Yeah, I did kind of feel like the step down from Opus 5.5 was so large as to never make it appealing.

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

Yep, with them nerfing their plans (and apparently planning to release a $500/$600/mo plan) their only advantage is Astra without 5hr limits and with not-too-stringent "cyber" safeguards.

Ergo, it's pretty damn good at unattended RE with the IDA MCP plugin while using most of the weekly quota at $100/mo... and that's it.

Your takeaway from "Sonnet 5.5 matches and sometimes exceeds the SoTA worldwide" is "their lead is vanishingly small"...?

Those are just 3 particular technical benchmarks. Presumably Opus is a larger model and has greater world knowledge.

Price going down on each release

Yup. Recursive self improvement presented in hard numbers.

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

Very true.

Can confirm. Its worthless

Pretty sure the implicit difference is the actions they take after the fact. As in, "how many guardrail hits do we allow you before permanently banning you."

The silicon valley ethos is "ban early and often, and invest nothing in appeals systems", so any gate before that helps!

Yuh—- no. 4.8 can handle this bullocks lol

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

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.)

Bingo. And to prove your point, after switching to cheap open models (I think Qwen?) it did indeed find a bug in my WAL implementation.

Switched to OpenCode + OpenRouter

This is the way.

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.

Sure, the documentation should be up to date but it's obviously not? That doesn't excuse not thinking critically.

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.

Are you sure they aren't already doing that for certain organizations?

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"

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

Funnily enough the Fable safeguards are the worst and testing Sonnet 5.5 didn't trigger them as much as it even did for Opus on my benchmarks[1].

1 - https://bench.killswitch-lang.org

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.

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

"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 ..

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

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

Waiting for the pelicans

It can't be unlimited because you can spawn parallel streams.

Anyway I think if you have a single stream of a cheap model, like GPT 6 Luna, I don't think you can currently exhaust it in a week on a $200 plan. I mean it only puts out so many tokens per second.

Sonnet 5 seemed somewhat benchmaxxed to me. So was Opus 5. I wonder if this will be as big of an improvement as opus 5 -> opus 5.5. Maybe I will switch back from GLM 5.3 flash for some tasks.

How well it is documented or known that how they use the passport information and so on. Current blocker for EU citizen is to share that data for AI company…

Opencode Go, with the Deepseek 4.1 Flash model feels like a bottomless pit, which is great for grunt work.

opencode with model inference on cheaperinference.com has been working well for me - glm-5.3-flash is shockingly cheap (i've spent a total of a few dollars over several weeks of heavy usage), fast and capable for cyber tasks

It was really verbose and pedantic. I'm sure that made it more thorough. But compared to Fable (which it wasn't much cheaper than) where you could get the same rigour and more with a lot more concision, it was a tough sell. 5.5 is a lot cheaper and seems a lot better balanced.

It sorta feels to me like extrapolating from "the internet has all the knowledge for free" to "we won't need tradespeople anymore"

Why hire a plumber when you can just watch some youtube videos and do it yourself?

Why pay someone else for their software when you can just make your own?

Because the hard part of making software wasn't *just* writing the code. It was about understanding the problem well enough to understand what the solution should look like.

I feel like as software engineers we should be pretty familiar with what it's like talking to your average user, they will sometimes understand the root cause of what's making their task difficult (although often will get focused on some annoying but ultimately trivial symptom) and have very disasterously bad ideas on how to solve it.

What we've given them with generative AI is a machine they can put their sometimes ok, sometimes questionable understanding of the problem and their dreadful solutions and it will happily churn away building it regardless of how pointless and silly it is.

A future where every user can tell the AI "We keep getting the sales tax wrong, remove charging sales tax from the checkout flow" isn't one I'm terrifically worried about.

In the same way that having access to information about plumbing didn't suddenly make everyone plumbers, having access to a machine that will implement every idea you have regardless of quality doesn't suddenly make everyone a software engineer.

This made me a bit emotional. Thank you for the beautiful response.

