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I am finding that I am now less interested in better models than I am in token budgets. My issue with Anthropic models now is that I don't feel like I can rely
by madrox 1mo ago
I am finding that I am now less interested in better models than I am in token budgets. My issue with Anthropic models now is that I don't feel like I can rely on them as a daily driver because they'll dry up before my quota resets.
I am becoming dependent on AI to make a living, and I need predictable spend on it. If I know I can't use a model regularly all month, my enthusiasm is limited.
I urge Anthropic to get better at this aspect of their business so I can come back to it.
- ThouYS 1mo agoGLM 5.3-flash fits the bill
- andai 1mo agoWhat is it equivalent to? What kind of things are you using it for? I haven't tested it yet but on all the benchmarks it looks like it's 5-7x slower for agentic tasks.
- ThouYS 1mo agoI made some webapps with it, and have it running my hermes agent (which also does a lot of coding, but not webapps). Not sure what it's equivalent to, but it's super cheap and I am happy with the results
- glub 1mo agoIt's a mix of slightly worse kimi k3 for UI work and slightly smarter than luna for everything else. But yeah, it's very slow. I've put it to work as an LLM-as-RAG agent.
- andai 1mo agoI was wondering that, when DeepSeek became so cheap a while back, if it would be suitable as a superior embedding model. Although, RAG means search and search means latency?
- mark_l_watson 1mo agoand $0.15 1M input, $0.50 1M output I am a huge enthusiast of running local models, but when multiple quality USA vendors provide models like GLM 5.3-flash, I run locally just for the fun of it. For the purposes of comparing to Fable 5.1, I would mention GLM 5.3 that is about 1/12 the cost.
- george_max 1mo agoAgreed. The area I think will become more prevalent in the future for organizations are cost per intelligence -- effectively efficiency. An unoptimized model that costs 90x more than another that is only 10-15% less intelligent is something I would say is not a good deal.
- John7878781 1mo agoTry gpt 5.6 Luna max
- slopinthebag 1mo agooverthinks, been slow lately through the official api (slower than glm 5.3 somehow), and tries to run every conceivable e2e test once it does literally anything. like yesterday it ran for like an hour to build a fairly basic frontend... i like luna and sol but it feels bad lately
- lgl 1mo agoI'm with you, for what I usually do most models are already more than enough. What I'm really keen on is better auto-reasoning so I don't have to constantly have the constant inner debate on which reasoning effort to pick for each task. I seriously hate the none-low-medium-high-xhigh-max-ultra etc that we have now, with companies frequently recommending different ones on each new model release, etc. It's apparently called Adaptive Test-Time Compute or Dynamic Test-Time Compute and companies are apparently working on it (according to some LLM :shrug:)
- doodlesdev 1mo agoAdaptive reasoning is known to be an extremely hard problem to solve, though. It requires you to predict whether a certain LLM, with a certain effort level, with a certain prompt, will give you the right answer.
- lilytweed 1mo agoThis feels like exactly the kind of problem domain that belongs in (and can be solved by) RL?
- Rover222 1mo agoHave you tried Grok 4.6, if you're focused on token budgets? In a league of it's own for tokens/intelligence.
- jesse_dot_id 1mo agoNot for enterprise. Can't trust the company behind it with my data.
- Rover222 1mo agovery ironic if you think you can trust open ai over xai with you data. if you don't trust any, then at least that's a coherent position
- jesse_dot_id 1mo agoI do not trust any of them. I do not trust xAI the most.
- artdigital 1mo agoSuperGrok quota is garbage for anything coding. I burn through my quota in a few hours with very mild use. SuperGrok Plus is slightly better but doesn’t last me more than a few days. Even Claude Max feels leagues more generous in usage… I haven’t tried SuperGrok Heavy because it’s too expensive
- radium3d 1mo agoAll of the subscription AI platforms are trimming down quotas across the board to push users into higher tiers. Whatever they can do. Local inference needs to meet pricing sooner
- electriclove 1mo agoYeah I actually prefer Grok 4.6 but the quotas are so low that I find myself using Codex 5.6 sol medium on their $100 plan.
