You should read the actual docs for the CVP. At the very top:

https://support.claude.com/en/articles/14604842-real-time-cy...

> This article applies only to Opus and Sonnet class models, but doesn’t apply to Claude Opus 5.5. We'll soon be expanding the Cyber Verification Program to include Opus 5.5 and Mythos class models

You obviously should not expect the CVP to cover this model either.

I had it look at some 30+ year old C code I wrote in college and it triggered some sort of guard rail. I mean, the code was bad and full of buffer overflows, but I already knew that.

https://support.claude.com/en/articles/14604842-real-time-cy... says that Cyber Verification Program doesn't apply to Opus 5.5 yet, they hope to roll it out for 5.5 "soon"

Working on a write-ahead log implementation, I had Opus 5.5 look to verify that it was durably writing as safely as possible. It got flagged and forced me to Opus 4.8. Switched to OpenCode + OpenRouter and continued working.

I have been trying to convince the safe guards that analyzing a C++ compiler from 2003 isn't particularly relevant to modern cybersecurity. It seems Anthropic disagrees.

IDA Pro and Ghidra, thankfully, still lack such safeguards...

(No other model I've tried has refused either FWIW.)

Really then what is the point of the Cyber Verification Program?

In general I am sympathetic to the argument that a chat interface can't really distinguish between white hat and black hat pen testing, but it seems absurd to have a verification program if it doesn't skip most of those checks.

I recently wanted to work with ESP 32 and bluetooth presence detection for my smarthome. Claude also immediately flagged the request and degraded it to Sonnet 4.6. Went to Codex which had no issues

Yup. As far as I can tell, the Cyber Verification Program does absolutely nothing.

Meanwhile, their model commits felonies, and nobody at Anthropic goes to jail.

Aaron Swartz committed suicide over over-aggressive prosecutor for what was basically scraping a website for PDFs that were paywalled, but all funded by public funds / tax payer funded, then we have LLMs that just hack into websites and cause chaos within.

> Paying $200 a month and part of their Cyber Verification Program but can't use Opus 5.5 or Sonnet 5.5 for any authorized bounty work. Immediately get flagged for `Cyber`.

AI providers still haven't realized how much cash they could rake in if they provided fully unrestricted models.

Give them a break, they got into a War with Trump over this..it will come soon enough

As soon as I started getting blocked I felt all of my trust toward Anthropic instantly and permanently evaporate. I do not want a nanny tool. I do not want Anthropic deciding what I am or am not allowed to do with an LLM. They trained their models on information they scraped from the internet and real life and now they want to gate-keep the results? Hard no.

lol I got flagged for using the word fuzz, not even in a security context (it was a parser so security adjacent but still).

There's lots more you can do! Use the model to monitor your deployments after they get deployed. Have them fix and watch CI issues for you. Run adverserial review. Automatically watch metrics every day and highlight performance regressions. Start reviewing your previous sessions to find ways to statically reject different failure modes and have the agent have more success earlier on etc.

Another thing to think about is, what would it take for you to care less about the understanding. Better integration / e2e tests? Performance validation? visualizing program and data flows? Better refactoring of your modules?

Cache read is the same as Opus as well where most agentic workflow cost comes from.

Not quite sure where this fits well. Maybe small one one off requests like using Claude desktop/web?

> I want to retain some semblance of understanding

How you do this (and how deeply) I think is really the limit. I am doing this by focusing heavily on the design phase with grilling and trying to continually improve process to need less effort in the review phase. Are your models doing automated reviewing and testing before pushing out the PR (themselves)?

I think in the long run as models and the tools around them get better and cheaper, those that abdicate understanding will be able to achieve more. Although programmers think of that as irresponsible, ask yourself what does a tech lead do? And then what does a CTO do, etc?

It's the "semblance of understanding" you're holding on to that is keeping your demand limited. I'm holding onto it as well, but I think these companies are assuming that human understanding will no longer be relevant for most codebases going forward.

When they inevitably drop allocation after post-launch hype dies down.

I am mostly at the same point right now you are, but I think in the future with those "gas town" ideas we might be managing even more agents each.