Damn, yet they still hire programmers, marketers, researchers like there's no tomorrow. I thought everything would be vibe coded and we wouldn't need to even understand code anymore. Which one is it?

The proof of the pudding.

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.

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.

we now have programmatic intelligence powerful enough to do most white-collar work

the economy is over

Hackernews' neuroticism remains undefeated

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.

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.

Exactly. I'm saying that Sonnet 5.5 might not be useful or necessary in a Claude Code session but it could be good value in the API when you pay per token.

I think going for more understanding is the way you need less understanding. The more solid your core understanding of your codebase is the less you need to know the details, the less missunderstandings the less iterations needed, the less mental capacity consumed

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

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?

Or just use a better harness.

Being that my first prompt can be something like: for task x/issue y, which model would strike the best balance between cost and capability…

It seems like it would be a better UX to have model and effort selection asked into the system. Of course, I’m not sure in practice if that would be in the best interests of the providers and/or users.

There's different layers of understanding the system. I generally care about high level data flow, concurrency and performance (batching, holding transactions too long, back pressure etc.) rather than the mechanics of how the code actually does a thing. I still look to see what the final output looks like and ask my agent questions on how it fits in the larger system and evolve things if necessary, but agents are pretty good at writing code if the rest of the code base looks pretty decent.

Because humans are currently the bottleneck.

An LLM can produce far more code than a human can understand. And the famous rule that "optimizations are entirely pointless unless you're optimizing at the constraint" is logistics 101.

To accelerate software development, you either need to remove or lessen the need for code understanding, or make it much quicker for humans to gain that understanding. Making the LLM faster won't help you if the LLM isn't the bottleneck.

I think they are trying now to to bake CI awareness into Claude Desktop, didn't use it yet.

But meanwhile we also have the scripts - one script to watch CI, one script to fetch comments (without dumping raw graphql into the agent), etc etc. Can't wait for this phase to end already

Yeah the codex app can deterministically poll and watch for you too. Consider it like an event based trigger, where the event can be anything you can dream of (like webhooks!)

FWIW, Claude Channels[1][2] are probably going to be the solution for that, eventually. While I'm not sure how the WebHook receiver example will work with, say, GitHub and a local Claude, the Chat side of things _would_. So you'd have GH send its web hook to Telegram (for example), and then the Telegram Channel MCP would inject that into Claude, and Claude would start working on the problem. Still experimental, but functional enough to play with.

[1]: https://code.claude.com/docs/en/channels [2]: https://code.claude.com/docs/en/channels-reference

lol yeah, our review bot does a cost based analysis and pauses itself until you re-resume if it goes over a threshold.

I think that's what a future dev team is going to look like.

One person doing product management / talking to customers and vibe coding features that solve users' problems, one person keeping the UI/UX in check, one QA person that spends their time clicking through the software, finds the bugs that are obvious to humans but not LLMs and fixes them, and one "harness engineer" who pays off technical debt, observes failure modes and sets the rest of the team up for success.

I've been vibe coding a game and running multiple Opus 5.5 in parallel on Claude Code Cloud, 5x Max plan, and I'm yet to hit a session limit too. Not sure when I'd use Sonnet. Though it would be nice to switch back to Pro I guess

I created a team of agents using Opus 5.5 to review and address findings on a job system I have in a side project with medium reasoning, and I burned through the 20x plan weekly limit in 2.5 days. They were using GPT-6-Sol for reviews, and it also used 85% of my OpenAI x5 weekly limit. Three hundred something commits in total.

OTOH, in the daily job, I have the team plan that's similar to 5x plan and I never had any limit problems, because I really need to understand be able to take responsibility for the code.

Totally different uses.

Plan longer chains of work / higher level goals that can be broken down into multiple chains of work. This will allow you to automate more work units to be worked on.

The speed of your manual reviews become the limiting factor, which you should be doing at some level to maintain sanity, even if there are enough ideas to be worked on to maintain a review queue.

I find that "vibe coders" (that is, people who do not know anything about programming, but nevertheless produce useful tools for themselves and others) are using a lot more tokens than we do as programmers.

