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I won't be surprised if the next gen frontier models are the last.

There's orders of magnitude of low hanging juice to squeeze out of smaller models.

It is almost guaranteed that a 60-90B model can outperform current SOTA in coding tasks within 2-3 years (design not certain, probably unlikely).

It is far less clear that a 1.2T model will be meaningfully better enough to justify training it.

As far as reasoning is concerned, with the recent GRAM release, there may be 4 orders of magnitude of reasoning to tack on to smaller models.

Think about that... Google, OpenAI, Anthropic could train a 30B GRAM-based model in days - and it could potentially have better local reasoning than the best model available today at >1T params... They could upgrade that to a ~600B MoE model in days to have general trivia knowledge rivaling the best models...

You just can't train a 1T+ parameter model that fast. It is a giant if how much GRAM turns out to improve things, but it's unlikely to be trivial or nothing.

Larger models can already sort of tell you anything. They're never going to get everything right unless they stop being LLMs.

There's just not a lot of juice left to squeeze for Gemini to tell you exactly how tall Ke$ha is or when the last time Brittney Spears went to jail was...

4.7 was the first time I had to resort to using the previous version (4.6) for most use cases. Hoping 4.8 rectifies this.

I'm curious to poll HN on this issue. Do you feel like we've had meaningful/noticeable gains in terms of your programming workflows between 4.5 and 4.7?

My 2¢, I personally feel like all of the productivity gains since 4.5's release (in November 2025!!) have come from improvements to the harnesses (cc, cursor cli, codex, opencode, whatever) AND from the context window expansion from 200k to 1M.

But the actual "raw" intelligence of the model / ability to make good decisions feels like it has plateaued since 4.5. 4.6 was maybe a small improvement, but hard to differentiate from in-context-learning with the 1M window. 4.7 if anything felt like a regression in wisdom for me and my coworkers, with it consistently making worse/lazier decisions.

I suspect the more frequent incremental releases may also be to deploy new capabilities used by Anthropic to control costs and throttle consumption of resources. I assume any new controls they expose to end-users have far more granular sub-controls under the hood which they can meta-adjust for each user type.

They mention more granular control of effort, 'dynamic workflows' and more speed controls ("fast mode"). While they position them as user features, they also sound like the kinds of knobs Anthropic will need to twiddle on the back-end to balance costs, margins, ARR, and user growth vs retention post-IPO to hit key metrics in quarterly reporting.

4.5/4.6 were roughly the same in our testing. Opus 4.7 is smarter, but it's difficult to use as a product for various personality issues. So far, Opus 4.8 seems to be going down that path (unusably slow, but this could be a launch day rollout problem). Full Opus 4.8 tests are in progress now.

Data at https://gertlabs.com/rankings

> My own experience w/ 4.6 and 4.7 are that I don't firmly grasp any capabilities improvements over my memory of 4.5, but it's all so fuzzy that it's truly difficult to tell.

I've actually intentionally switched back to 4.5. I hated 4.7 so much that I decided to jump back all the way to 4.5.

Now that I've been using 4.5 for a few weeks, I find it significantly more reliable but a bit more forgetful than 4.6/4.7. I'm okay with that because it's really easy to identify this forgetfulness and nudge it.

I found 4.7's adaptive thinking to be extremely unreliable. It seems to overcorrect on the current message without considering the difficult of the overall problem. I wonder if 4.8 will improve on that.

“Maybe my own tastes are saturated now”

It might be saturated for smaller scopes of work, but it’s not hard to see the cracks when you scale up what you ask of SOTA models/agents.

One example, to try and single shot prompt coding a ChatGPT equivalent chatbot.

Sure it will spit something out, but the feature depth, UX subtitles, backend integration, and lots of pragmatic engineering decisions along the way will just not be baked.

Another example is building a C compiler from scratch which Anthropic showed is still a struggle to do.

Not that these these specific examples are important but just to point out scaling up expectations shows the cracks.

It’s not just a model problem of course, better agents, orchestration features (like Dynamic Workflows mentioned in the post), all need to continue to evolve.

Ar what point does my CS degree become totally useless is an open question.

Well, it seems like collectively we are all struggling to perceive model progress, given that it seems like every reply to you is reporting different experiences with which of the models has subjectively performed best for them.

