criley2

Considering that the biggest and most successful play for SpaceXAI in the AI space has been "Sell compute to Anthropic" I also wouldn't say that all of the grok junk has been obviously successful, either.

The way I think of it is that "20x" means "You get up to 20 tokens for each 1 if you run your limits down".

For example last week I put $1400 thru my Anthropic enterprise $200 plan. That's $5600 per month, or 28X the $200 my company pays (according to `npx ccusage`)

There is a whole side of sci-fi that has explored the idea that AI government could finally cure us so much more than just one class of corruption. There are, however, new downsides.

I use Opus 5.5 at work and I use Sonnet 5.5 at home. Sonnet 5.5 medium according to AA cost per task is $0.48 per task, while GLM 5.3 (max) is $2.01 (opus 5.5 high is $1.82). GLM 5.3 flash is $0.28 per task and Deepseek 4.1 flash 1017 is $0.26.

So it seems like Sonnet 5.5 medium, GLM 5.3 flash and Deepseek V4.1 flash 1017 are all in a similar space in capacity and price, however the $20 anthropic sub will get you like $200-$300 in sonnet 5.5...

Another bonus is that sonnet uses a fraction of the overall tokens and finishes much faster than the average chinese model, so your work goes faster (Sonnet 5.5 medium in 2 minutes, Deepseek 1017 in 6.5 minutes, GLM 5.3 flash in 13 minutes...)

I still don't get why paying for chinese inference makes sense. I should try Haiku 5.5 high/max because it's posting higher numbers than Deepseek V4.1 flash for half the price

That senile President didn't order a violent coup and call for violence on social media, therefore he didn't earn a ban.

He also didn't rape children and steal billions while destroying the world economy...

Hey, if you want to argue with the parameters about what stereotypes come out most strongly for that prompt, be my guest. But no need to shoot the messenger. LLMs are basically stereotype machines, it just turns out if that you make a trillion stereotypes, you get a lot right.

Honestly, my first impression is that the model believes that "software for a US persona" is less secure, and potentially more <something else> that wasn't tested.

Sounds about right. As we can see with the US approach to AI, it really is about maximizing the investment as fast as possible, maximizing the utility as fast as possible, and blowing past any concept of safety or responsibility.

I mean these models will have at times radically different output when the only difference between prompts is that one prompt is lowercased.

I'd love to see the reasoning traces because if you write "software for a US persona", it will try to fill in the blanks. What did the user mean? "US persona"? Prioritizing profits, advertising, and shipping new features over safety and responsibility? That's what many humans would assume you meant...

An obvious downside to plan mode is that without access to the edit tool, the model can't confirm its hallucinations.

Removing edit tool from your model is like lobotomizing your engineer.

Here's the secret: Just building the thing is the modern "plan mode". You don't have to keep what it made. But a completed, verified, and tested output is always more valuable to my planning process than a hallucinated sloppy guess.

No "may be" about it. There is no benefit to having bad models write your code, and there's no benefit to "plan" mode. Plan mode is an outdated 2025 concept that should be removed from standard harnesses.

There are 2 scenarios for dumb builder agents:

1. Your "planner" actually implements the project almost completely, finds all the gotchas, solves all of the emergent problems, and fully plans the solution.

2. Your "planner" takes a half-assed first attempt, solving none of the major problems that will arise during implementation

In scenario 1 -- just pay cache output on a few more tokens to finish the project. No need to spend twenty minutes pumping a few million tokens through deepseek regardless of how cheap it is to do so.

In scenario 2 -- good luck getting quality output

I do think this "software team" LARPing is an engineering version of AI psychosis. You can get all of the little parts humming along, sending messages, and looking really cool. You can even convince yourself that all of these busy little bees actually make better product.

But a frontier model working alone can produce better output in a tiny fraction of the time. Just build the thing.

What would you prefer, 30t/s 50k output tokens needed for solution, or 300t/s, 500k output tokens needed for solution?

This is why "tokens per second" alone is a deeply flawed comparator.

This is also why discussions of Chinese models focus on tokens per second- they struggle to compete on intelligence so they dramatically increase effort levels to compensate, meaning they require massive total output tokens.

For example, flash 1017 isn't benchmarked yet, but 0731 required 15.4M tokens to complete the artificial analysis benchmark, and Sol 6.1 high required just 2.8M.

Sol 6.1 uses 2.8M at 64 tok/s = 12h 9m

Flash 0731 reported 149 token/s at 15.4M needed = 28h 42m

And in my example in the first line, scenario A (30 t/s) completes 6 minutes faster than scenario B, despite 10X slower inference.