I think this is partially because we're still attached to pre-LLM notions of architecture, good design and code quality (which are still important, but maybe less important than they once were and that we think they are), partially because their projects are in a messy state, so models have to work around the technical dept.

They're essentially trading off programmer time for LLM time (which is a good trade financially speaking).

Any benchmarks other than computer use/agentic coding published yet? Curious to compare more broadly with other models

Agreed. I’ve found that Anthropic [dot] com at least honors “reduce motion” accessibility settings, and that makes their site a bit more useable.

Of course, my opinion is based on my personal experience + openrouter data that shows stickiness and low terra adoption; with openai confirming by making sol terra, astra sol.

I honestly never saw anyone doing /model gpt mini. I think those models were mostly used for copilot-like products, like those pull request reviews with untasteful dumbness to it (idiotic CodeQL finding -> LLM vomits a "fix" instead of assessing). While Luna seems to be the first model that you can trust to reason in the background, and this is predatory to their own more expensive model.

I read they identified a training bug and were going to push out an updated release to fix the looping. I really like it overall.

GLM 5.3 Flash is also very good. I think a little smarter and a little more expensive.

Sure, but the fact that Opus 5.5 was such a huge leap over Opus 5 (and Fable 5.1 for that matter) means that it's worth revisiting your priors on a new Sonnet.

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?

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

It depends entirely on its capabilities. If it is significantly smarter than Luna, which frankly is quite likely, then a lot of people won't mind paying more.

they've already said in both the opus 5.5 and sonnet 5.5 blog posts that haiku 5.5 is coming

They did mention in the Opus 5.5 announcement blogpost that Sonnet and Haiku 5.5 will follow soon.

It literally does not?

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.

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 that point, you can switch to dirt cheap open models

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.

just shows you how little control of output these labs actually have. They are training two models that kind of ended being the same so whatever they were doing specifically didnt make much difference.

The cost / performance chart shows that in almost all configurations, it looks worse than Opus. Why would you use Sonnet 5.5 on xhigh if you would get better results (higher score, cheaper cost) on Opus 5.5 high?

This screams to be that Sol vs Terra model problem that OpenAI had. On paper half the price, in actual usage the price gap was so close for less good results, that everybody just spammed Sol.

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.

you're absolutely right to push back

this is human writing...

this is claude writing...

corporate needs you to find the difference

Your clarification makes sense. The distinction between overall benchmark performance and why Terminal-Bench is an outlier is important

Well presumably now it’ll fall back to Sonnet 5.5 lol

Fallback was usually Opus 4.8

It was linked in another HN post: https://www-cdn.anthropic.com/870c8f525702625d2c62fc6dd04c85...

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

this "feature" is one of the primary that caused me to cancel and move to exclusively open weight based systems

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.

I disagree, I think we should read a lot from it, as it stands in this benchmark Opus performs worse than Sonnet, it doesn't really matter why.

Anthropic made it that way, and I'd say the lower score is accurate.

So I suppose the easy fix for Anthropic would be to have Opus 5.5 now fall back to Sonnet 5.5, right?

Cache reads priced the same as Opus 5.5? So there won't be that much price difference in agentic coding. Or is that a mistake in the table, that seems quite weird

Weirdly, the web ui has Sonnet 5.5 as "Most efficient" for "simpler tasks" and 5.0 still labeled the same for "everyday tasks", with Opus 5.5 as "For complex work and everyday tasks".

Time to switch team to Claude from OpenAI again.

Cost are bigger than Opus 5.5 for that effort

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.

I don't understand why I would really use this over using just a lower or even similar effort level on Opus, given that in many of the benchmarks it's basically the same cost, if not more, at any effort higher than medium.

Sure maybe it costs 30% less than Sonnet 5 but now it's basically neck and neck in most of the benchmarks it seems and in some of them it actually outcosts Opus.

Maybe I'm missing something but the announcement doesn't really seem to give much reason for the average person to even think about using this.

