> 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.
zozbot234 3 hours 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.
0xbadcafebee 9 minutes ago [-]
[delayed]
reilly3000 3 hours ago [-]
Which takes a $20k thunderbolt cluster of 2 512GB RAM Mac Studio Ultras to run at full quality…
0xbadcafebee 7 minutes ago [-]
[delayed]
bigyabai 30 minutes ago [-]
"Full quality" being a relative assessment, here. You're still deeply compute constrained, that machine would crawl at longer contexts.
PlatoIsADisease 1 hours ago [-]
[flagged]
42 minutes ago [-]
zozbot234 1 hours ago [-]
70B dense models are way behind SOTA. Even the aforementioned Kimi 2.5 has fewer active parameters than that, and then quantized at int4. We're at a point where some near-frontier models may run out of the box on Mac Mini-grade hardware, with perhaps no real need to even upgrade to the Mac Studio.
PlatoIsADisease 53 minutes ago [-]
>may
I'm completely over these hypotheticals and 'testing grade'.
I know Nvidia VRAM works, not some marketing about 'integrated ram'. Heck look at /r/locallama/ There is a reason its entirely Nvidia.
sealeck 50 minutes ago [-]
Are you an NVIDIA fanboy?
This is a _remarkably_ aggressive comment!
teaearlgraycold 3 hours ago [-]
Which while expensive is dirt cheap compared to a comparable NVidia or AMD system.
SchemaLoad 2 hours ago [-]
It's still very expensive compared to using the hosted models which are currently massively subsidised. Have to wonder what the fair market price for these hosted models will be after the free money dries up.
cactusplant7374 2 hours ago [-]
Inference is profitable. Maybe we hit a limit and we don't need as many expensive training runs in the future.
paxys 35 minutes ago [-]
Inference APIs are probably profitable, but I doubt the $20-$100 monthly plans are.
teaearlgraycold 45 minutes ago [-]
For sure Claude Code isn’t profitable
bdangubic 3 minutes ago [-]
Neither was Uber and … and …
blharr 2 hours ago [-]
What speed are you getting at that level of hardware though?
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.
paxys 2 hours ago [-]
LOCAL models. No one is running Kimi 2.5 on their Macbook or RTX 4090.
DennisP 41 minutes ago [-]
On Macbooks, no. But there are a few lunatics like this guy:
Having used K2.5 I’d judge it to be a little better than that. Maybe as good as proprietary models from last June?
bityard 1 hours 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.
kamov 60 minutes ago [-]
What do you use local models for? I'm asking generally about possible applications of these smaller models
Gravey 1 hours ago [-]
Would you mind sharing your hardware setup and use case(s)?
CamperBob2 58 minutes ago [-]
Not the GP but the new Qwen-Coder-Next release feels like a step change, at 60 tokens per second on a single 96GB Blackwell. And that's at full 8-bit quantization and 256K context, which I wasn't sure was going to work at all.
It is probably enough to handle a lot of what people use the big-3 closed models for. Somewhat slower and somewhat dumber, granted, but still extraordinarily capable. It punches way above its weight class for an 80B model.
redwood_ 49 minutes ago [-]
Agree, these new models are a game changer. I switched from Claude to Qwen3-Coder-Next for day-to-day on dev projects and don't see a big difference. Just use Claude when I need comprehensive planning or review. Running Qwen3-Coder-Next-Q8 with 256K context.
zozbot234 52 minutes ago [-]
IIRC, that new Qwen model has 3B active parameters so it's going to run well enough even on far less than 96GB VRAM. (Though more VRAM may of course help wrt. enabling the full available context length.) Very impressive work from the Qwen folks.
58 minutes ago [-]
richstokes 28 minutes ago [-]
This. It's a false economy if you value your time even slightly, pay for the extra tokens and use the premium models.
bicx 2 hours 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 2 hours 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.
kridsdale3 1 hours ago [-]
"You must always take two steps forward, for when you are off the clock, your adversary will take one step back."
DANmode 41 minutes ago [-]
and you really should be measuring based on the worst-case scenario for tools like this.
nik282000 2 hours 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.
sathish316 10 minutes 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
alexhans 4 hours 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.
I've also made decent experiences with continue, at least for autocomplete. The UI wants you to set up an account, but you can just ignore that and configure ollama in the config file
For a full claude code replacement I'd go with opencode instead, but good models for that are something you run in your company's basement, not at home
2 hours ago [-]
drifkin 3 hours 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
vorticalbox 3 hours ago [-]
I have been using glm-4.7 a bunch today and it’s actually pretty good.
I set up a bot on 4claw and although it’s kinda slow, it took twenty minutes to load 3 subs and 5 posts from each then comment on interesting ones.
