3 ms·

No mention of open weights anywhere in the piece, which is weird. Qwen, Llama, DeepSeek are months behind frontier, not years. If you're a European startup worr

No mention of open weights anywhere in the piece, which is weird. Qwen, Llama, DeepSeek are months behind frontier, not years. If you're a European startup worried about getting cut off from Anthropic's API in 2027, the real question is what the open-weight frontier looks like then. Probably pretty capable. That undercuts most of the doom scenario. Also, he concedes Mythos-level capabilities will be cheap next year, then handwaves it with "you need the best AI, not good-enough AI." For most use cases, frontier minus six months is fine.

wahnfrieden 5mo ago
Llama is not months behind GPT 5.5 Pro. I don't think Qwen or DeepSeek are either. edit: I'm specifically referring to the "5.5 Pro" model, not regular 5.5 with Pro tier subscription. Claude has no model available that's comparable to 5.5 Pro either.
- vasachi 5mo ago
  I’ve used DeepSeek 4 Pro through Claude. It’s fine. Plans are similar to what sonnet/opus make. Same massage-the-plan -> massage-the-code loop. Maybe the code is a bit worse, but that’s the “months behind” thing. The thing is, vast majority of code tasks aren’t a venture into the unknown. We as an industry for the most part build CRUD interfaces and dashboards. That can be achieved, with supervision, with frontier open-weights models quite well.
  - fwipsy 5mo ago
    I think maybe you are both right. Perhaps AI coding assistants just don't need to be all that smart in many cases, so open weights models are fine. At the same time, frontier models are advancing in other domains, like mathematics, where raw intelligence is a more important factor.
    - vasachi 5mo ago
      I can’t compare raw intelligence of these models, and I certainly can’t say anything about their advances in mathematics (without repeating press releases). But, erm, does it really matter? It’s not like some engineer somewhere will vibe-calculate how much weight a bridge can hold. Well, yes, someone probably will do that. But I’m pretty sure there will be consequences for the engineer errors in this vibe-calculations.
- lostmsu 5mo ago
  There's no evidence there's any 5.5 Pro model distinct from 5.5 xhigh or whatever. https://developers.openai.com/api/docs/models https://developers.openai.com/api/docs/models
  - zozbot234 5mo ago
    https://developers.openai.com/api/docs/models/gpt-5.5-pro https://developers.openai.com/api/docs/models/gpt-5.5-pro is a thing
  - wahnfrieden 5mo ago
    lol (tap view all on yr link or ask gpt to search for you next time)
rTX5CMRXIfFG 5mo ago
Affordability of hardware that can run local LLMs is a real factor, too. Not sure when RAM prices are going down, but with everything that’s happening and can happen in the world right now, it doesn’t look like it’ll drop in the near or medium-term
- wahnfrieden 5mo ago
  No one is going to run models that are comparable to frontier locally without spending enormous sums for use at scale or in large orgs. Even with cheap RAM, you will still need a very large budget for frontier-level capability. Open models that are competitive with frontier will be used on shared hosts.
  - jorvi 5mo ago
    Models have been capped out on training and (active) parameters a while ago, its tooling / harness that is making the big jumps in performance happen. And then you have things like DeepSeek with a pretty small KV cache. And with the extreme chip shortages for the next two years, there's little appetite for even bigger models anyway. Barring a breakthrough in scaling, the only direction the models can really go is smaller. Which will inevitably mean better performing local models for same chip budget.
  - zozbot234 5mo ago
    > No one is going to run models that are comparable to frontier locally without spending enormous sums for use at scale You can always run these models cheaper locally if you're willing to compromise on total throughput and speed of inference. For most end-user or small-scale business needs, you don't really need a lot of either.
    - 9dev 5mo ago
      It would be awful if running models locally became the primary way of using LLMs. On dedicated servers sharing GPUs across requests, energy usage and environmental impact is way lower overall than if everyone and their mother suddenly needs beefy GPUs. It’s the equivalent of everyone commuting alone in their own car instead of a train picking up hundreds at once.
sholladay 5mo ago
Open models are pretty good at this point but the problem is that they are limited by the tooling and infrastructure that surrounds them. For example, the last time I tried to set up web search with an open model, the experience was pretty bad.
BrtByte 5mo ago
Open weights undercut the absolute cutoff scenario. They don't fully solve the question of who gets the best model first, who gets enough tokens to use it heavily, and who gets to integrate it into sensitive workflows without waiting for permission
cubefox 5mo ago
Someone recently made a graph showing that the gap between US American frontier LLMs and Chinese open weight LLMs (including DeepSeek v4) is widening. Unfortunately I can't find it anymore. Update: GPT-5.5 found it. Article: https://www.nist.gov/news-events/news/2026/05/caisi-evaluation-deepseek-v4-pro https://www.nist.gov/news-events/news/2026/05/caisi-evaluati... Graph: https://www.nist.gov/sites/default/files/images/2026/05/01/1-Overall-AI-Capability.png https://www.nist.gov/sites/default/files/images/2026/05/01/1...
- lugu 5mo ago
  Someone is an official website of the united states gouvernement. I would prefer another source.
  - cubefox 5mo ago
    I think no other source exists.
- mordae 5mo ago
  Give it time. It's inevitably a logistic curve.
  - cubefox 5mo ago
    I believe logistic curves make no sense when you have Elo scores.
- tirpen 5mo ago
  This is propaganda, not data. If the Chinese government published a graph that said the opposite, would you consider that a serious and objective source?
  - cubefox 5mo ago
    If the methodology in the accompanying write-up did look credible, yes. Though I have significantly more trust in US agencies, like NIST in this case.
pu_pe 5mo ago
There are two problems with that scenario: 1. Your European startup will be competing with others using a much better frontier model. In a scenario where you already have other major disadvantages (access to capital, labor), you might be outcompeted 2. Open models have been keeping pace very nicely, but they rely on distillation of frontier models. If the race gets really tight, this could be affected so that the time gap grows larger (ie, it's very unlikely anyone but Anthropic is distilling from Mythos at the moment)
- pjerem 5mo ago
  > 1. Your European startup will be competing with others using a much better frontier model. If the small (and I'd even say, sometimes imperceptible) difference between Opus & DeepSeek v4 Pro is such a disadvantage for your startup, it's that your startup have an issue, not the LLM. At the end of the day, your startup is there to solve real problems and even before the LLMs, being fast at coding things have never been such a huge competitive advantage compared to marketing, sales, customer support, product vision ...
  - pu_pe 5mo ago
    The direction we are going suggests AI will also be used for marketing, sales, customer support and product vision. Besides, if the difference between Opus and DeepSeek 4 is so small and imperceptible, you are missing the opportunity to launch a startup on your own and compete with Claude Code.
baq 5mo ago
Open weights will remain open only if they’re significantly worse than the frontier weights. Before you challenge with benchmarks, consider the labs which release open weight models have internal testing and unpublished results.
- gpugreg 5mo ago
  > Open weights will remain open only if they’re significantly worse than the frontier weights. This makes the assumption that you earn more money by selling access to the model than by releasing the weights. That might be true for a company, but a US adversary might profit more from tanking the US economy. NVIDIA's stock dropped by 17% in a single day after DeepSeek-R1 was released, and the share of tech companies in the S&P 500 has only risen since then.
  - zozbot234 5mo ago
    Open weights don't really "tank" the U.S. economy though. To the extent that they change anything in the U.S., they simply incent the big model suppliers to move their datacenter capacity to smarter, more capable models that have no real open weight equivalent.