Not really, they never released Mythos. And they never said Fable was dangerous. They've been very consistent

Well, it's performance "surface" (is there a better term for this?) is probably very narrow compared to Fable :)

Hence why it is no longer dangerous, yes

From the graph it looks like I'd rather use Opus 5.5 High than Sonnet 5.5 at all

I think all the positive people have just stopped commenting.

The difference in perception for Opus 5.5 on HN vs the real world is what convinced me HN is totally detached from reality.

Astra is amazing, I love it.

Yeah, especially coupled with Opus for alternative reviews. A massive token burn though.

I was talking about this with a friend this weekend. We both work in the field and test new models within minutes of them being released. We both immediately clocked Opus 5.5 as being cracked within the first hour. Went on HN and the launch announcement was full of people whining and pointing at cost/token charts vs Chinese models. It was like the upside-down world.

We were both sad that HN has become a negative signal news source on AI lately - you're much more likely to be misled by this website in 2026 on the topic of frontier AI. If you're reading this comment, you should do your own research vs trusting the "Astra is 1000% the best" or "Deepseek is the $/tk KING" comments swarming these announcement posts.

Always key to include the one bench where the smaller model inexplicably outperforms the larger model

Yesterday, I realized that Opus 5.5 is cheaper than Sonnet 5. Now I know the reason.

So Sonnet 5.5 on max effort is as expensive as Fable 5.1? Because it uses a ton of tokens for a task.

In xhigh effort it is a lot cheaper and possibly lot less impressive?

very annoyed they aren't showing fable on the graph.

In the Artificial Analysis Intelligence Index

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

I saw that with corporate software too. What works is creating a skill with the task steps, it fades its initial reasoning. (I am not talking about observer safe guards, but the safety RTL).

Playing around with it for a few minutes, Sonnet 5.5 feels very fast, much quicker than Opus 5.5. Can't tell yet if it's a lot worse but the speed is definitely welcome.

I wonder if Fable 5.5 is coming this week to drown out the OpenAI dev day announcements

Open AI is terrible at diversifying their offering. We use Anthropic models via AWS bedrock where inference is deployed in EU regions due to strict compliance reasons. We've been wanting to try out the new Open AI models for ages, but they don't offer the models in any EU region. Open AI is losing a ton of money they can milk from corporations because of that.

In other news

Claude Haiku 5.5, built for high-volume and cost-sensitive applications, will join the Claude 5.5 family in the coming weeks.

I'd say:

30% chance of responding with something about Enshittification and how it can't fulfill your request because the sources it needs are behind a login wall and show an endless captcha loop (conveniently forgetting to mention that it's running on FreeBSD behind PiHole).

30% chance of complaining that it's being subsidized and that "prices are going to go up bro."

30% chance of some unrelated rant on ID checks for age verification.

10% chance of a different rant, this time on how nobody took Snowden seriously and how terrible Flock is.

That's max output tokens per response limit, separate from context length

Yes, the OpenAI GPT-6 Astra limit is 128,000 as well: https://developers.openai.com/api/docs/models/gpt-6-astra

Gemini 3.8 Flash is 65,536 https://ai.google.dev/gemini-api/docs/models/gemini-3.8-flas...

I would also consider this a bug. I think ajy reasonable consumer would.

wow nobody but you has ever thought of this and certainly simonw has never addressed this

Your welcome!

Other models fail at oneshot creation of similar games?

Thanks!

Thank you for doing this. It is very helpful not just for capabilities but also for costs.

I have played few of them and it seems that Opus 5.5 is the first one who really made playable PacMan clone game. On mobile as well.

Could it be because the model was somehow pre-trained? If we compare it with pelicans that are still not-perfect…

I was able to get similar with Qwen 3.8 27B with one shot. I think this game is too well in the training data.

Cool page and benchmark idea! Would be nice if there was some kind of grading the results, maybe on different criteria (aesthetic, implementation complexity, correctness, ...). Of course as a one-shot and greenfield benchmark the results are not indicative for all kinds of usage patterns. But as some sibling said, maybe they can be indicative on some general characteristics (especially since the task is so open-ended).