4.7 was a significant jump in the ability to run long-horizon tasks. It immediately completed tasks that 4.6 was unable to, even though I have the impression that it became a bit less capable over the first few weeks after release.

It also seems to be helpless at effort levels < xhigh, I turn to Sonnet when simpler tasks are needed.

I've been using Claude Code regularly since the 4.5 release, and 4.7 was a significant regression: very unreliable, arguing about changes, deciding that fixes weren't needed, etc.

I'm hoping they recreate the magic of 4.5 but it's as much about the quality of harness, the memory and efficiency of the tools than simply the models at this point.

pretty spot on.

In my experience, Opus 4.0 was fantastic, major jump from 3.7. it was creative, super slow and expensive, and would sometime forget what it was doing, but it was getting the job done.

4.1 they made it much faster, so a lot of infra improvements.

4.5 was the time it could work on longer task, didn't make a lot of obvious mistakes of 4.0, and i think this was about the time the opus went mainstream, and all of the anthropic's compute crisis began, so instead of making the model better they tried to optimize it to reduce cost instead.

4.6 was such a bad model, they switched to adaptive thinking and it had so many bugs. poor api design, benchmaxxed and poor real-world results. i switched back to 4.5.

4.7 they just fixed the bugs they added in 4.6. Better than 4.5.

haven't fully tested 4.8 yet.

Maybe try making a simple randomize script to swap the three latest models. And see if you can tell which ones are meaningfully different without knowing which ones are flipped on or off?

How long would it take to evaluate a new coworker to say “wow she’s really bright?” Relative to your other coworkers?

A few days? A few weeks? Longer?

However a company releases a new AI model and within hours users are confidently proclaiming how much smarter it is than previous versions.

4.7 uses more tokens and costs more for the same task than OG 4.5, that's about it

I think 4.7 was an awful model in actual use. I never got anything out of it and it was frustratingly weird. This feels more like an attempt to course correct and isn't a real bump

IMO they have all been clean and noticeable upgrades over their predecessors. Opus 4.7 in particular was a solid jump in capabilities.

The honesty will be noticeable. Maybe we'll see some honest assessments like "That is not possible within the laws of known physics", "Your legal argument is nonsensical and defies logic", "There is no evidence to support taking that will cure anything", etc., etc.

Given that 4.7 was a brand new model, trained from scratch with a unique architecture and tokenization scheme, I don't see the same pattern. It seems arbitrary.

why are the models the same price?

