3 ms·

Open source models that you can run locally are much more than 3 to 6 months behind. 6 months was the November inflection for Claude. No open source model is

Open source models that you can run locally are much more than 3 to 6 months behind. 6 months was the November inflection for Claude. No open source model is as good as Claude Opus 4.6.

simplyluke 5mo ago
> that you can run locally That's doing a lot of work here. The future I see isn't most companies buying hundreds of thousands in hardware to run models, it's them adding a line item to their AWS bill. Inference costs on the larger hosted open source models are dramatically lower than the frontier labs API pricing.
- apocalyptic0n3 5mo ago
  > it's them adding a line item to their AWS bill That's the future Amazon sees too. We just had a week long session with the AWS team and they pushed that to us multiple times.
- teiferer 5mo ago
  The future I'm seeing is AI coprocessors running inference locally in most devices that today have a CPU. Just look at how powerful your mobile phone has become compared to your desktop computer 15 years ago and compared to a main frame 30 years ago. The days of requiring a data center to run anything resembling opus 4.6 are already counted. (But the industry will fight hard to get people to keep paying the Claude tax.)
  - selimthegrim 5mo ago
    Counted but not yet numbered?
  - simplyluke 5mo ago
    I'm already running a google TPU over USB on an otherwise very cheap board to do local computer vision on a front-door camera since I wanted to get away from Ring and other cloud services for that use case. And yeah, that may be the ~decade world, but we're in the mainframe era of the frontier models. It's going to be more economical for basically any consumer, and most businesses, to pay someone else to host models for quite a while.
    - dom96 5mo ago
      Curious why you went for a custom solution. I am aware of at least one company that seems to ship devices with local computer vision (Reolink).
      - yurishimo 5mo ago
        I'm guessing that they are using Fargate which is an OSS NVR. It supports a little addon USB stick you can buy for about $30 that will run common computer vision tasks for object detection. Stuff that we've been able to do with WebAssembly and Canvas for a long time now.
      - simplyluke 5mo ago
        My experience over the past decade has been being subsequently burned by being reliant on one provider's ecosystem after another. This is great until Reolink starts doing something shady to pad the bottom line and then it's on to the next. I wanted the ability to run whatever cameras on a VLAN and own the stack.
    - lelandbatey 5mo ago
      A gaming PC can already host models that perfectly serve casual users who just want recipes, todo tracking, picture identification, etc. E.g. Qwen 3.6 35b which will run on a $650 GPU at 75 t/s (Nvidia 1660 ti 16GB). Said model will also run as a tool-calling coding model excellently (it's no Opus, but for a thing that once set up is just the cost of energy, it's incredible). It can type faster than you can, probably 10x faster, so with guidance it'll make you faster. And it's free. It's here. If folks want ChatGPT without a subscription, they can have it today on their computer. The only money to be made is in the high end models doing "serious business" work spanning 1M+ token contexts and massive uncertainty. Everything else is already set to be eaten by today's local models.
      - simonw 5mo ago
        The problem with models like Qwen 3.6 35B (which really is an excellent model) is that my expectations of what a model can do have gone SO high now. Here's a prompt I just ran against Claude Opus 4.7: > Use python3 to experiment with whether the SQLite3 authorizer mechanism can be used to detect an INSERT OR REPLACE based just on running an explain query without examining the SQL string itself Opus nailed it: https://claude.ai/share/c4212606-3fee-4b7c-bc97-505e0348ccac https://claude.ai/share/c4212606-3fee-4b7c-bc97-505e0348ccac I tried the same thing against qwen/qwen3.5-35b-a3b running locally in lmstudio, with the Pi coding agent. At first it looked like it was going to do great! And then it fell apart over the course of several tool calls: https://gisthost.github.io/?8ae2f842df619fb7fd8f1ccd82fe41c7 https://gisthost.github.io/?8ae2f842df619fb7fd8f1ccd82fe41c7 I'm used to GPT-5.5 and Opus 4.7 handling that kind of prompt without any problems at all.
  - gedy 5mo ago
    > But the industry will fight hard to get people to keep paying the Claude tax. I bet this will ironically be couched in "safety" reasons or regulation to get anti-AI folks on board, even if it favors the large incumbents.
  - ai_fry_ur_brain 5mo ago
    Magical thinking. I guess if your phone is going to have 128gb of dddr5 then sure. You people fundamentally don't understand the memory requirements for running inference. Your cute local models seem good enough because you have no standards and anything an LLM produces seems like magic to you.
    - teiferer 5mo ago