wahnfrieden 5mo ago

Llama is not months behind GPT 5.5 Pro. I don't think Qwen or DeepSeek are either. edit: I'm specifically referring to the "5.5 Pro" model, not regular 5.5 with Pro tier subscription. Claude has no model available that's comparable to 5.5 Pro either.

vasachi 5mo ago
I’ve used DeepSeek 4 Pro through Claude. It’s fine. Plans are similar to what sonnet/opus make. Same massage-the-plan -> massage-the-code loop. Maybe the code is a bit worse, but that’s the “months behind” thing. The thing is, vast majority of code tasks aren’t a venture into the unknown. We as an industry for the most part build CRUD interfaces and dashboards. That can be achieved, with supervision, with frontier open-weights models quite well.
- fwipsy 5mo ago
  I think maybe you are both right. Perhaps AI coding assistants just don't need to be all that smart in many cases, so open weights models are fine. At the same time, frontier models are advancing in other domains, like mathematics, where raw intelligence is a more important factor.
  - vasachi 5mo ago
    I can’t compare raw intelligence of these models, and I certainly can’t say anything about their advances in mathematics (without repeating press releases). But, erm, does it really matter? It’s not like some engineer somewhere will vibe-calculate how much weight a bridge can hold. Well, yes, someone probably will do that. But I’m pretty sure there will be consequences for the engineer errors in this vibe-calculations.
lostmsu 5mo ago
There's no evidence there's any 5.5 Pro model distinct from 5.5 xhigh or whatever. https://developers.openai.com/api/docs/models https://developers.openai.com/api/docs/models
- zozbot234 5mo ago
  https://developers.openai.com/api/docs/models/gpt-5.5-pro https://developers.openai.com/api/docs/models/gpt-5.5-pro is a thing
- wahnfrieden 5mo ago
  lol (tap view all on yr link or ask gpt to search for you next time)

rTX5CMRXIfFG 5mo ago

Affordability of hardware that can run local LLMs is a real factor, too. Not sure when RAM prices are going down, but with everything that’s happening and can happen in the world right now, it doesn’t look like it’ll drop in the near or medium-term