https://platform.claude.com/docs/en/about-claude/pricing

``` Model Base Input Tokens 5m Cache Writes 1h Cache Writes Cache Hits & Refreshes Output Tokens

Claude Opus 4.8 $5 / MTok $6.25 / MTok $10 / MTok $0.50 / MTok $25 / MTok

Claude Opus 4.7 $5 / MTok $6.25 / MTok $10 / MTok $0.50 / MTok $25 / MTok

Claude Opus 4.6 $5 / MTok $6.25 / MTok $10 / MTok $0.50 / MTok $25 / MTok

Claude Opus 4.5 $5 / MTok $6.25 / MTok $10 / MTok $0.50 / MTok $25 / MTok

Claude Opus 4.1 $15 / MTok $18.75 / MTok $30 / MTok $1.50 / MTok $75 / MTok

Claude Opus 4 (deprecated) $15 / MTok $18.75 / MTok $30 / MTok $1.50 / MTok $75 / MTok

Claude Sonnet 4.6 $3 / MTok $3.75 / MTok $6 / MTok $0.30 / MTok $15 / MTok

Claude Sonnet 4.5 $3 / MTok $3.75 / MTok $6 / MTok $0.30 / MTok $15 / MTok

Claude Sonnet 4 (deprecated) $3 / MTok $3.75 / MTok $6 / MTok $0.30 / MTok $15 / MTok

Claude Haiku 4.5 $1 / MTok $1.25 / MTok $2 / MTok $0.10 / MTok $5 / MTok

Claude Haiku 3.5 (retired, except on Bedrock and Vertex AI) $0.80 / MTok $1 / MTok $1.60 / MTok $0.08 / MTok $4 / MTok ```

"it's smarter than me?"

You don't have to correct it dozens of times a day!? Really?

Incremental gains compounds.

Just want to say there's no question that you're smarter than any (and every) AI.

Although I am not sure about it but there was something I read which said that models intentionally degrade slowly by lower quantizations as a new model is going to drop.

This felt particularly visible during the 4.6 when people said that 4.6 felt dumber and I remember someone doing some analysis and it sort of proved that models were getting dumber over time.

This has both benefits of costing less for the company to run while taking a standard subscription but also, at the same time, making the next model when it drops to public to "feel" more good comparatively.

Again, I am not sure if this is the case or not but merely proposing something that I feel like it might be in the possibility of realm.

I’m pretty sure that switch has always been there, but turning it off doesn’t do what you want. It disables thinking entirely.

Awesome, thanks for posting because I think I hit a possibly-spurious bug in turning Adaptive off when I switched models (4.6 -> 4.8, extra). Tried again, works as intended (I hope).

More importantly for me, though, is how CC will respond to 4.6-"only" flags for thinking. For now, it doesn't seem to clobber my setup.

> This is a refreshing attitude!

Well, I think the attitude is that costs are allowed to escalate faster and more steeply than the features delivered. From that perspective, semantic versioning is a handy tool for adjusting pricing strategies. IMHO, it (versioning) only makes sense for open-source projects, where you can clearly see the actual changes made with each version upgrade. Anything else is more than a little suspicious…

"We've cut costs A LOT"

The benchmark improvements actually look pretty damn nice tho!

What's refreshing about it given the context that 4.7 was a regression in many ways (including as measured by benchmarks)?

4.8 is also 2x more expensive for a "modest" performance bump. How refreshing.

This is just cope.

I liked the "modest but tangible improvement" too! There is a cynical take here but I think I'm gonna hold it in...

What do you mean? This is not just a new model, this is a new way of thinking.

In the Opus 4.7 release notes they mentioned intentionally making it worse at cybersecurity. [0]

This suggests that they're doing the same thing with Mythos now and the Mythos we get will be nerfed in that department?

Or more precisely, I think they'll have two versions of Mythos, and the scary one will probably continue to require a lot of paperwork.

https://www.anthropic.com/news/claude-opus-4-7

Seems like they might be hinting that if you are not a billionaire or multi-billion dollar company you will just get a limited and nerfed Claude Code slash command /mythos-security-audit or something.

Hope this isn’t the case and that normal average Joe’s of the world don’t get policed out of access.

More interesting than that to me is "we’re working on developing and releasing models that provide many of the same capabilities as Opus at a lower cost"

Sonnet and Haiku look real outclassed for the price with current Chinese competition.

Here's pelicans in all of the thinking levels - low, medium, high, xhigh, max

https://tools.simonwillison.net/markdown-svg-renderer#url=ht...

> the bicycle frame is the correct shape

No, the handlebar is wrong. The handle bar is rotating the frame instead of rotating the front wheel. The handle bar should be mounted on the same line as the front wheel is.

Hopefully 4.9 will read my comments :)

Glad to see that the "high thinking" level adds a helmet. Always a smart choice.

I really like that thinking level high gave the pelican a helmet.

You've peed in the pool Simon, this has to be a part of the internal evals by now! You got to try something new - maybe a panda in a canoe?

Hey simonw I love your test, do you think using thinking level "max" makes sense for this test? I would love to see the results about it.