      > Magical thinking. I guess if your phone is going to have 128gb of dddr5 then sure. Why would it not? The typical new phone today has 16gb of RAM. 20 years ago that was somewhere around 32mb. Factor 512. It's not hard to see that we'll get there rather soon, especially if there is an application that provides demand. > You people fundamentally don't understand the memory requirements for running inference. You seem to be overlooking how fast things change in this industry, especially if tons o money can be made as a consequence. > Your cute local models seem good enough because you have no standards and anything an LLM produces seems like magic to you. Please don't generalize. I'm an expressed AI skeptic and have to deal with the bad consequences of AI slop every day. But you can't deny that there are enough applicationn areas where people have use cases and those will be much easier if things don't need a few round trips to a data center that sucks all the electricity and water out of neighboring communities.
      - saalweachter 5mo ago
        Eh, you're off by an order of magnitude or so on both ends. The iPhone 17 has like 8 gb, the Pixel 10 12. The original iPhone was 128mb, and the iPhone 6 from 2016-2018 was around 1gb; that puts the iPhone at around 8x RAM per decade, and puts us at 128gb in our pockets at around 2036 or so. (Incidentally, the big news in phone RAM is that a lot of new phones are dropping back to 4gb because of RAM shortages.)
  - enaaem 5mo ago
    Even when run on datacenters, it would be like current day webhosting. It is hyper competitive and it will be a race to the bottom. There is money to be made but not as much as investors hope. There will be datacenters in random countries like Kazakhstan because some oligarchs have found a free energy glitch (like with bitcoin mining).
- majormajor 5mo ago
  Buying "hundreds of thousands in hardware" sounds like a lot but many companies - especially software companies - already do that if they have 100+ employees. Running software in the cloud gives you certain reliability and scaling advantages that would be very hard to replicate locally. Running some code agents in the cloud vs local hardware, if the local hardware gets "good enough," breaks the other way - offline usage, alone, would be hugely valuable to many people and companies. It'd be very interesting to see where various players would decide to make a call "local is good enough" though. Buying the hardware isn't a small bet, if it's not something that ends up as part of your standard computer.
PunchyHamster 5mo ago
But one will be in few months. And then you have choice of paying say $100k for hardware and pay just power cost (or pay someone to do that for you), or pay way, way more for your team to have access to marginal improvement. And 5% worse model for 10% of the price of the bleeding edge will be worth it for majority of people
PeterStuer 5mo ago
Many business tasks do not need the latest frontier models. I have a production system running since early GPT-4o. It now runs with GPT-5.2, not for improvements, but because it is cheaper. I could invest in switching to a local model, I tried and it works well enough, but api costs for this task are so low, it barely scratches $30/month. So I am using the local machine for other things and leave the inference on OpenAI, for now.
jobs_throwaway 5mo ago
It depends what you mean by locally. I don't foresee running a model on my laptop anytime soon to power a coding agent. Far more likely is an infra team at my company operating an open source model on cloud infrastructure. When they're already paying $1000 / month / dev, it starts to pencil pretty quickly.
- wrsh07 5mo ago
  Is there any open model as good as opus 4.6 at any price?
  - overfeed 5mo ago
    How many problems require Opus-4.6-level performance? The "I'll accept nothing but the very best model for any task" thinking is perplexing to me. People got a lot done before Opus 4.6. In 6 months, would you be dissatisfied by Opus-4.6-level open-weight models, just because Opus 4.8 will be out?
    - strix_varius 5mo ago
      Not OP but I've been thinking about this a lot (like everyone ha) and I think my answer is, yes? I hope there's a "good enough" point but I don't think we're there yet. Like for me hardware got good enough several years ago. But while opus 4.7 is really good compared to everything else, it's not so good that I would use it at a discount over whatever is available in a few months. The improvement in quality, speed, and daily frustration is worth it to me... Spoken as someone whose employer is footing the bill, so take that with a grain of salt. I want to run my own local models, but I don't think that's feasible without lots of frustration until a few generations of frontier models are so good that they're almost indistinguishable for common tasks. Kind of like how MacBook pros have been for a while.
      - majormajor 5mo ago
