Give Terra 6.1 Low a spin in places where you'd have previously used Luna Max. Costs seem similar, due to Terra being much smarter: thus requiring far fewer tokens per task.
Looking solely at AA's intelligence index, recognizing the pitfalls with using that as a measure- Luna 6 Max > Terra 6.1 Low is 51.3% cheaper, 94.1% less output tokens, for 1.56 higher index score.
Im surprised, when I've worked through benchmarking things I've run into comparable costs for Luna Max vs Terra Low. Also anecdotal from me, of course.
They're both great- but I feel like for folks like me that are working on company's pseudo-infinite token budget, it's really a bimodal game. Im either reaching for the best cheapest for fanned out benchmark runs, or the fastest best for everything else. 6.1 Sol ultrafast is the latter for me, so Terra doesn't really compete with it. Terra has certainly replaced 6 Luna for me, however.
(And yes Astra is a thing, but if im considering Astra, im usually opting for an Anthropic model, 90% of the time.)
I use Luna xhigh for when I use it, Luna Max spends 40+ minutes overthinking. I've also found that Terra below xhigh will just flat out not do what it is told.
Yea, this problem is really clear when you look at output tokens for xhigh vs max. I think the jump in capability from Luna xhigh to terra low is really sold by this. I would say that this is just the nature of being post Terra 6.1, pre-luna 6.1
Are you not able to /login in pi with your Anthropic enterprise account? Or- do they need users in software that they can manage with enterprise controls?
I appreciate you getting communication out in front of this fast.
> "but we are looking into what we can do to prevent this"
I would presume that CC's auto-review of tool-call turns would already solve this >99% of the time, no? From my read, any harness-side deterministic solution here would be saving the user from themselves, which seems like a bad pattern. Somewhat dulling the scalpel in my mind.
There’s a few possible approaches: making sure auto mode catches this, building in basic secret scanning, a few other ideas. Going to prototype a few of these to understand the tradeoffs better before we ship something.
I'm not a CC user myself, so I can't say that I have my head wrapped around exactly how the "yes + don't ask again" flow works. That said- from my (outsider) perspective, saving off the entire tool call [1] and its arguments is very weird. I would expect that the hit-rate following that for similar calls would be very small, which leads me to believe that the list either must be seeded into the auto-reviewer's context, or used in some other similarity-check engine for future calls.
If we've already crossed the bridge to being confident in a non-deterministic reviewer for auto-mode, then, I see no obvious reason as to why we couldn't have the proposed rules be generated by a model. If that were reality, then- all rules placed in settings.local could be secretless, with the confidence of whatever model we tell 'make sure the rule does not contain secret material, consider its destination as potentially public'.
If a given action requires a secret, and you'd like that action to be repeated in the future, how do you expect it to work without said secret included in the save (and without being entangled in secrets management tools)?
Yea good point- constraint of no secrets management tooling kinda makes this impossible. Ideally no action requires a secret in plain text. It would be neat if the harness could bundle its own secret manager, and requests for secrets ("hey I need this api token") are out of band, and placed in the store out of sight of the agent.
Otherwise you need a broker that can verify chain of trust for the workload, but that's significantly more complexity if you're pitching a harness/app.
https://www.nature.com/articles/s42256-026-01268-y/ This is probably what you're looking for. TLDR; weaker orchestrator + strong workers scored higher than the reverse. Caveat with that, old paper: "High-capability models: GPT-5, Claude Sonnet 4.5, Gemini-2.5 Pro; low-capability: GPT-5 nano, Claude Sonnet 3.7, Gemini-2.0 Flash."
With that said, there is evidence that we recently crossed over a line for 'best practices' when it comes to systems of multiple agents. Comparing the shapes of 'n agents working collaboratively' vs '1 agent spawning n agents to handle distinct work', Opus 5.5[1] and Opus 5[4] performs better with the former, Sonnet 5.5[2] and Opus 4.8[3] performs better with the ladder.
GPT Pro is the pro setting on OAI's consume chat model. It is only available online or API.
It's branded as a separate model, but really it's like an 'ultrathinking' mode of OAI's model.
The details are not public but it's parallel test time compute and multi search, aka the model reasons along different vectors and compares answers.
If 'feels like' a smarter model, even though I'm pretty sure it's a synthetic variation of Astra.
It is by far the 'smartest model' if you were to benchmark it, and many of OAIs released were based on this.
There are few or no benchmark scores on it because it's not suitable for harness testing or coding, it's almost designed to be 'one shot' or 'few shot'.
It takes 8-25 to answer, and if you give it a sufficiently sophisticated prompt, it will think for 1 hour.
It requires OAI's 'pro' account aka the $200/month one.
It's the best open secret in AI, I can't recommend it enough.
Oh wow, as someone who prides themselves on my depth of knowledge on the OpenAI docs, im ashamed I didn't know about this! I figured that Pro was a deprecated feature post 5.6 family release. Didn't realize Mode:Pro was something you could do with 6 and 6.1, thanks!
Hosted search service sounds like its referring to the alpha/v1 web_run hosted search api. Alternative to the hosted websearch tool, which is a tool- rather than api surface
Im sorry, but how could this possibly be used as reason not to use an on-host harness? If you own the code, and control the supply chain, then this is not a problem- because you can read it. Weird comment.
Looking solely at AA's intelligence index, recognizing the pitfalls with using that as a measure- Luna 6 Max > Terra 6.1 Low is 51.3% cheaper, 94.1% less output tokens, for 1.56 higher index score.
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