wahnfrieden 5mo ago
No one is going to run models that are comparable to frontier locally without spending enormous sums for use at scale or in large orgs. Even with cheap RAM, you will still need a very large budget for frontier-level capability. Open models that are competitive with frontier will be used on shared hosts.
- jorvi 5mo ago
  Models have been capped out on training and (active) parameters a while ago, its tooling / harness that is making the big jumps in performance happen. And then you have things like DeepSeek with a pretty small KV cache. And with the extreme chip shortages for the next two years, there's little appetite for even bigger models anyway. Barring a breakthrough in scaling, the only direction the models can really go is smaller. Which will inevitably mean better performing local models for same chip budget.
- zozbot234 5mo ago
  > No one is going to run models that are comparable to frontier locally without spending enormous sums for use at scale You can always run these models cheaper locally if you're willing to compromise on total throughput and speed of inference. For most end-user or small-scale business needs, you don't really need a lot of either.
  - 9dev 5mo ago
    It would be awful if running models locally became the primary way of using LLMs. On dedicated servers sharing GPUs across requests, energy usage and environmental impact is way lower overall than if everyone and their mother suddenly needs beefy GPUs. It’s the equivalent of everyone commuting alone in their own car instead of a train picking up hundreds at once.

sholladay 5mo ago

Open models are pretty good at this point but the problem is that they are limited by the tooling and infrastructure that surrounds them. For example, the last time I tried to set up web search with an open model, the experience was pretty bad.

BrtByte 5mo ago

Open weights undercut the absolute cutoff scenario. They don't fully solve the question of who gets the best model first, who gets enough tokens to use it heavily, and who gets to integrate it into sensitive workflows without waiting for permission

cubefox 5mo ago

Someone recently made a graph showing that the gap between US American frontier LLMs and Chinese open weight LLMs (including DeepSeek v4) is widening. Unfortunately I can't find it anymore. Update: GPT-5.5 found it. Article: https://www.nist.gov/news-events/news/2026/05/caisi-evaluation-deepseek-v4-pro https://www.nist.gov/news-events/news/2026/05/caisi-evaluati... Graph: https://www.nist.gov/sites/default/files/images/2026/05/01/1-Overall-AI-Capability.png https://www.nist.gov/sites/default/files/images/2026/05/01/1...

lugu 5mo ago
Someone is an official website of the united states gouvernement. I would prefer another source.
- cubefox 5mo ago
  I think no other source exists.
mordae 5mo ago
Give it time. It's inevitably a logistic curve.
- cubefox 5mo ago
  I believe logistic curves make no sense when you have Elo scores.
tirpen 5mo ago
This is propaganda, not data. If the Chinese government published a graph that said the opposite, would you consider that a serious and objective source?
- cubefox 5mo ago
  If the methodology in the accompanying write-up did look credible, yes. Though I have significantly more trust in US agencies, like NIST in this case.

pu_pe 5mo ago

There are two problems with that scenario: 1. Your European startup will be competing with others using a much better frontier model. In a scenario where you already have other major disadvantages (access to capital, labor), you might be outcompeted 2. Open models have been keeping pace very nicely, but they rely on distillation of frontier models. If the race gets really tight, this could be affected so that the time gap grows larger (ie, it's very unlikely anyone but Anthropic is distilling from Mythos at the moment)

pjerem 5mo ago
> 1. Your European startup will be competing with others using a much better frontier model. If the small (and I'd even say, sometimes imperceptible) difference between Opus & DeepSeek v4 Pro is such a disadvantage for your startup, it's that your startup have an issue, not the LLM. At the end of the day, your startup is there to solve real problems and even before the LLMs, being fast at coding things have never been such a huge competitive advantage compared to marketing, sales, customer support, product vision ...
- pu_pe 5mo ago
  The direction we are going suggests AI will also be used for marketing, sales, customer support and product vision. Besides, if the difference between Opus and DeepSeek 4 is so small and imperceptible, you are missing the opportunity to launch a startup on your own and compete with Claude Code.

baq 5mo ago

Open weights will remain open only if they’re significantly worse than the frontier weights. Before you challenge with benchmarks, consider the labs which release open weight models have internal testing and unpublished results.

gpugreg 5mo ago
> Open weights will remain open only if they’re significantly worse than the frontier weights. This makes the assumption that you earn more money by selling access to the model than by releasing the weights. That might be true for a company, but a US adversary might profit more from tanking the US economy. NVIDIA's stock dropped by 17% in a single day after DeepSeek-R1 was released, and the share of tech companies in the S&P 500 has only risen since then.
- zozbot234 5mo ago
  Open weights don't really "tank" the U.S. economy though. To the extent that they change anything in the U.S., they simply incent the big model suppliers to move their datacenter capacity to smarter, more capable models that have no real open weight equivalent.