        While I can imagine that I'd want to use Opus 4.8 over 4.6 for a fair number of things (at least if they can avoid further speed regressions), I also have noticed that certain types of failures seem to be systemic. Bigger context has been helpful for bootstrapping, but still doesn't fix problems of getting stuck on the wrong things - you can toss more things in the blender, but you don't necessarily know which way it'll slice them up in advance, or which things from them it'll latch onto. And output still seems to get into "blindered" states where important details get dropped - even though it'll agree very quickly when you point that out. As long as we're in that sort of "spit something out in local targeted manner, and then do a revision loop until tests are green" style of execution, bigger models haven't shown me the ability to really avoid finding non-optimal / subtly-broken outputs for complex problems. Using Cursor to hop between models, I've found Opus to be generally better at really tricky debugging than GPT 5.5 or earlier models, but not reliably better at execution because of these things. I'm not sure Composer 2.5 is quite there yet for the execution side, but it's getting pretty close to those other ones, such that I'm definitely still in a "debug and plan with slow, execute with faster ones" operating model for working on hard shit.
applfanboysbgon 5mo ago
Opus 4.6 is a February model. Every time this subject comes up it seems like people post intentionally misleading things and move the goalposts. The goalpost we've been bludgeoned with over and over again is that, in particular, Everything Changed in November 2025. That GPT 5.2 and Claude 4.5 were the inflection point. That is actually 6 months ago. And DeepSeek 4 is already there. > run locally You can't run DeepSeek locally on consumer hardware[1], but you can on enterprise hardware, and enterprise spend is the subject of this conversation -- and even if you aren't self-hosting, it doesn't matter, because you can just get your inference from one of the the many companies serving DeepSeek, who trivially undercut the pricing of OpenAI/Anthropic because they didn't have to spend hundreds of billions on training frontier from scratch but instead only invest in supporting inference, which is already profitable. [1] Since this misconception comes up all the time, I'll go ahead and pre-empt it: no, training a 32b parameter model on outputs from DeepSeek and running that locally is not "running DeepSeek", despite the hundreds of stupid articles and Youtube videos making that idiotic claim that they're running it on a 5090.
- simonw 5mo ago
  > You can't run DeepSeek locally on consumer hardware Maybe not DeepSeek v4 Pro, but I've run DeepSeek v4 Flash on my 128GB MacBook Pro using antirez's carefully quantized https://github.com/antirez/ds4 https://github.com/antirez/ds4 and it's impressive.
  - applfanboysbgon 5mo ago
    Oh sure, yeah, that's nothing to sneeze at either. I think unqualified "DeepSeek" should generally refer to the main model, though, especially in the context of GPT5.2-grade quality.
- zozbot234 5mo ago
  > You can't run DeepSeek locally on consumer hardware I'd qualify that by writing that you can't run it with ordinary, real-time speed and throughput. If all you care about is slow and high-latency inference, there's no reason why that shouldn't be feasible even on the cheapest miniPC around, as long as it can literally store the model weights and keep around the (rather small) context.
overgard 5mo ago
I keep hearing about this "inflection", but it feels extremely exaggerated to me. And yes, I was using it at the time. It got incrementally better, it wasn't that amazing.
- simplyluke 5mo ago
  I think the bigger shift was harnesses and the two ended up somewhat commingled in people's minds. Claude code was a lot of people's introduction to using coding agents that could do a lot more than copy-pasting from a chatbot or autocomplete.
- noman-land 5mo ago
  The tool usage + skills got markedly better and so did the thinking cohesion. Add 1m context windows and it was a very noticeable shift. Opus 4.6 quality for local inference would be revolutionary.
  - viking123 5mo ago
    1m context is garbage
    - nazgul17 5mo ago
      It's just a metric. If it can find a needle in a 1Mtok haystack, then it's likely good at coding within a 200Ktok context (or whatever, insert your number here, I'm just trying to make a point)
touristtam 5mo ago
[dead]
lukeasrodgers 5mo ago
This project argues that with appropriate harness, the performance gap between frontier and much smaller open weight models shrinks dramatically: https://github.com/antoinezambelli/forge https://github.com/antoinezambelli/forge. I haven't kicked the tires yet.
3fgf 5mo ago
[dead]
_3u10 5mo ago
Kimi is better.
myaccountonhn 5mo ago
I've been doing my work with OpenCode Go, with Kimi2.6. It is not as good as Claude Opus, but it's good enough to get the job done, and I never run out of tokens.
damnitbuilds 5mo ago
To be relevant to this discussion, models running on reasonably-priced local hardware do not have to be as good as the best. They just have to be useful enough that companies don't need the best. They are.
londons_explore 5mo ago
Deepseek v4 pro is damn close to Claude 4.6, and whilst you'll pay quite a lot for a rig able to run it, it is open source.