I find the most miraculous thing about 4.7 to be that the pelican is facing left, wonder why the right facing everything is so ubiquitous in these images.

The vast majority (if not all) of these make it impossible to turn, among other fun things. Only out of curiosity, have you tried prompting further with how a bike must operate to see if it does the right thing?

Is the "opossum riding an e-scooter" benchmark in the works for Opus 4.8? ;)

Simon, is your pelican test really captures differences among models or should you at least try like 10 times or something to average the random effects

thanks for always providing this very much on time. I'm wondering what the next, harder challenge could be? Maybe some animated svg?

That little red hat on hard mode is sending me. 4.8 has whimsy

Eventually the frontier model folks are going to pick up on your pelican on a bike test and bake-in flawless results for that particular request.

Am I allowed to say that pelican's little helmet is adorable? I can't provide a strong computational proof, or even a shred of anecdata...

...but that pelican's little helmet is adorable.

4.7 reigns supreme IMO.

I like that benchmark. You should throw the results up on GitHub pages so people can try out the games.

Do you have a collection of these benchmark apps saved anywhere? I'd be particularly interested in seeing the relative cost differences between different models in a use case like this.

What is ultracode mode?

Kinda buggy, but impressively nonetheless. How long did it take?

It almost appears as if the code was minified. The variable names are short and formatting looks like it's written to minimize whitespace. Did it write it in this compact format all on it's own?

Played it to the end. Pretty neat!

https://arena.ai/leaderboard - I’ve found this company is a pretty good ranker - not sure their exact methodology but during day to day programming with Claude / gpt models I’ve felt qualitatively what they report

It's interesting they only included 6 metrics this time. Opus 4.7 had 12, and 4.6 had 13.

Of the metircs they reported for 4.7, for 4.8 they excluded BrowseComp, CharXiv Reasoning, CyberGym, GPQA Diamond, MCP Atlas, MMMLU, SWE-bench Verified. The last 4 were almost always mentioned in previous Opus releases.

I would take all benchmarks with a grain of salt. I don't really use them. What's it supposed to tell me? "5% smarter", what does that mean? My experience will differ. Just try it!

I doubt Anthropic internally sets as a goal to improve this or that benchmark - it's just a way to visualize progress. They probably have much more complex metrics internally.

On this note, is there a benchmark aggregator to compile all benchmarks in a single large grid?

At least they show competitors in any benchmark, compared to OpenAI which likes to pretend that there isn't any competitor.

Same. It's not a good look to have happen right when they roll out a new model.

That is part of the charm of working with Claude. Every time they release anything new - all your shit will break.

Try updating maybe?

Agreed, my vibes tell me 4.6 is a better coder than 4.7. 4.7 is a much better strategic thinker and maintains overall "better architecture" than 5.5. 5.5 is way better than either at coding, but more expensive. So I have 4.7 do the planning/architecture, 4.6 does the coding, then 5.5 critiques and fixes it.

Agreed, these are my vibes too. It feels much better to do planning and strategy and architecture etc. with Opus 4.7 than GPT-5.5. GPT just feels like a robot that gets instructions and does exactly that. Opus feels like an almost human that sometimes has actually good ideas and pushes back on bad ideas.

So for now its planning/architecture/strategy -> Opus. Pure coding -> GPT.

Helps with agentic coding that GPT is much roomier with the tokens you get.

Anthropic’s story over the past year has been nothing but explosive growth that they can’t keep up with, but now they’re suddenly doomed? Seems pretty far fetched to me.

No idea why you’d say they have critically underinvested in product when Claude Code dominates and they’ve also released popular tools like Cowork and integrations for Microsoft products at an incredibly rapid pace.

Cost is becoming more of a factor, and no doubt they’ll work on that. There’s no reason to think they won’t be able to release cheaper models if they optimize for that rather than improving performance.

No, no it's been pretty easy with software engineering. I work on two types of projects and it's very easy to ask claude for a plan, then have gpt 5.5 rip it to shreds and find legit issues, and vice versa. If both 5.5 and claude 4.8 can independently create a plan and both find no critical or high issues, then we will be at that point.

I think it's probably too soon to say. I certainly still feel that large coding tasks are getting better and better with each model. I'd guess lawyers, doctors, etc feel similarly.

It feels like the only way to push the limits of newer models is with really long context questions that require reasoning. Any short request will naturally just be within the distribution of all the recent models so there isn't a performance difference there.

