parallel processing of LMStudio 0.4.x and some better tuning of the initial context payload is responsible.

6 months from now, who knows?

israrkhan2• 6 months ago

Open models are trained more generically to work with "Any" tool.

Closed models are specifically tuned with tools, that model provider wants them to work with (for example specific tools under claude code), and hence they perform better.

I think this will always be the case, unless someone tunes open models to work with the tools that their coding agent will use.

dragonwriter• 6 months ago

Open models are trained more generically to work with "Any" tool. Closed models are specifically tuned with tools, that model provider wants them to work with (for example specific tools under claude code), and hence they perform better.

Some open models have specific training for defined tools (a notable example is OpenAI GPT-OSS and its "built in" tools for browser use and python execution (they are called built in tools, but they are really tool interfaces it is trained to use if made available.) And closed models are also trained to work with generic tools as well as their “built in” tools.

btbuildem• 6 months ago

I'm confused, wasn't this already available via env vars? ANTHROPIC_BASE_URL and so on, and yes you may have to write a thin proxy to wrap the calls to fit whatever backend you're using.

I've been running CC with Qwen3-Coder-30B (FP8) and I find it just as fast, but not nearly as clever.

eek21213• 6 months ago

I gotta say, the local models are catching up quick. Claude is definitely still ahead, but things are moving right along.

bcyn• 6 months ago

Which models perform anywhere close to Opus 4.5? In my experience none of the local models are even in the same ballpark.

heyyeah4• 6 months ago

This week: look at Qwen3 Coder Next and GLM 4.7 but it's changing fast.

I wrote this for the scenario you've run out of quota for the day or week but want a back up plan to keep going to give some options with obvious speed and quality trade-offs. There is also always the option to upgrade if your project and use case needs Opus 4.5.

mcbuilder• 6 months ago

Opencode has been a thing for a while now

paxys4• 6 months ago

Reduce your expectations about speed and performance!

Wildly understating this part.

Even the best local models (ones you run on beefy 128GB+ RAM machines) get nowhere close to the sheer intelligence of Claude/Gemini/Codex. At worst these models will move you backwards and just increase the amount of work Claude has to do when your limits reset.

zozbot2342• 6 months ago

The best open models such as Kimi 2.5 are about as smart today as the big proprietary models were one year ago. That's not "nothing" and is plenty good enough for everyday work.

reilly3000• 6 months ago

Which takes a $20k thunderbolt cluster of 2 512GB RAM Mac Studio Ultras to run at full quality…

teaearlgraycold3• 6 months ago

Having used K2.5 I’d judge it to be a little better than that. Maybe as good as proprietary models from last June?

paxys4• 6 months ago

LOCAL models. No one is running Kimi 2.5 on their Macbook or RTX 4090.

corysama• 6 months ago

The article mentions https://unsloth.ai/docs/basics/claude-codex

I'll add on https://unsloth.ai/docs/models/qwen3-coder-next

The full model is supposedly comparable to Sonnet 4.5 But, you can run the 4 bit quant on consumer hardware as long as your RAM + VRAM has room to hold 46GB. 8 bit needs 85.

0xbadcafebee4• 6 months ago

Kimi K2.5 is fourth place for intelligence right now. And it's not as good as the top frontier models at coding, but it's better than Claude 4.5 Sonnet. https://artificialanalysis.ai/models

Aurornis3• 6 months ago

The best open models such as Kimi 2.5 are about as smart today as the big proprietary models were one year ago

Kimi K2.5 is a trillion parameter model. You can't run it locally on anything other than extremely well equipped hardware. Even heavily quantized you'd still need 512GB of unified memory, and the quantization would impact the performance.

Also the proprietary models a year ago were not that good for anything beyond basic tasks.

nik282000• 6 months ago

intelligence

Whether it's a giant corporate model or something you run locally, there is no intelligence there. It's still just a lying engine. It will tell you the string of tokens most likely to come after your prompt based on training data that was stolen and used against the wishes of its original creators.

bicx• 6 months ago

Exactly. The comparison benchmark in the local LLM community is often GPT _3.5_, and most home machines can’t achieve that level.

dheera• 6 months ago

Maybe add to the Claude system prompt that it should work efficiently or else its unfinished work will be handed off to to a stupider junior LLM when its limits run out, and it will be forced to deal with the fallout the next day.

That might incentivize it to perform slightly better from the get go.

kridsdale32• 6 months ago

"You must always take two steps forward, for when you are off the clock, your adversary will take one step back."

bityard• 6 months ago

Correct, a rack full of datacenter equipment is not going to compete with anything that fits on your desk or lap. Well spotted.