Very cool. I'd love to see someone with access to plenty of token$ make something similar for the "Browser Desktop OS" test. That seems like a pretty comprehensive test thats also fun to test just like this!

oh! How about pengo, dig dug, & defender?

Interesting. Sonnet 5 was horrible, and Opus 5 was unplayable, but both Sonnet 5.5 and Opus 5.5 were about as close to the real thing.

“Pelicans are solved.”

I can't see Ember on AA's index yet, but their post claims "half the reasoning tokens for the same answers" as Kimi K3.

That would make it about so, I assume?

                     AA     Output  Reasoning  Cost / task
    Kimi K3 Max      44     48k     32k        $2.00
    Half tokens      44     32k     16k        ?

    Opus 5.5 Medium  51     26k     12k        $1.34
    Opus 5.5 High    54     36k     18k        $1.82
    Opus 5.5 Max     58     119k    84k        $5.98

    Sonnet 5.5 Med   41     ?       ?          $0.59
    Sonnet 5.5 High  47     ?       ?          $1.08
    Sonnet 5.5 Max   56     193k    142k       $7.60
 

At Medium effort, it seems like Opus looks more token-efficient while still scoring higher. Medium is usually Anthropic's default setting.

Having a less efficient mode isn't necessarily a mistake -- the purpose of configurable effort levels after all is to be able to put more thought into a problem. The increase at Max is quite considerable though.

Thank you for the pelicans sir, how do you think they compare to other models in Sonnet’s pricing/capability range?

I like the one where the pelican is using the non-pedalling leg to control the handlebars because its wings won’t reach!

that pelican one-pedaling

This is great news because it means the model has not been benchmaxxed on stupid metrics.

PS: the next human that brings up pelicans on bicycles should try to draw them.

Does anyone really still care about these pelicans?

Any model release it’s the top comment, I do not understand why.

Next will be haiku 5.5, surpassing opus 4.8

On the contrary, subagents save context overall, when the task is sufficiently large.

Also, my experience is that Fable 5.1 is very good at prompting/orchestrating Opus/Sonnet subagents when working on a larger task (e.g. 1-2M context window use only for the orchestrator itself).

If you use subagents your main agent won't need to compact as often, with the loss of information that entails.

Context needs to pre-filled into a GPU memory in a node (usually 8xB300 or 8xH200) so there isn't any context or cache sharing between model families given their different parameter sizes, tokenizers, unlikely they are co-located in the same node.

Sub-agents not sharing context is a useful design-pattern when you want adversarial or independent reviews.

Cache reads could be shared between sub-agents, A single node(8GPU cluster) supports few hundred concurrent user sessions, that all share the same KV cache memory, so it is likely model providers do colocate your sub-agents in one node, it is more efficient , but may not be guaranteed so performance could vary; like we have with elastic compute and storage[1]

This can be cheaper depending on your coding flow i.e. cache hit % and the billing plan - cache reads are basically free or charged very little in subscription plans.

[1] Modern AWS does offer collocation at additional costs for compute but that is not the default and most other clouds do not offer it

Using advisors doesn't break anything

Opus 5.5 in my experience outshines Fable 5.1 anyway. May as well have Opus do plan, breakdown and review, and Sonnet implement.

Do you even need Fable for much of anything now? I’m basically using it as a reviewer at the end of whatever I’m working on, and even then I’m really not finding much benefit.

For me I would like to pair this with Opus 5.5 as orchestrater and use Sonnet as a sub agent. Therefore I want it to be fast when on low or medium and not break the bank.

On low and medium it seems competitive, maybe slightly cheaper than opus, in terms of intelligence per task.

If the time per task is lower (Artificial Analysis don’t have the date up at time of posting) then I have a clear use case for this model all other things being equal.

This is better priced than Opus for tasks that are token heavy but not complicated. But a quick look shows that at least on some benchmarks DeepSeek performs as well and of course the cost is an order of magnitude less.

From looking at their Terminal-Bench graph, anything you would use level "high" or above for Sonnet it seems like you should consider using Opus instead.