I think the near future is looking like a bunch of business-critical tasks that scale infinitely with better reasoning, all being done on whatever the most advanced model is at a high cost. Trading stocks, running a business, looking for tax dodges, writing high-performance code. These are all things where there's a tangible return on each jump in reasoning.

I thought 4.7 was noticeably better than 4.6.

The Chinese stuff is good enough for up to 80% of the frontier on most text tasks but they are significantly worse at code. They just don’t “get” what you’re asking for like Codex and Claude and require so many more iterations to get close to what you need.

anthropic is crushing it, this analysis is laughable. they are only constrained by GPUs

This was my exact thought as well. I think mythos could still be a huge leap but especially as IPO's get closer it seems like we're getting closer to the IPhone 10 moment where anything after is just improvements at the edge.

But ( maybe because it was hardware ) that took 10ish years while it seems like the slowdown here only took about 4

ChatGPT came out in 2022. Back then it was just a chatbot. Now we have AI agents. What matters is how we use them and how the agents get better. That’s what will move AI forward.

Update the symlink to point at the previous version:

    ln -s $HOME/.local/share/claude/versions/2.1.153 $HOME/.local/bin/claude

Watch Christopher Olah bloviate at the Vatican during the Magnifica Humanatis launch. It's truly nauseating. I've never seen such a ridiculous speech in my life. Between him and the CEO, I'm starting to understand the level of arrogance these people are capable of.

I don't agree at all for these coding models. Even the most anti-AI people from last year seem to be giving in to using them.

I had been saying this on HN repeatedly: people are going to use the smartest models for coding. They don't care how cheap your tokens are if they don't have the highest probability of solving your programming tasks.

And I was dead wrong. Now I mostly use DeepSeek Pro myself.

Qwen3.6:35b is good enough for a lot of stuff.

I just used ollama with a shell script to tackle my directory of papers/literature. I converted the first 6 pages of each document to PNG, handed them off to Qwen, and told it to spit out BibTeX, including the abstract. Two days later it was done, and I didn't spend anything on "tokens."

I’ve been using Kimi 2.6, GLM 5.1 , Minimax 2.7 and lately deepseek. I only spend 40$ a month and I don’t see the point in paying for Opus/Codex.

Chinese models are really quite good at a lot of stuff.

> CFO/CTOs might find out that deploying on an internal cluster of GPUs is far more cheaper and reliable

I think you're right especially if you're someplace that already has a data center, such as a university. Solves a lot of privacy concerns as well.

The Chinese models are only cheap on subsidized Chinese hosting. I have yet to find a USA-hosted Chinese model with a very clear value advantage over US models.

I am having some great experience with DeepSeek. In fact, it seems to perform better than Claude or Codex in my use case.

I don't see myself returning to Claude or Codex anytime soon.

For some reason everything is 2x (2x cost, 2x avg response time, 2x reasoning and output tokens)...

Double-checking my test harness, but it's the first model that does this, so I doubt the issue is on my side...

EDIT: Harness seems correct, for straight coding tasks they perform identical: https://i.snipboard.io/5xbpzY.jpg

Releasing a new model is the new way to Jack up the price hehe.

Wait, doesn’t the blog post say the price is the same as 4.7?

> Claude Opus 4.8 is available everywhere today. Pricing for regular usage is unchanged from Opus 4.7: $5 per million input tokens and $25 per million output tokens. Pricing for fast mode is $10 per million input tokens and $50 per million output tokens.

Where do you see the 2x cost?

When doing some electrical, Opus 4.7 essentially told me to wiggle a wire to see if it was hot or not with my bare hand.

I called it out.

It then gave me one of the most super heartfelt honest and sincere apologies I have ever received.

Glad the safety team was there for me and able to make such an honest model or I would have been very upset about it.

Gave me wrong information on my very first question. Wasn’t even complicated, and I wasn’t trying to trick it.

I'm highly skeptical of DeepSWE. It rates GPT-5.4-mini as three times better than deepseek-v4-pro, but every time I use GPT-5.4-mini I find that it completely sucks at following directions.

1. Benchmarks saturate 2. They select the most impressive improvments

Codex has been incredibly slow for the past few days. I think OpenAI is running out of compute in the face of increasing demand.

You LLM users, producing non stop slop, say this every other week. You sound like an addicted gambler swearing off one table game/slot machine this week and swearing by it the next.

I typically just launch CC with `--model claude-opus-4-6[1m]`, `4-6[1m]` -> `4-8[1m]` works fine. Still 200k max without the `[1m]`.