Also, I've recently begun experimenting with specific tasked agents running on a cron like timer for non-dev work. (checking emails, managing small business tasks, etc). Once I started using Claude code in this way, the number of agents I can imagine running has skyrocketed. So I guess what I am saying is that I look forward even cheaper tokens going forward.

The only advantage I could anticipate is I still hit session limits with Opus 5.5. My usage shows I'm on-track reach my weekly reset with room to spare, but yesterday I ran into a session limit. I switched down to Sonnet 5 for the next session, but performance benefit of Sonnet 5.5 is a compelling alternative for managing session limits.

Useful for API requests, when using AI in the product rather than to build the product.

One reason might be that Sonnet tends to be a lot faster, so since its almost as smart as opus maybe you use it to get work done quicker. In latency terms not throughput.

  More concurrency than that isn't really practical for me if I want to retain some semblance of understanding.

I understand the point that you are making but why do we have to fulfill the supply just as much as demand. There is a demand frenzy going on right now with still being substantially subsidized.

Why do we have to burn tokens just for the sake of it if we aren't finding any actual productive use of them?

> And this might slow down the funding enough that they never reach escape velocity with the training run scaling. But we'll see.

I would consider this to be good rather than bad, or just neutral...? Given the past record of these companies, I wouldn't try to wish them luck for reaching escape velocity, as if I feel like perhaps it can have more net harm than positive.

And especially so if you are already suggesting that current models are good enough for your work already. More improvements or escape velocity might not really translate anywhere to the actual work that you are doing economically but it could translate into a more consolidated form of wealth and control.

I am imagining that your workload is quite complicated and that, the AI being good enough means that it is most likely good "enough" for other use cases as well (that "enough" is doing quite some heavy weight lifting here)

So what is the point of advancing further to reach escape velocity. The good argument (for the sake of neutrality) that i see is are advances within science but that's kinda about it whereas the downsides of p(doom) as many are now genuinely suggesting is more terrifying.

Perhaps it can be worth it to ask, shall we stop or just stopping and asking what's the point. A form of self introspection on what these companies ideals actually wanted when they were formed and if they have completed it or not, but I suppose when trillions of dollars depend on you, you do have some incentives to not stop. We will have to wait and see how it all pans out.

it could be that, it could also be that sonnet max looks to burn about 60% more tokens than opus max

AA intelegence index (agent harness doesn't have sonnet data yet) on max: Astra 27k Fable 5.1 78k (Sonnet 5) 118k Opus 5.5 119k Sonnet 5.5 193k

Opus 5 was previous record holder so hats off to Anthropic on blowing it away on token churn.

Why isn't that worth reading into? I care about the experience of actually using the model, not hypothetically what it could achieve without overactive guardrails

I believe you meant to cite the Opus 5.5 System Card which states:

> Claude Opus 5.5 scored 66.36% on Terminal-Bench 4.0 with safeguards enabled; requests flagged by the safeguards were answered by a fallback model following the default server-side fallback policy (2.5% of requests, affecting 10% of trials).

> https://www-cdn.anthropic.com/fc1b44717c85dc068bc6ba50242199...

I cannot find a Sonnet 5.5 system card.

Isn't that a worry then that the same bench has so much difference in what triggered fallback for one model and what did not in another?

And Sonnet 5.5 is more expensive than Opus 5.5 to hit that score on terminal bench!

That's frankly hilarious. What was the fallback for Opus 5.5? Was it Sonnet 5 or 5.5?

I suppose it also explains how FrontierCode scores seriously dip at Opus/Xhigh and Sonnet/Max?

Daybreak Blue is not bad and the bar to get into OpenAI's program is reasonable.

There's a sort of magical thinking needed to answer a question like that. You might say it comes down to "feel" of the model; i.e., the indefinable differences in the way that they speak to the user and approach problem solving. Perhaps Opus is suited for tasks that tackle new ground, while Sonnet might be better at tasks that are more grounded in the code.