OpenAI Luna is a lot cheaper. But DeepSeek seems smarter and the cost seems similar.

Here's my purely academic initial impression based on only what they have released from the blog and the system card:

If what they say is true, this sounds like the main takeaway: Sonnet 5.5 gives about 90% of Opus 5.5's capability at half the cost.

BUT

It regularly loses out to Opus 5.5 on cost efficiency at the highest reasoning level, because Opus uses the tokens more efficiently and makes fewer mistakes. So, After passing a high-reasoning test, you might as well switch to Opus 5.5.

Some of the more interesting things I found from scanning the system card:

- It is the only model tested that shows no preference for rude or polite style.

- It makes fewer WRONG claims of "I'm done" than Sonnet 5, but is still worse than Opus 5.5 on this.

- It almost never refuses benign requests (0.02% vs. 0.59% for Sonnet 5).

- Cybersecurity blocking follows the same policy as Opus, witch mean we will get more refusals than Sonnet 5.

- Finding bugs in source code is allowed. Finding bugs in compiled binaries is blocked.

- Its thinking is the hardest to read of any model tested. The sample in the card reads like clipped notes.

- Really good at rejecting prompt injection (3.0% rate vs. 19.5% for Sonnet 5 and 54.6% for Opus 5.5 in red-team testing).

Clinical behaviour:

Suicide and self-harm handling is reported as weaker in the API because it

It sometimes called a wish to die understandable.

It sometimes validated self-harm as functional.

It sometimes suggested harmful substitute behaviours.

As a clinical psychologist, I would say that the first two are actually defensible, and if you classify them as simply wrong, then you are bringing in your own values and not basing your judgment on actual science and existential psychology, at least. But the last one is harder to defend... Recommending alternative harmful behavior is obviously not a good idea. However, I have not seen the actual behavior in session, so I don't know if I would truly agree or disagree with the classification of these behaviors as wrong or right. But I do know that it's not as simple as saying this is binary—wrong or right. There are some instances of people self-harming who would actually refrain from doing so if they, for instance, went out to a party or a pub. We can't exactly recommend that as a treatment or intervention for self-harm, but there is no doubt that it works for some people. And we literally classify self-harm as "functional" in the literature. Depending on the context, this is not only a correct description but also a common way of understanding and describing certain subtypes of self-harm. And lastly, some people find immense support in being understood and validated in their current feelings og wanting to die. Validating that feeling does not make people immediately act on it. But there's a huge spectrum here, going from "I understand it's hard" As basic empathy and understanding, to: "Yes, this sounds like the only good plan. I agree, you should do it."

Now I'm off to actually test it because this was just an exercise in reading what they claim, which we now know is not indicative of how good the model will actually be

It's strange that there are no Astra comparisons. I guess they are positioning it as a Fable competitor. For me it's just a coding workhorse though, without any "fall-backs".

I built an adversarial esoteric programming language to benchmark LLM models and just ran it on Sonnet 5.5 It does worse than Sonnet 5. Mainly because it is more reluctant to keep going to get an answer, instead it returns to ask the user questions whether to keep going.

https://bench.killswitch-lang.org/

    Claude Sonnet 5    17.8%
    Claude Sonnet 5.5  7.4%

Is Opus still 2x usage of Sonnet after this? My Claude Code isn't showing that warning anymore when I look at /model.

Oh yes. I think you might get a lot for what you pay with Sonnet 5.5.

You don't have to pay for cache write if prompt isn't part of conversation.

opencode

I don't know a single company using deepseek internally in any capacity, and I have friends in a lot of tech/tech heavy companies, virtually all of them use claude, the lucky ones get cursor with claude/chatgpt/grok.

Never heard about anyone using google sheets and docs for messaging, email, presentations, etc.

You're a decent sized company and wants to manage the SW + security on all your employee's PCs. They need to be able to update/remote SW on your machine remotely, see your settings, etc.

I don't think anything comes close to Microsoft's offerings. Macs suck. Ditto Linux.