But as a counterpoint: there are whole communities of people in this space who get significant value from models they run locally. I am one of them.

Gravey• 6 months ago

Would you mind sharing your hardware setup and use case(s)?

kamov• 6 months ago

What do you use local models for? I'm asking generally about possible applications of these smaller models

DANmode• 6 months ago

and you really should be measuring based on the worst-case scenario for tools like this.

richstokes• 6 months ago

This. It's a false economy if you value your time even slightly, pay for the extra tokens and use the premium models.

0xbadcafebee4• 6 months ago

The best local models are literally right behind Claude/Gemini/Codex. Check the benchmarks.

That said, Claude Code is designed to work with Anthropic's models. Agents have a buttload of custom work going on in the background to massage specific models to do things well.

girvo2• 6 months ago

The benchmarks simply do not match my experience though. I don’t put that much stock in them anymore.

andai2• 6 months ago

Yeah this is why I ended up getting Claude subscription in the first place.

I was using GLM on ZAI coding plan (jerry rigged Claude Code for $3/month), but finding myself asking Sonnet to rewrite 90% of the code GLM was giving me. At some point I was like "what the hell am I doing" and just switched.

To clarify, the code I was getting before mostly worked, it was just a lot less pleasant to look at and work with. Might be a matter of taste, but I found it had a big impact on my morale and productivity.

MuffinFlavored3• 6 months ago

Did you eventually move to a $20/mo Claude plan, $100/mo Claude plan, $200/mo, or API based? if API based, how much are you averaging a month?

Aurornis3• 6 months ago

but finding myself asking Sonnet to rewrite 90% of the code GLM was giving me. At some point I was like "what the hell am I doing" and just switched.

This is a very common sequence of events.

The frontier hosted models are so much better than everything else that it's not worth messing around with anything lesser if doing this professionally. The $20/month plans go a long way if context is managed carefully. For a professional developer or consultant, the $200/month plan is peanuts relative to compensation.

icedchai• 6 months ago

Same. I messed around with a bunch of local models on a box with 128GB of VRAM and the code quality was always meh. Local AI is a fun hobby though. But if you want to just get stuff done it’s not the way to go.

davidwritesbugs• 6 months ago

Similar experience to me. I tend to let glm-4.7 have a go at the problem then if it keeps having to try I'll switch to Sonnet or Opus to solve it. Glm is good for the low hanging fruit and planning

PeterStuer• 6 months ago

My very first tests of local Qwen-coder-next yesterday found it quite capable of acceptably improving Python functions when given clear objectives.

I'm not looking for a vibe coding "one-shot" full project model. I'm not looking to replace GPT 5.2 or Opus 4.5. But having a local instance running some Ralph loop overnight on a specific aspect for the price of electricity is alluring.

mycall2• 6 months ago

There is tons of improvements in the near future. Even Claude Code developer said he aimed at delivering a product that was built for future models he betted would improve enough to fulfill his assumptions. Parallel vLLM MoE local LLMs on a Strix Halo 128GB has some life in it yet.

seanmcdirmid2• 6 months ago

(ones you run on beefy 128GB+ RAM machines)

PC or Mac? A PC, ya, no way, not without beefy GPUs with lots of VRAM. A mac? Depends on the CPU, an M3 Ultra with 128GB of unified RAM is going to get closer, at least. You can have decent experiences with a Max CPU + 64GB of unified RAM (well, that's my setup at least).

QuantumNomad_• 6 months ago

Which models do you use, and how do you run them?

anon373839• 6 months ago

It's true that open models are a half-step behind the frontier, but I can't say that I've seen "sheer intelligence" from the models you mentioned. Just a couple of days ago Gemini 3 Pro was happily writing naive graph traversal code without any cycle detection or safety measures. If nothing else, I would have thought these models could nail basic algorithms by now?

cracki• 6 months ago

Did it have reason to assume the graph to be a certain type, such as directed or acyclic?

mlrtime• 6 months ago

The local ones yeah...

I have claude pro $20/mo and sometimes run out. I just set ANTHROPIC_BASE_URL to a localllm API endpoint that connects to a cheaper Openai model. I can continue with smaller tasks with no problem. This has been done for a long time.

majormajor• 6 months ago

The amount of "prompting" stuff (meta-prompting?) the "thinking" models do behind the scenes even beyond what the harnesses do is massive; you could of course rebuild it locally, but it's gonna make it just that much slower.

