111–120 of 223 posts
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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.
LOCAL models. No one is running Kimi 2.5 on their Macbook or RTX 4090.
No one's running Sonnet/Gemini/GPT-5 locally though.
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 ac…
This completely depends on the domain, as always. Each of the big 3 have their strengths and weaknesses.
> 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.
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.
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Would you mind sharing your hardware setup and use case(s)?
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 w…
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> 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…
Until last week, you would've been right. Kimi K2.5 is absolutely competitive for coding. Unless you include it in "frontier", but that has usually been used to refer to "Big 3".
(At todays ram prices upgrading to that for me would pay for a _lot_ of tokens...)
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Which while expensive is dirt cheap compared to a comparable NVidia or AMD system.
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.
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> 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…
Until last week, you would've been right. Kimi K2.5 is absolutely competitive for coding. Unless you include it in "frontier", but that has usually been used to refer to "Big 3".
Kimi K2.5 is good, but it's still behind the main models like Claude's offerings and GPT-5.2. Yes, I know what the benchmarks say, but the benchmarks for open weight models have been overpromising for a long time and Kimi K2.5 is no exception.
Kimi K2.5 is also not something you can easily run locally without investing $5-10K or more. There are hosted options you can pay for, but like the parent commenter observed: By the time you're pinching pennies on LLM costs, what are you even achieving? I could see how it could make sense for students or people who aren't doing this professionally, but anyone doing this professionally really should skip straight to the best models available.
Unless you're billing hourly and looking for excuses to generate more work I guess?
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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.
Which takes a $20k thunderbolt cluster of 2 512GB RAM Mac Studio Ultras to run at full quality…
> 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.
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. Mig…
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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 w…
"Single 96GB Blackwell" is still $15K+ worth of hardware. You'd have to use it at full capacity for 5-10 years to break even when compared to "Max" plans from OpenAI/Anthropic/Google. And you'd still get nowhere near the quality of something like Opus. Yes there are plenty of valid arguments in favor of self hosting, but at the moment value simply isn't one of them.
I need to do more testing before I can agree that it is performing at a Sonnet-equivalent level (it was never claimed to be Opus-class.) But it is pretty cool to get beaten in a programming contest by my own video card. For those who get it, no explanation is necessary; for those who don't, no explanation is possible.
And unlike the hosted models, the ones you run locally will still work just as well several years from now. No ads, no spying, no additional censorship, no additional usage limits or restrictions. You'll get no such guarantee from Google, OpenAI and the other major players.