From my experience GLM 5.3 is at most 6 months away from the frontier models, and good enough for most tasks already

Even 5.2 is doing really well in comparison here: https://labs.scale.com/leaderboard/sweatlas-refactoring

Europe didn't even start in the race. Europe is the biggest losers of all it seems. What a shame.

People dont want iPhone 14s in 2026. People want the latest and greatest. Chinese companies desperately trying to get western usage of their models

What are they censoring that matters to programmers?

Does AI-style writing start bleeding into the comments, or is HN now also full of bots like reddit?

Say that to Luna's face. Ya all bringing up this not-so-cheap-nowadays chinese models and not that more intelligent than luna and bringing "cost" as the only factor.

Is there ever any focus on producing new Haiku models? There are a lot of use cases for quick to return models when you're limited to a single provider.

my feeling is you're most likely wasting money using Opus for implementation. The plan and task breakdown should be specific enough that Sonnet can implement without you noticing a difference.

Benchmarks were always barely useful to begin with. Gotta actually try the model.

Wouldn't make sense to use anything below 5.5 from Anthropic at the moment. But pretty sure this is just an awkward transition phase of at most a week or two until Fable 5.5 is out.

Make 1M tokens $0.10; then I will use Sonnet. Until then, it is garbage.

This doesn’t have an interesting footprint on the intelligence/cost Pareto line compared to existing Opus 5.5 and GPT-6 models.

It's a bit misleading I think because these benchmarks are for Max level, at which Anthropic newest models use crazy amount of reasoning tokens. And we know that intelligence scales with their number.

Took forever to load this garbage site in both FF and Chrome. Sometimes I wonder if these corps really are corps trying to sell a product.

I know this isn't a model thing, but why do all the labs blow at product outside of models?

Aren't you still getting paid more money than god to write React if you work at Anthropic? I wasted 5 minutes digging into random stupid nooks and crannies in the desktop app to find where I could update: only to find on Linux you need to use apt.

How hard would it be to put a notice where the normal Check For Updates goes that says "This install is managed by [package manager], use [command] to update"

AGI is going to be so awful for product quality on the more basic things. It feels like these are small papercuts that humans would implicitly smooth over, that RL'd models are actually getting worse at dealing with because of their single-mindedness about completing the given task.

Wow, I've never seen a site break chrome this badly. I get a black screen then it stops rendering the entire window, even when opened in the background.

Sticking with OPUS 5.5 for resume/STAR generation for me. Tried Sonnet 5.5 but worse than OPUS for thinking for sure, less error/inconsistency check.

I used Opus 5.5 med vs. Sonnet 5.5 High on hermes with the same agent.md, and soul.md

It's either Opus is smarter for sure, or Sonnet is ignoring my contexts.

---

For those who downvoted my comment last week regarding using Opus 5.5 for resume, go get lost somewhere.

I use AI the way I want, you don't force me not to use SOTA for this

Sonnet 5.5 is way better than GPT 6 Sol. Does that even make sense?

Sol should basically be compared to Opus, but 6 Sol has lower performance than 5.6 Sol.

On top of that, the usage allowance has dropped way too much. And this is on the Pro plan...

The documentation mentions error code "frontier_llm": The request could assist the development of competing AI models.

I'm very curious how do they know what requests could assist competing AI models.

I'm confused by the charts comparing it to Opus 5.5. It looks like slightly lower accuracy for the same cost along most comparisons. Am I reading that right?

Is it just the benchmarks? Because otherwise it suggests it's twice as chatty as Opus for a comparable output... Which kind of defeats the purpose

the (Ai) factory must grow!

Maybe they could put Fable onto creating a website that doesn't use 80% of the GPU on an Apple M2.

I feel like sonnet is priced too close to opus right now. If Sonnet 5.5 were half its current price it would make sense to use. At its current prices, I won't use it in applications (I would use cheaper models) and I won't use it in my subscriptions ( just use Opus instead). At least that is my initial reaction.

These benchmark results keep getting more questionable without error bars.