- kilroy123 1mo agoI literally only make it halfway through the week until my weekly usage runs out. This is using only Opus, no fable, and I'm on the max x20 plan. It's become ridiculous.
- oliver236 1mo agojust buy two 20x, no?
- bel8 1mo agoor switch to codex
- solenoid0937 1mo agoThese comparisons are meaningless I use Opus every day and easily have most of my weekly limit left over at the end of the week
- pbasista 1mo agoIf you just say that you run out of tokens, it does not mean anything about the token quotas themselves being reasonable or not. That depends on how much you use it. For instance, if you had 10s of agents running all the time, it is not that unexpected that you run out of tokens quickly.
- zmmmmm 1mo agoI'm curious what your methodology is that results in that? Are you running multiple teams of agents all adversarially reviewing each others code? Lots of different projects in parallel? I've only rarely maxed things out and then it's t through doing extreme things.
- kilroy123 29d agoYup, it's a lot of reviewing. I'll have Claude do a very exhaustive review. It's the only way I can get it to produce decent results.
- indemnity 1mo agoI am on the Claude Max 20x plan, and this still happens when using Fable 5/Opus 5. I would run out of weekly quota in 2 days, whereas Opus 4.8 would last the entire week, and sit at about 80-90% at the end.
- robgough 1mo agoSame here. Over the last two weeks I switched back to Opus 4.8 and turns out that still seems to last the week like it used to. These new models must be eating tokens.
- mirekrusin 1mo agoInvest a bit of your time into optimising usage cost. Anthropic has first class docs, actually read it or ask llm to read them all for you and summarise most important points / ask to to reflect it on your .md files. Maybe silly thing like dropping your default thinking effort by one level or adding (sub)agent pinned to other model is all there it to completely fix it or maybe you have instructions that encourage big dumps in CLAUDE.md/AGENTS.md that needs splitting so progressive disclosure works correctly? Naively sending everything to the most expensive model on high thinking effort is anti pattern and will drain quota quickly. My personal guess is that it's one of those. With effective context engineering it's hard to use all 20x quota, the limit becomes your own attention and time really. You may argue that you're doing multiple, parallel extreme effort tasks – which may be true but then again, there will be results to actually look at sooner or later and that takes time.
- agile-gift0262 1mo ago> I am becoming dependent on AI to make a living IMO, if you depend on AI to make a living, I'd invest in hardware for local inference, and learn on how to effectively make a living using AI inference you control, on hardware you control. Sure, economically speaking it's way cheaper to use one of these heavily subsidised services (for now), and their models are faster and more capable, but if your livelihood depends on AI inference, and you are renting AI inference, you are a being a serf of the tokenlord. And your livelihood depends on the whims of the tokenlord. They can increase rent prices, they can decide you can no longer do whatever you are doing, and you have no recourse, because you are dependant on them to make a living.
- madrox 1mo agoThere are a lot of things in my toolchain pre-AI that I did not own and relied on to make a living. Mobile developers are in even worse shape, and iOS developers doubly so. The idea we were somehow less beholden before AI, I think, is silly. None of us can wholly do our trades without support. Local inference is a fun idea, but you'll be out-competed by the serfs, as you call them.
- NewJazz 27d agoYou listing things that make developers dependent doesn't mean they weren't less dependent before. Now they have all those things, and more. It is debatable whether local inference is a competitive disadvantage. One key advantage is consistent performance. No unexpected model downgrades or yanks, no silly safeguards imposed, and no quotas is a lot of advantage. And by the way, it is not an either-or decision. You can use local inference as your daily driver while still leaning on frontier models when you get stuck.
- rafaelmn 1mo agoExcept there's a huge gulf of self-hosting and using API hosts - no way you can reach the economics of a shared host. Privacy is a problem but you can chose who you host with and where it's hosted (which jurisdiction). When privacy/compliance really starts to matter it's up to the client/business to provide you with tooling - you're not running that on your own hardware anyway. So the local AI for individuals is just a hobby/gimmick at this point not a rational decision. Self-hosting for business is a different story.
- replwoacause 1mo agoI moved off my $200 max plan with Anthropic because of this. OpenAI gets it though.