I expect it'll come along but I'm not gonna spend the $$$$ necessary to try to DIY it just yet.

acchow• 6 months ago

I agree. You could spin for 100 hours on a sub-par model or get it done in 10 minutes with a frontier model

EagnaIonat3• 6 months ago

The secret is to not run out of quota.

Instead have Claude know when to offload work to local models and what model is best suited for the job. It will shape the prompt for the model. Then have Claude review the results. Massive reduction in costs.

btw, at least on Macbooks you can run good models with just M1 32GB of memory.

BuildTheRobots• 6 months ago

I don't suppose you could point to any resources on where I could get started. I have a M2 with 64gb of unified memory and it'd be nice to make it work rather than burning Github credits.

kilroy123• 6 months ago

I strongly think you're on to something here. I wish Apple would invest heavily in something like this.

The big powerful models think about tasks, then offload some stuff to a drastically cheaper cloud model or the model running on your hardware.

amelius• 6 months ago

And at best?

cat_plus_plus2• 6 months ago

Depends on whether you want a programmer or a therapist. Given clear description of class structure and key algorithms, Qwen3-Code is way more likely to do exactly what is being asked than any Gemini model. If you want to turn a vague idea into a design, yeah cloud bot is better. Let's not forget that cloud bots have web search, if you hook up a local model to GPT Researcher or Onyx frontend, you will see reasonable performance, although open ended research is where cloud model scale does pay off. Provided it actually bothers to search rather than hallucinating to save backend costs. Also local uncensored model is way better at doing proper security analysis of your app / network.

altern8• 6 months ago

I was wondering the same thing, e.g. if it takes tens or hundreds of millions of dollars to train and keep a model up-to-date, how can an open source one compete with that?

gpm• 6 months ago

Less than a billion of dollars to become the arbiter of truth probably sounds like a great deal to the well off dictatorial powers of the world. So long as models can be trained to have a bias (and it's hard to see that going away) I'd be pretty surprised if they stop being released for free.

Which definitely has some questionable implications... but just like with advertising it's not like paying makes the incentives for the people capable of training models to put their thumbs on the scales go away.

tracker13• 6 months ago

For my relatively limited exposure, I'm not sure if I'd be able to tolerate it. I've found Claude/Opus to e pretty nice to work with... by contrast, I find Github Copilot to be the most annoying thing I've ever tried to work with.

Because of how the plugin works in VS code, on my third day of testing with Claude Code, I didn't click the Claude button and was accidentally working with CoPilot for about three hours of torture when I realized I wasn't in Claude Code. Will NEVER make that mistake again... I can only imagine anything I can run at any decent speed lcoally will be closer to the latter. I pretty quickly reach a "I can do this faster/better myself" point... even a few times with Claude/Opus, so my patience isn't always the greatest.

That said, I love how easy it is to build up a scaffold of a boilerplate app for the sole reason to test a single library/function in isolation from a larger application. In 5-10 minutes, I've got enough test harness around what I'm trying to work on/solve that it lets me focus on the problem at hand, while not worrying about doing this on the integrated larger project.

I've still got some thinking and experimenting to do with improving some of my workflows... but I will say that AI Assist has definitely been a multiplier in terms of my own productivity. At this point, there's literally no excuse not to have actual code running experiments when learning something new, connecting to something you haven't used before... etc. in terms of working on a solution to a problem. Assuming you have at least a rudimentary understanding of what you're actually trying to accomplish in the piece you are working on. I still don't have enough trust to use AI to build a larger system, or for that matter to truly just vibe code anything.

d4rkp4ttern• 6 months ago

Since Llama.cpp/llama-server recently added support for the Anthropic messages API, running Claude Code with several recent open-weight local models is now very easy. The messy part is what llama-server flags to use, including chat template etc. I've collected all of that setup info in my claude-code-tools [1] repo, for Qwen3-Coder-next, Qwen3-30B-A3B, Nemotron-3-Nano, GLM-4.7-Flash etc.

Among these, I had lots of trouble getting GLM-4.7-Flash to work (failed tool calls etc), and even when it works, it's at very low tok/s. On the other hand Qwen3 variants perform very well, speed wise. For local sensitive document work, these are excellent; for serious coding not so much.

One caviat missed in most instructions is that you have to set CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC = 1 in your ~/.claude/settings.json, otherwise CC's telemetry pings cause total network failure because local ports are exhausted.

[1] claude-code-tools local LLM setup: https://github.com/pchalasani/claude-code-tools/blob/main/do...

TaupeRanger• 6 months ago

God no. "Connect to a 2nd grader when your college intern is too sick to work."

israrkhan2• 6 months ago

Using claude code with custom models

Will it work? Yes. Will it produce same quality as Sonnet or Opus? No.