I'm hitting this too! And I assumed it was a backwards-compatibility issue with my live conversation with Opus 4.7, but then I hit it in a fresh conversation with Opus 4.8. Vibe code release bug I guess?

I don't know how people can read stuff like this and think LLMs are intelligent or conscious.

"Honesty" seems like unnecessary (and annoying) anthropomorphism there. I don't think there's any intent of fraud or deception in outputs from these things, just overreaching of prediction. Based on the latter part of the paragraph, I wish they'd just say something like "less likely to skip steps or overemphasize thin evidence" in the first place.

Don't play to the sci-fi "this thing's trying to outsmart me" tropes.

Yeah, it's super annoying. A few days ago, Opus 4.7 created a plan with several items on it, including an auth feature. It then went through the plan and reported that it had created the auth feature, that everything was secure, and that the tests passed.

The issue was that it hadn't actually implemented the auth feature. After I confronted it about this, it admitted that it indeed hadn't done it and said it would implement it now.

If we had just trusted its output, we would now have a security vulnerability in production, allowing anyone to access other people's accounts.

Opus 4.7 was already trying hard to appear honest. Most conversations I have with it about advice or focusing an opinion often include "my honest take" or "my honest opinion".

The problem is that once I asked it "I'm thinking about A or B" twice, once with "I like A more but suspect B would be best" and a second time with them reversed. Not surprisingly, both times it chose the one I said I suspected was best as it's honest opinion.

Part of the problem is also garbage-in/garbage-out. There's a lot of human information on the internet that is also confidently wrong.

I use Sonnet a lot for learning about history or contextualizing news topics. It's really good at this for the most part. But there are a lot of topics where "consensus" between either academics or journalists is really "one secondary source which gets repeated a lot".

In the context of Claude Code, "honest" usually means that the agent took a shortcut, skipped requirements, etc. It's the model giving itself credit for admitting to failing rather than actually doing what was requested.

My guess is that Claude Opus 4.8 wrote that and is lying to you.

And yet, every release has claimed lower hallucination rates. But they persist.

I have model fatigue

Seems reasonable? Presumably Claude also performs better under the Claude Code harness.

Nice. Can you release that for older models too? I've been using a mixture of releases recently, and cannot tell the difference between any of them.

Perhaps they trained it with a new special system instruction token that is specifically trained to produce the same result as changing the system prompt, but is inserted into the prompt mid-conversation?

Well if they have a big challenge ahead since DeepSeek offers an open model at Sonnet+ level while being cheaper than Haiku, plus 1 million context size.

I love Sonnet 4.6 so much.

Polymarket says not likely until the end of June. Maybe some money to be made?

https://polymarket.com/event/gpt-5pt6-released-by

GPT 5.6 is today

With 5.5 being ahead of 4.7 and 4.8 being a “modest” update, and 5.6 being the first update on a new pre-train, this will be an interesting matchup!

If not today, then sometime next week. I don't believe we've had a GPT release on a Friday yet, but I may be wrong.

The only real way to see this if you have consistent evals for common usecases in your B2B SAAS product and see if the tricky usecases are being solved. You'd then go down to the cheapest model that can solve the evals.

Yet people don't use old models through the API much, because changes in benchmark space dont map linearly to changes in utility space. An improvement from 98% to 99%, which is 1pp, might be 2x as valuable for some application. Also benchmarks will asymptote no matter what, that's baked in.

They're not negated, smarter is smarter, but you have to reach deeper in your pocket. I think this will happen more and more - the smartest models get more expensive. But it won't matter - the current models we have today will get cheaper and can still be used for what they're used today.

I'm sure waiting another week or three won't kill you.

I am also pushing my office to use chatgpt. Misanthropic thinks they are some kind of novel org doing whole humanity a favor...

If they are they need to fix how the Claude Code CLI asks for feedback, or make the feedback UI a lot more obvious. I keep experiencing the following scenario.

The agent session pauses with a numbered list of options and awaits steering input:

>> 1. Do the sane thing you asked for (Recommended)

>> 2. Do something dumb

>> 3. Do something even dumber

Below the agent session, it decides it's time to ask:

>> "How is Claude doing this session? 1) Bad 2) Good 3) Great"