It actually managed to correctly use the api via curl though at one point it got a little stuck as it didn’t escape its json.
I’m going to run it for a few days but very impressed so for for such a small model.
cyanydeez 3 hours 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.,
alexhans 60 seconds 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... ).
d4rkp4ttern 3 hours 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.
When your AI is overworked, it gets dumber. It's backwards compatible with humans.
2 hours ago [-]
sorenjan 2 hours 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 1 hours ago [-]
Is there an easy way to fine tune? I havent tried fine tuning since 2024, but it was not trivial back then.
baalimago 5 hours ago [-]
Or better yet: Connect to some trendy AI (or web3) company's chatbot. It almost always outputs good coding tips
wkirby 3 hours 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?
israrkhan 3 hours 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 1 hours 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.
starkeeper 2 hours 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.
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 :)
RockRobotRock 31 minutes ago [-]
Sure replace the LLM equivalent of a college student with a 10 year old, you’ll barely notice.
TaupeRanger 3 hours ago [-]
God no. "Connect to a 2nd grader when your college intern is too sick to work."
eek2121 3 hours ago [-]
I gotta say, the local models are catching up quick. Claude is definitely still ahead, but things are moving right along.
bcyn 46 minutes ago [-]
Which models perform anywhere close to Opus 4.5? In my experience none of the local models are even in the same ballpark.
israrkhan 3 hours ago [-]
Using claude code with custom models
Will it work? Yes.
Will it produce same quality as Sonnet or Opus? No.
btbuildem 3 hours 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.
zingar 4 hours 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.
IgorPartola 1 hours 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.
raw_anon_1111 4 hours 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.
mercutio2 1 hours 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_1111 1 hours 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
mcbuilder 3 hours ago [-]
Opencode has been a thing for a while now
swyx 4 hours 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:
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
esafak 3 hours ago [-]
Or they could just let people use their own harnesses again...
usef- 3 hours 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)
esafak 3 hours ago [-]
End users -- people who use harnesses -- have subscriptions so that makes no sense. General API usage is for production.
usef- 3 hours ago [-]
"Production" what?
The API is for using the model directly with your own tools. It can be in dev, or experiments, or anything.
Subscriptions are for using the apps Claude + code. That's what it always said when you sign up.
eli 3 hours ago [-]
Production = people who can afford to pay API rates for a coding harness
usef- 2 hours ago [-]
Saying their prices are too high is an understandable complaint; I'm only arguing against the complaint that people were stopped from hacking the subscriptions.
LLMs are a hyper-competitive market at the moment, and we have a wealth of options, so if Anthropic is overpricing their API they'll likely be hurting themselves.
esafak 3 hours ago [-]
Production code, of course; deployed software. For when you need to make LLM calls.
threethirtytwo 2 hours 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.
kridsdale3 1 hours ago [-]
I, for one, support this kind of meta philosophical poetic reflection on our current times.
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.
I'm completely over these hypotheticals and 'testing grade'.
I know Nvidia VRAM works, not some marketing about 'integrated ram'. Heck look at /r/locallama/ There is a reason its entirely Nvidia.
This is a _remarkably_ aggressive comment!
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.
https://www.youtube.com/watch?v=bFgTxr5yst0
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.
It is probably enough to handle a lot of what people use the big-3 closed models for. Somewhat slower and somewhat dumber, granted, but still extraordinarily capable. It punches way above its weight class for an 80B model.
That might incentivize it to perform slightly better from the get go.
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.
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
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
For a full claude code replacement I'd go with opencode instead, but good models for that are something you run in your company's basement, not at home
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
I set up a bot on 4claw and although it’s kinda slow, it took twenty minutes to load 3 subs and 5 posts from each then comment on interesting ones.
It actually managed to correctly use the api via curl though at one point it got a little stuck as it didn’t escape its json.
I’m going to run it for a few days but very impressed so for for such a small model.
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.,
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... ).
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...
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.
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?
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.
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.
Thanks again for this info & setup guide! I'm excited to play with some local models.
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 :)
Will it work? Yes. Will it produce same quality as Sonnet or Opus? No.
I've been running CC with Qwen3-Coder-30B (FP8) and I find it just as fast, but not nearly as clever.
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.
This is with my regular $20/month ChatGpT subscription and my $200 a year (company reimbursed) Claude subscription.
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...
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
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)
The API is for using the model directly with your own tools. It can be in dev, or experiments, or anything.
Subscriptions are for using the apps Claude + code. That's what it always said when you sign up.
LLMs are a hyper-competitive market at the moment, and we have a wealth of options, so if Anthropic is overpricing their API they'll likely be hurting themselves.