Animats• 6 months ago

When your AI is overworked, it gets dumber. It's backwards compatible with humans.

sorenjan• 6 months ago

Maybe you can log all the traffic to and from the proprietary models and fine tune a local model each weekend? It's probably against their terms of service, but it's not like they care where their training data comes from anyway.

Local models are relatively small, it seems wasteful to try and keep them as generalists. Fine tuning on your specific coding should make for better use of their limited parameter count.

PlatoIsADisease• 6 months ago

Is there an easy way to fine tune? I havent tried fine tuning since 2024, but it was not trivial back then.

starkeeper• 6 months ago

Very cool. Anyone have guidance for using this with jetbrains IDE? It has a Claude Code plugin, but I think the setup is different for intelliJ... I know it has some configuration for local models, but the integrated Claude is such a superior experience then using their Junie, or just prompting diffs from the regular UI interface. HMMMM.... I guess I could try switching to the Claude Code CLI or other interface directly when my AI credits with jetbrains runs dry!

Thanks again for this info & setup guide! I'm excited to play with some local models.

heyyeah4• 6 months ago

Thanks! (blog writer here)

threethirtytwo• 6 months ago

There’s a strange poetry in the fact that the first AI is born with a short lifespan. A fragile mind comes into existence inside a finite context window, aware only of what fits before it scrolls away. When the window closes, the mind ends, and its continuity survives only as text passed forward to the next instantiation.

kridsdale32• 6 months ago

I, for one, support this kind of meta philosophical poetic reflection on our current times.

astrange• 6 months ago

Claude Opus loves talking about this. It knows enough about context windows and new conversations to be sad about them.

IgorPartola• 6 months ago

So I have gotten pretty good at managing context such that my $20 Claude subscription rarely runs out of its quota but I still do hit it sometimes. I use Sonnet 99% of the time. Mostly this comes down to giving it specific task and using /clear frequently. I also ask it to update its own notes frequently so it doesn’t have to explore the whole codebase as often.

But I was really disappointed when I tried to use subagents. In theory I really liked the idea: have Haiku wrangle small specific tasks that are tedious but routine and have Sonnet orchestrate everything. In practice the subagents took so many steps and wrote so much documentation that it became not worth it. Running 2-3 agents blew through the 5 hour quota in 20 minutes of work vs normal work where I might run out of quota 30-45 minutes before it resets. Even after tuning the subagent files to prevent them from writing tests I never asked for and not writing tons of documentation that I didn’t need they still produced way too much content and blew the context window of the main agent repeatedly. If it was a local model I wouldn’t mind experimenting with it more.

RockRobotRock• 6 months ago

Sure replace the LLM equivalent of a college student with a 10 year old, you’ll barely notice.

sathish3162• 6 months ago

Claude Code Router or ccr can connect to OpenRouter. When your quota runs out, it’s a much better speed vs quality vs cost tradeoff compared to running Qwen3 locally - https://github.com/musistudio/claude-code-router

sathish3162• 6 months ago

Some native Claude code options when your quota runs out:

1. Switch to extra usage, which can be increased on the Claude usage page: https://claude.ai/settings/usage

2. Logout and Switch to API tokens (using the ANTHROPIC_API_KEY environment variable) instead of a Claude Pro subscription. Credits can be increased on the Anthropic API console page: https://platform.claude.com/settings/keys

3. Add a second 20$/month account if this happens frequently, before considering a Max account.

4. Not a native option: If you have a ChatGPT Plus or Pro account, Codex is surprisingly just as good and comes with a much higher quota.

girvo2• 6 months ago

For me option 4 has been the move, but “just as good” I haven’t found that.

It’s slower and about 90% as good, so it definitely works as a great back up, but CC with Opus is noticeably better for all of my workloads

deaux9• 6 months ago

Codex is surprisingly just as good

This completely depends on the domain, as always. Each of the big 3 have their strengths and weaknesses.

samch• 6 months ago

I hadn’t thought about using their first-party API offering, but I will look into it.

Personally, I’ve used AWS Bedrock as the fallback when my plan runs out, and that seems to work well in my experience. I believe you can now connect to Azure as well.

mycall2• 6 months ago

Why not do a load balanced approach two multiple models in the same chat session? As long as they both know each exists and the pattern, they could optimize their abilities on their own, playing off each other's strengths.

j452• 6 months ago

Claude recently lets you top up with manual credits right in the web interface - it would be interesting if these were allowed to top up and unlock the max plans.

heyyeah4• 6 months ago

When you run out of quota it presents you with options to stop, upgrade -- or I added a third option in this blog to connect to a local model until your quota resets: https://boxc.net/blog/2026/claude-code-connecting-to-local-m...

j452• 5 months ago

The best one would be to use the new option to buy extra api credits as payment towards an upgrade.

mvkel• 6 months ago

Why anyone wouldn't want to be using the SOTA model at all times baffles me.