Open source models that you can run locally are much more than 3 to 6 months behind. 6 months was the November inflection for Claude. No open source model is as good as Claude Opus 4.6.

simplyluke 5mo ago

> that you can run locally That's doing a lot of work here. The future I see isn't most companies buying hundreds of thousands in hardware to run models, it's them adding a line item to their AWS bill. Inference costs on the larger hosted open source models are dramatically lower than the frontier labs API pricing.

apocalyptic0n3 5mo ago
> it's them adding a line item to their AWS bill That's the future Amazon sees too. We just had a week long session with the AWS team and they pushed that to us multiple times.
teiferer 5mo ago
The future I'm seeing is AI coprocessors running inference locally in most devices that today have a CPU. Just look at how powerful your mobile phone has become compared to your desktop computer 15 years ago and compared to a main frame 30 years ago. The days of requiring a data center to run anything resembling opus 4.6 are already counted. (But the industry will fight hard to get people to keep paying the Claude tax.)
- selimthegrim 5mo ago
  Counted but not yet numbered?
- simplyluke 5mo ago
  I'm already running a google TPU over USB on an otherwise very cheap board to do local computer vision on a front-door camera since I wanted to get away from Ring and other cloud services for that use case. And yeah, that may be the ~decade world, but we're in the mainframe era of the frontier models. It's going to be more economical for basically any consumer, and most businesses, to pay someone else to host models for quite a while.
  - dom96 5mo ago
    Curious why you went for a custom solution. I am aware of at least one company that seems to ship devices with local computer vision (Reolink).
    - yurishimo 5mo ago
      I'm guessing that they are using Fargate which is an OSS NVR. It supports a little addon USB stick you can buy for about $30 that will run common computer vision tasks for object detection. Stuff that we've been able to do with WebAssembly and Canvas for a long time now.
    - simplyluke 5mo ago
      My experience over the past decade has been being subsequently burned by being reliant on one provider's ecosystem after another. This is great until Reolink starts doing something shady to pad the bottom line and then it's on to the next. I wanted the ability to run whatever cameras on a VLAN and own the stack.
  - lelandbatey 5mo ago
    A gaming PC can already host models that perfectly serve casual users who just want recipes, todo tracking, picture identification, etc. E.g. Qwen 3.6 35b which will run on a $650 GPU at 75 t/s (Nvidia 1660 ti 16GB). Said model will also run as a tool-calling coding model excellently (it's no Opus, but for a thing that once set up is just the cost of energy, it's incredible). It can type faster than you can, probably 10x faster, so with guidance it'll make you faster. And it's free. It's here. If folks want ChatGPT without a subscription, they can have it today on their computer. The only money to be made is in the high end models doing "serious business" work spanning 1M+ token contexts and massive uncertainty. Everything else is already set to be eaten by today's local models.
    - simonw 5mo ago
      The problem with models like Qwen 3.6 35B (which really is an excellent model) is that my expectations of what a model can do have gone SO high now. Here's a prompt I just ran against Claude Opus 4.7: > Use python3 to experiment with whether the SQLite3 authorizer mechanism can be used to detect an INSERT OR REPLACE based just on running an explain query without examining the SQL string itself Opus nailed it: https://claude.ai/share/c4212606-3fee-4b7c-bc97-505e0348ccac https://claude.ai/share/c4212606-3fee-4b7c-bc97-505e0348ccac I tried the same thing against qwen/qwen3.5-35b-a3b running locally in lmstudio, with the Pi coding agent. At first it looked like it was going to do great! And then it fell apart over the course of several tool calls: https://gisthost.github.io/?8ae2f842df619fb7fd8f1ccd82fe41c7 https://gisthost.github.io/?8ae2f842df619fb7fd8f1ccd82fe41c7 I'm used to GPT-5.5 and Opus 4.7 handling that kind of prompt without any problems at all.
- gedy 5mo ago
  > But the industry will fight hard to get people to keep paying the Claude tax. I bet this will ironically be couched in "safety" reasons or regulation to get anti-AI folks on board, even if it favors the large incumbents.
- ai_fry_ur_brain 5mo ago
  Magical thinking. I guess if your phone is going to have 128gb of dddr5 then sure. You people fundamentally don't understand the memory requirements for running inference. Your cute local models seem good enough because you have no standards and anything an LLM produces seems like magic to you.
  - teiferer 5mo ago
    > Magical thinking. I guess if your phone is going to have 128gb of dddr5 then sure. Why would it not? The typical new phone today has 16gb of RAM. 20 years ago that was somewhere around 32mb. Factor 512. It's not hard to see that we'll get there rather soon, especially if there is an application that provides demand. > You people fundamentally don't understand the memory requirements for running inference. You seem to be overlooking how fast things change in this industry, especially if tons o money can be made as a consequence. > Your cute local models seem good enough because you have no standards and anything an LLM produces seems like magic to you. Please don't generalize. I'm an expressed AI skeptic and have to deal with the bad consequences of AI slop every day. But you can't deny that there are enough applicationn areas where people have use cases and those will be much easier if things don't need a few round trips to a data center that sucks all the electricity and water out of neighboring communities.
    - saalweachter 5mo ago
      Eh, you're off by an order of magnitude or so on both ends. The iPhone 17 has like 8 gb, the Pixel 10 12. The original iPhone was 128mb, and the iPhone 6 from 2016-2018 was around 1gb; that puts the iPhone at around 8x RAM per decade, and puts us at 128gb in our pockets at around 2036 or so. (Incidentally, the big news in phone RAM is that a lot of new phones are dropping back to 4gb because of RAM shortages.)
- enaaem 5mo ago
  Even when run on datacenters, it would be like current day webhosting. It is hyper competitive and it will be a race to the bottom. There is money to be made but not as much as investors hope. There will be datacenters in random countries like Kazakhstan because some oligarchs have found a free energy glitch (like with bitcoin mining).
majormajor 5mo ago
Buying "hundreds of thousands in hardware" sounds like a lot but many companies - especially software companies - already do that if they have 100+ employees. Running software in the cloud gives you certain reliability and scaling advantages that would be very hard to replicate locally. Running some code agents in the cloud vs local hardware, if the local hardware gets "good enough," breaks the other way - offline usage, alone, would be hugely valuable to many people and companies. It'd be very interesting to see where various players would decide to make a call "local is good enough" though. Buying the hardware isn't a small bet, if it's not something that ends up as part of your standard computer.

