This is a plus in my opinion.

Cache doesn't help you much when you are compacting every 5 minutes...

I was shocked at how quickly I ran out my $100/mo subscription with a single agent (sol medium).

Also, a lot of this work is verification to ensure that AI generated code does what is intended and is safe to merge and deploy. That verification work is critical and uses a lot of tokens.

My main worry is that we get to a point where they have something much, much smarter than anything public and access is gated by extraordinarily high costs.

That probably can't happen though right? Inference is surprisingly cheap compared to the training.

This model might be the first step in that direction, as competition heats up between OpenAI and Anthropic.

- Training data, harnesses, and processes, that drives the quality of the models. Several companies have those. Some of those companies are in China.

- Infrastructure and funding for running those. That's a scarce commodity currently, training the latest frontier models cost billions of dollars apparently. And if you don't have the infrastructure already and don't have the suppliers on speed dial, good luck getting anything.

- Infrastructure for running inference for running what comes out of those. Several of the key providers of this infrastructure are using their own in house chips for this now. At scale this means huge cost savings.

If you start from scratch without infrastructure, there are a bunch of open source things you can find. But beyond that, you'll have a lot of catching up to do. That's the very definition of a moat.

Which, honestly, is fine. A lot of juice to squeeze in efficiency and even if models got zero more capable, making the capability that is already here cheaper is a huge win for everyone (except Nvidia)

it's also why there have been so many calls for regulation and slowdowns.

I remember when bandwidth was super expensive and now it’s dirt cheap.

I've implemented multiple features side by side with Opus 5.5 and 6 Sol, and the Opus 5.5 results always have fewer high severity bugs and require fewer rounds of fixes to get it over the finish line.

If 6.1 Sol has actually matched Opus 5.5, I'd be very happy. However, benchmarks and real usage don't seem to agree in my own tests. So we'll have to see.

For coding specifically, I've found 5.6-Sol > 6.0 Sol > Astra.

For modeling and artwork, Astra has been great routinely outperforming Kimi.

This is reminiscent to me of what Anthropic pulled back in February with their adaptive thinking rollout.

I can't wait for technology to catch up to a point where we can rid ourselves of this oligopoly.

I tried out fable 5.1 the day it was released and coming from gpt-5.6-sol I was truly mind blown (both in terms of code and prose it was generating - outputs I could finally enjoy reading and looking at).

Then when opus 5.5 came out, again same thing + far cheaper and faster.

I went from using OAI exclusively the entire year to a point now where i haven’t touched one of their models in at least a few weeks now.

I think OAI has lost the plot. OAI models simplify have no taste. And I don’t mean in front-end design way (although that too). They have no taste in how the model writes code, how it writes prose, how it writes in-line comments, how it writes documentation, or how it even picks variable names. There’s just no taste throughout.

Anthropic models are very thoughtful and have so much taste all around.

And, of course, GPT-6 came out as Anthropic fixed a bunch of stuff with their models -- faster (via fewer tokens, and TPS for Sonnet), easier to work with, better results, cheaper (via pricing and, again, fewer tokens). I don't know if the timing and the suddenness of the improvement on Anthropic's side sharpened the vibes comparison this round, but Internet opinion went pretty clearly to Anthropic.

FrontierCode's results make it look like Sol-6.1 may slot in well where you'd use Sonnet or Opus's low effort.

One thing I don't think any of this reflects is that many well-specified coding tasks, including the self-testing and doing research and tracing out dependencies and so on, aren't really bleeding-edge now: Luna-5.6 and small open models handle them fine. Stuff like "why is this box dropping connections?" or "here's a thing I want you to model/figure out" can benefit from bigger models. But far from everything does!