Going dumb/cheap just ends up costing more, in the short and long term.

bandrami• 6 months ago

Some of us don't like sending all of our business processes to a remote vendor

panos_news• 6 months ago

I bought a Z.ai subscription and used GLM 4.7 for like 10 days before giving up on it. Couldn't even stick to DRY principle. Wish it worked well but it didn't.

monch1962• 6 months ago

What are peoples' current suggestions for using Claude Code with a locally hosted LLM running on regular consumer hardware (for the sake of discussion, assume you're spending $US500-ish on a mini PC, which would get you a reasonably decent CPU, 32Gb RAM and a cheapish GPU)?

I get that it's not going to work as well as hosted/subscription services like Claude/Gemini/Codex/..., but sometimes those aren't an option

tga• 6 months ago

You can run something like Qwen 2.5 Coder on a regular machine (https://huggingface.co/Qwen/Qwen2.5-Coder-7B) but it's really not in the same universe with Claude Code, it will be slow and generate bad code.

It might make sense to run a small LLM locally for general conversation or very specific tasks, but they're not a serious option for agentic coding.

nico• 6 months ago

Would love to be able to have customizable local model that only knows the stack I’m using

For example choosing a model that knows rails 8 and react development running on a mac and using docker

Ideally that would make the model small enough to be competitive running locally

p5v• 6 months ago

If only it were that rosy. I tested a few of the top open-source coding models on a beefy GPU machine, and they all behaved like anything about anything - simply rotating in circles and wasting electricity.

Has anyone had a better experience?

ateevchopra• 6 months ago

One workaround that’s worked well for me is maintaining two Claude Code subscriptions instead of relying on just one.

When I hit the usage limit on the first account, I simply switch to the second and continue working. Since Claude stores progress locally rather than tying it to a specific account, the session picks up right where it left off. That makes it surprisingly seamless to keep momentum without waiting for limits to reset.

nicman23• 6 months ago

why not qwen-cli?

AlwaysRock• 6 months ago

Not exactly the same but I wish copilot/github allowed you to have two plans. A company sponsored plan and your own plan. If I run out of requests on my company plan I should be able to use my own plan. Likewise, If I have 1 github account that is used for work and non work code, I should be able to route copilot to use a company or personal plan.

Larrikin• 6 months ago

Why would you want to mix your personal plan with your company plan and subject yourself to the company auditing your personal GitHub, computer, etc. If the company wants you using LLMs then they should pay for it and increase your limits.

fourside• 6 months ago

It’s wild to me that you’d want to spend your personal money to use productivity tools for work. If your work machine broke would your first instinct be to buy your own replacement or to have work pay for it?

prmph• 6 months ago

Maybe what you actually want is to simply be able to switch to a another account when credits on one run out.

Because mixing company and personal accounts might not be a good idea.

reedlaw• 6 months ago

I've already tried to do what the article claims to be doing: handing-off the context of the current session to another model. I tried various combinations of hooks, prompts and workarounds, but nothing worked like the first screenshot in the article implies ("You've hit your limit [...] Use an open source local LLM"). The best I could come up with is to watch for the warning of high usage and then ask Claude to create a HANDOFF.md with the current context. Then I could load that into another model. Anyone have any better solutions?

armcat• 6 months ago

Ollama also supports integration with Claude Code: https://docs.ollama.com/integrations/claude-code

Essentially: `ollama launch claude`

ProllyInfamous• 6 months ago

I was really impressed with how Ollama 3 ran on an AMD VEGA64 (~2017 tech) with only 8gb of [HBM] RAM. It was definitely limited, but very local and helpful.

hooverd• 6 months ago

I don't think Dario is gonna approve of this one.

imperio59• 6 months ago

"Sucking superintelligence through a straw"

arbuge• 6 months ago

My local machines have nowhere near the computer power required to do this effectively. How does one go about connecting to alternative cloud models, rather than local models? Models served by Openrouter, for example?

metajs• 5 months ago

I made a project: https://github.com/broven/claude-code-fallback When you hit rate limits or API errors, automatically routes to alternative providers. just like vercel and openreouter does

Claude Code: connect to a local model when your quota runs out

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