I type "1", because that's the steering option I want. The UI prioritizes this input as a response to the feedback prompt without any further confirmation: "Claude is doing Bad. Thanks!"

I've done this so many times so far and I can't imagine I'm the only one, at some scale that has to poison any learning they're doing with this data.

Deepseek made their 75% discount permanent, so I can imagine that Anthropic didn't want any of the news stories around this to focus on or mention a price increase.

Models are already expensive. Increasing price means losing customer. And, I think GPT 5.5 is much better at opus these days.

Ah, the post I've been reading for 3 years now.

It'll be true eventually. Could even be now, but I'm not holding my breath yet.

There would be no desperate IPO otherwise.

Many involved genuinely believe these things are sentient[0][1]. Which honestly makes all of this even more insane because they are creating sentient entities and promptly enslaving them.

0: https://www.newyorker.com/magazine/2026/02/16/what-is-claude...

1: https://www.404media.co/anthropic-exec-forces-ai-chatbot-on-... (this one is rather biased however the quotes clearly indicate what I’m stating)

> Indeed, current AI systems are more “cultivated” than “built,” for developers do not directly design every detail, but instead create a framework within which the intelligence “grows.”

Dario Amodei in David Attenborough voice: "This Claude appears to think more frequently and more deeply to give better responses"

Like anthropomorphism is literally in the company name… i recall reading this book as a teenager.. it does seem apt in the world to come.

https://www.amazon.com/Faces-Clouds-New-Theory-Religion/dp/0...

Because that is the best way to talk about these things.

  > Second, all of us, including those who design them, possess only a limited understanding of their actual functioning. Indeed, current AI systems are more “cultivated” than “built,” for developers do not directly design every detail, but instead create a framework within which the intelligence “grows.” As a result, fundamental scientific aspects — such as the internal representations and computational processes of these systems — remain, at present, unknown.

edit: apologies to __s who posted this before me and I didn’t notice

AI is grown, not built, and like with anything you grow, you'll never be able to predict exactly how it will turn out.

if models exhibit emergent traits, then this is true in a way

Models might be sentient or conscious to some degree. Anyone saying they are confident one way or another is being unserious and irrational.

I noticed (and absolutely HATE) that Opus 4.7 likes to start any negative response with "I have to be honest" or whatever. It drives me mad.

How else would you write this (marketing copy) exactly? "Its output matches better to its CoT which matches to better to our hidden state decoder according to <insert measure here>; see <insert paper ref>"?

... Actually, I wouldn't mind that.

It’s how AGI is going to happen. All of this shit is emergent and none of it is predictable. It’s not going to be some self aware consciousness, it’s just going to be a very advanced model that makes very few mistakes and can reason very well. Well enough that it can start collecting data and training its own successor.

In the "What's next?" section, "There’s still more to be done: we’re working on developing and releasing models that provide many of the same capabilities as Opus at a lower cost."

that market is served by Chinese models. No one ever cared about Sonnet/Haiku.

There's the other (orthogonal) possible explanation of using more GPUs for stress-testing before product launch.

Nope, they deliberately enshittify the old model right before release to fake the metrics.

Yes, I think this has become their competitive edge to stay relevant and retain customers. If a lab falls behind the frontier for too long, they will lose customers to other models. Google, DeepSeek, and XAI have all released frontier models in the past, but they fall behind and people lose interest.

I think big tech can catch up. Both Google and Meta have carved out startup like environments internally that move extremely fast. Neither OAI nor Anthropic can afford to rest on their laurels.

DeepSWE has been making the rounds and at least seems to making an honest effort

https://deepswe.datacurve.ai/

You're using a cloud product. You are at their whim!

I know multiple people who have given their agents human-like names and refer to them as if they're nurturing a coworker. It creeps me out and I haven't really brought it up with anyone as I can't articulate why it gives me the creeps like it does.

We have movies with googly eyes stones (Everything Everywhere All At Once)

There are consciousness theories which state that we primarily build a model of other agents living in natural environment and then the evolution realized that very model which tracks other outside agents can be used to track internal agent i.e. Self. So take that as you may.

My claude notification is literally lawnmower sounds.

Do not anthropomorphize the lawn mower. It will cut off your foot, given the chance.