PunchyHamster 5mo ago

But one will be in few months. And then you have choice of paying say $100k for hardware and pay just power cost (or pay someone to do that for you), or pay way, way more for your team to have access to marginal improvement. And 5% worse model for 10% of the price of the bleeding edge will be worth it for majority of people

PeterStuer 5mo ago

Many business tasks do not need the latest frontier models. I have a production system running since early GPT-4o. It now runs with GPT-5.2, not for improvements, but because it is cheaper. I could invest in switching to a local model, I tried and it works well enough, but api costs for this task are so low, it barely scratches $30/month. So I am using the local machine for other things and leave the inference on OpenAI, for now.

jobs_throwaway 5mo ago

It depends what you mean by locally. I don't foresee running a model on my laptop anytime soon to power a coding agent. Far more likely is an infra team at my company operating an open source model on cloud infrastructure. When they're already paying $1000 / month / dev, it starts to pencil pretty quickly.

wrsh07 5mo ago
Is there any open model as good as opus 4.6 at any price?
- overfeed 5mo ago
  How many problems require Opus-4.6-level performance? The "I'll accept nothing but the very best model for any task" thinking is perplexing to me. People got a lot done before Opus 4.6. In 6 months, would you be dissatisfied by Opus-4.6-level open-weight models, just because Opus 4.8 will be out?
  - strix_varius 5mo ago
    Not OP but I've been thinking about this a lot (like everyone ha) and I think my answer is, yes? I hope there's a "good enough" point but I don't think we're there yet. Like for me hardware got good enough several years ago. But while opus 4.7 is really good compared to everything else, it's not so good that I would use it at a discount over whatever is available in a few months. The improvement in quality, speed, and daily frustration is worth it to me... Spoken as someone whose employer is footing the bill, so take that with a grain of salt. I want to run my own local models, but I don't think that's feasible without lots of frustration until a few generations of frontier models are so good that they're almost indistinguishable for common tasks. Kind of like how MacBook pros have been for a while.
    - majormajor 5mo ago
      While I can imagine that I'd want to use Opus 4.8 over 4.6 for a fair number of things (at least if they can avoid further speed regressions), I also have noticed that certain types of failures seem to be systemic. Bigger context has been helpful for bootstrapping, but still doesn't fix problems of getting stuck on the wrong things - you can toss more things in the blender, but you don't necessarily know which way it'll slice them up in advance, or which things from them it'll latch onto. And output still seems to get into "blindered" states where important details get dropped - even though it'll agree very quickly when you point that out. As long as we're in that sort of "spit something out in local targeted manner, and then do a revision loop until tests are green" style of execution, bigger models haven't shown me the ability to really avoid finding non-optimal / subtly-broken outputs for complex problems. Using Cursor to hop between models, I've found Opus to be generally better at really tricky debugging than GPT 5.5 or earlier models, but not reliably better at execution because of these things. I'm not sure Composer 2.5 is quite there yet for the execution side, but it's getting pretty close to those other ones, such that I'm definitely still in a "debug and plan with slow, execute with faster ones" operating model for working on hard shit.