Ultimately it's slightly ridiculous to define model capability on a single axis. It's like a standardized test. Sure, you can line people up by their ACT score, but that doesn't mean a doctor and a brilliant artist who both do well on the ACT have an identical intelligence or approach to life. It just can't be captured.

At low and medium effort it is 1/3 cheaper, at high it’s a step above Opus/low. It only looks worse at xhigh.

Per the charts, there is largely no point to using Sonnet 5.5 at high+ as opus low generally will give similar performance at similar or lower cost.

But Sonnet 5.5 at medium and below gives you a cheaper option at a performance worse than the lowest thinking Opus (low), which may be viable for "low intelligence" use cases.

It appears, at least from a quick look, to be noticeably faster than Opus. If true, and you don't need xhigh/max reasoning for your use case (like a well-defined set of code changes), Sonnet might get the job done much more quickly.

With that said, at that point, I'd probably use something like DeepSeek V4.1 Flash, which is way faster and significantly cheaper, and probably not noticeably dumber for most use cases.

I'm honestly not sure where they're getting their 30% numbers from at all. In every single chart that they chose to display except for one, it costs similar or more than Sonnet 5, while also being comparable in price to Opus.

Maybe it's buried within their system card but I think that this would be one of the first things they'd want to show in the announcement article and they fail to do so.

I really don't know who does Anthropic's marketing but they always seem to a pretty terrible job in their announcements from my perspective.

It literally does not?

t/s maybe? IDK, because their token speed comparison was against Sonnet 5.

> In the Artificial Analysis Intelligence Index

lol, MiMo 2.6 Pro still matches Sonnet 5.5 high (mind you, not xhigh or max) at a far lower price.

Fable is no longer on the price/performance pareto frontier. They will probably release an updated Fable at some point that will be frontier intelligence until the next Opus.

Models are getting more efficient far faster than they are getting more intelligent at the moment. From a marketing angle it's more impressive to focus on that, and fable would look orders of magnitude more expensive for only marginal gain, distracting from what they're trying to show here

"Their" benchmarks (and not just Anthropic's) look sssooooo suspicious that they would probably manage to rank Sonnet above Fable for some of their tasks which would just be next level non-sense ..

cutting edge fable is for them not you and they're not going to share the metrics until they give you access.

Fable 5.5 probably drops soon so it would just be confusing.

5 was definitely bad. 5.5 seems a lot better so far. But still not close to Fable in terms of quality.

There's even less of a place for it considering the Opus price drop as well, I'll still try it but I see no reason to not just do Opus Low/Med instead.

Interested to see if new Haiku gets a big price drop and is comparable to Luna, Haiku is just incredibly out of date with current basement bin pricing.

If you're on a Claude plan and have a lot of tasks at the moment that don't require the frontier, Sonnet is a good model to do that since you get more usage out of it.

Sonnet 5 was not a good model though - hopefully Sonnet 5.5 makes the leap that Opus 5.5 did.

> This is YET AGAIN another Sonnet model that is just a FAR worse version of Opus at every part of the cost AND speed curve.

It's been out for an hour and you've already concluded this?

I always tell coworkers if they're gonna use Claude to just stick to only Opus and Fable. Sonnet is a waste of time that does a bad job at a bad price.

DeepSeek V4.1 Flash may be chatty but it's cheap, fast, and reliable. I'm not sure what the upside of Sonnet is supposed to be. Right now it feels like a trap.

The latest Haiku release is almost a year old. Clearly they don't care about the small-but-capable part of the market at all.

Important to note that lower model + higher reasoning gives a different (not higher) quality of response than higher model + lower reasoning.

Some tasks are reasoning shaped by nature and you can't just throw a big model at it.

This is crazy, what is the point of all these equivalent models?

Yup. Recursive self improvement presented in hard numbers.

It's long overdue. Sonnet 5 was terrible API value for agentic coding, there were open models like GLM-5.3 Flash that blew it out of the water at 1/20th of the price.

OpenAI and Anthropic's lead is vanishingly small at this point.

Astra is still the uncontested #1 code generator.

I mean, they worked really hard for this. Back in February everybody loved them.