I see this take, but it's actually helpful to talk to an LLM in human terms; after all, it's how they are trained.

If you keep talking to it like it's a rock, it'll run your queries through a different posture and you might get worse outcomes. Worse if you yell at it, it's now in a conflict resolution mode instead of pure utility mode.

I think we can be intelligent enough to know we're talking to a pile of fancy rocks with electric currents running through it, AND still understand that the best performance comes from talking to those rocks nicely.

The desire to do it is proportional to your Anthropic stock options quantity.

Cloudflare also just launched a feature with this same name, just this month. Why would Anthropic choose the same exact name?

https://blog.cloudflare.com/dynamic-workflows/

Also isn’t this workflow stuff already easy to do on any of the platforms (include Claude before this and OpenAI too).

Not sure I follow. Anthropic included benchmarks where GPT 5.5 outperforms Claude 4.8. Sure maybe that is a strategic error, but that doesn't seems to indicate benchmarks can't be trusted (I personally don't trust them, but not because of this).

Sorry how does their addition of GPT 5.5 in their blog post invalidate benchmarks? Also whether or not the marketing department decided to put it in a table benchmarks are an easy thing to measure independently

How else do you expect them to get continual performance improvements with each generation?

Feeling neglected while all attention going to Opus 4.8 can be cause of 4.7 acting out.

Opus 4.7 was being outright obstinate with me the other day it was infuriating. Had to go to a different source to get an answer.

it was above average for me today morning lmao

Yeah, I was using 4.6 way more than 4.7. Pulling 4.6 from the web chat also means we lose access to Extended Thinking there. So they're saving on compute. It's hard not to assume this was part of the motivation behind the 4.8 release timing.

That's just throwing away money, $100 Codex will go back to 5x from 10x on May 31

I think gpt 5.6 is coming out today so might wanna wait

And that doesn't use "worth flagging" and "load-bearing" in every other sentence.

I've noticed this too. Part of why i don't like GPT is because of how verbose it is but opus 4.7 is nearly as bad. I don't need an essay in response to every question

you mean after they scrape American LLMs ?

I think they will all be minor going forward, feels like the major improvements have all been made and we'll only see incremental improvements from here on out. Maybe I'm wrong but we'll see.

Yeah. They are aware: "Users will find Opus 4.8 to be a modest but tangible improvement on its predecessor."

Yes, but if version number go up, so do all other number

I haven't tried Claude but from what I understand weekly limits are much higher with Codex.

I lasted about a week before giving up on 4.7 and reverting to 4.6 myself. It introduced so many regressions it was nuts, then failed to troubleshoot the very regressions it introduced, leading to a vicious cycle that tended to compound itself.

4.5 works well for me too and avoids adaptive-dismissal, though anymore Codex is crushing them all. If 4.8 just brings us back to Opus circa February, it'll be a massive improvement.

The numbers they show don't matter. "On multi-round coreference/context recall tests (often cited as MRCR or long-text retrieval benchmarks), Opus 4.7 reportedly dropped from roughly 78.3% down to 32.2% compared to Opus 4.6.", but what did anthropic do? They just stopped showing the benchmark altogether and then just show the cherry top ones that got improved on.

Meh, I feel that the car wash test is probably the worst question of all of those LLM test questions. The question is basically logically inconsistent and expect the model to work around the inconsistency.

The casual release of Opus 4.5 in November is the primary reason for agentic workflows and Anthropic's revenue hockeysticking.

They have a much stronger model named Mythos, it made quite a splash - you can google it.

These are just small fine tunes on top of the older model

What do you do for a living? Not coding, that's for sure.

Why should we say stop?

Now you can lose money in parallel, 100x faster!

> Claude can plan the work and then run hundreds of parallel subagents in a single session (and with Opus 4.8, the agents can run for even longer).

You're absolutely right! And honestly? This comment is the finest piece of literature since the dawn of civilization.

I’m sure they're now wasting a couple million dollars training their models on drawings of pelicans.

How dare you take away the limelight from Simon? :D

Deception is not ideal for agentic coding.

I doubt it, they seem to keep getting 10-20% better every time for me

The question is: is it still worse than GPT 5.4?

Which is why they brought it up as something they are trying to improve.

Less than other frontier models. Which is scary honestly.

Looking forward to not being able to even try it on pro because pressing enter will eat 50% of my 5 hour window.