applfanboysbgon 5mo ago

Opus 4.6 is a February model. Every time this subject comes up it seems like people post intentionally misleading things and move the goalposts. The goalpost we've been bludgeoned with over and over again is that, in particular, Everything Changed in November 2025. That GPT 5.2 and Claude 4.5 were the inflection point. That is actually 6 months ago. And DeepSeek 4 is already there. > run locally You can't run DeepSeek locally on consumer hardware[1], but you can on enterprise hardware, and enterprise spend is the subject of this conversation -- and even if you aren't self-hosting, it doesn't matter, because you can just get your inference from one of the the many companies serving DeepSeek, who trivially undercut the pricing of OpenAI/Anthropic because they didn't have to spend hundreds of billions on training frontier from scratch but instead only invest in supporting inference, which is already profitable. [1] Since this misconception comes up all the time, I'll go ahead and pre-empt it: no, training a 32b parameter model on outputs from DeepSeek and running that locally is not "running DeepSeek", despite the hundreds of stupid articles and Youtube videos making that idiotic claim that they're running it on a 5090.

simonw 5mo ago
> You can't run DeepSeek locally on consumer hardware Maybe not DeepSeek v4 Pro, but I've run DeepSeek v4 Flash on my 128GB MacBook Pro using antirez's carefully quantized https://github.com/antirez/ds4 https://github.com/antirez/ds4 and it's impressive.
- applfanboysbgon 5mo ago
  Oh sure, yeah, that's nothing to sneeze at either. I think unqualified "DeepSeek" should generally refer to the main model, though, especially in the context of GPT5.2-grade quality.
zozbot234 5mo ago
> You can't run DeepSeek locally on consumer hardware I'd qualify that by writing that you can't run it with ordinary, real-time speed and throughput. If all you care about is slow and high-latency inference, there's no reason why that shouldn't be feasible even on the cheapest miniPC around, as long as it can literally store the model weights and keep around the (rather small) context.

overgard 5mo ago

I keep hearing about this "inflection", but it feels extremely exaggerated to me. And yes, I was using it at the time. It got incrementally better, it wasn't that amazing.

simplyluke 5mo ago
I think the bigger shift was harnesses and the two ended up somewhat commingled in people's minds. Claude code was a lot of people's introduction to using coding agents that could do a lot more than copy-pasting from a chatbot or autocomplete.
noman-land 5mo ago
The tool usage + skills got markedly better and so did the thinking cohesion. Add 1m context windows and it was a very noticeable shift. Opus 4.6 quality for local inference would be revolutionary.
- viking123 5mo ago
  1m context is garbage
  - nazgul17 5mo ago
    It's just a metric. If it can find a needle in a 1Mtok haystack, then it's likely good at coding within a 200Ktok context (or whatever, insert your number here, I'm just trying to make a point)

touristtam 5mo ago

[dead]

lukeasrodgers 5mo ago

This project argues that with appropriate harness, the performance gap between frontier and much smaller open weight models shrinks dramatically: https://github.com/antoinezambelli/forge https://github.com/antoinezambelli/forge. I haven't kicked the tires yet.

3fgf 5mo ago

[dead]

_3u10 5mo ago

Kimi is better.

myaccountonhn 5mo ago

I've been doing my work with OpenCode Go, with Kimi2.6. It is not as good as Claude Opus, but it's good enough to get the job done, and I never run out of tokens.

damnitbuilds 5mo ago

To be relevant to this discussion, models running on reasonably-priced local hardware do not have to be as good as the best. They just have to be useful enough that companies don't need the best. They are.

londons_explore 5mo ago

Deepseek v4 pro is damn close to Claude 4.6, and whilst you'll pay quite a lot for a rig able to run it, it is open source.