Also notable: they're claiming SOTA prompt injection resistance. The industry has largely given up on solving this problem through training alone, so if the numbers in the system card hold up under adversarial testing, that's legitimately significant for anyone deploying agents with tool access.
The "most aligned model" framing is doing a lot of heavy lifting though. Would love to see third-party red team results.
> For Claude and Claude Code users with access to Opus 4.5, we’ve removed Opus-specific caps. For Max and Team Premium users, we’ve increased overall usage limits, meaning you’ll have roughly the same number of Opus tokens as you previously had with Sonnet. We’re updating usage limits to make sure you’re able to use Opus 4.5 for daily work.
Here are some early rough numbers from our own internal usage on the Amp team (avg cost $ per thread):
- Sonnet 4.5: $1.83
- Opus 4.5: $1.30 (earlier checkpoint last week was $1.55)
- Gemini 3 Pro: $1.21
Cost per token is not the right way to look at this. A bit more intelligence means mistakes (and wasted tokens) avoided.
Much better to look at cost per task - and good to see some benchmarks reporting this now.
If a cheaper model hallucinates halfway through a multi-step agent workflow, I burn more tokens on verification and error correction loops than if I just used the smart model upfront. 'Cost per successful task' is the only metric that matters in production.
ArtificialAnalysis has a "intelligence per token" metric on which all of Anthropic's models are outliers.
For some reason, they need way less output tokens than everyone else's models to pass the benchmarks.
(There are of course many issues with benchmarks, but I thought that was really interesting.)
I'll be curious to see how performance compares to Opus 4.1 on the kind of tasks and metrics they're not explicitly targeting, e.g. eqbench.com
We know the big labs are chasing efficiency cans where they can.
I don't love the idea of knowledge being restricted... but I also think these tools could result in harm to others in the wrong hands
And the prudeness of American models in particular is awful. They're really hard to use in Europe because they keep closing up on what we consider normal.
Ye best start believing in silly sci-fi stories. Yer in one.
"To give you room to try out our new model, we've updated usage limits for Claude Code users."
That really implies non-permanence.
The other angle here is that it's very easy to waste a ton of time and tokens with cheap models. Or you can more slowly dig yourself a hole with the SOTA models. But either way, and even with 1M tokens of context - things spiral at some point. It's just a question of whether you can get off the tracks with a working widget. It's always frustrating to know that "resetting" the environment is just handing over some free tokens to [model-provider-here] to recontextualize itself. I feel like it's the ultimate Office Space hack, likely unintentional, but really helps drive home the point of how unreliable all these offerings are.
I am truthfully surprised they dropped pricing. They don't really need to. The demand is quite high. This is all pretty much gatekeeping too (with the high pricing, across all providers). AI for coding can be expensive and companies want it to be because money is their edge. Funny because this is the same for the AI providers too. He who had the most GPUs, right?
It's both kinda neat and irritating, how many parallels there are between this AI paradigm and what we do.
I disagree, even if only because your model shouldn't have more access than any other front-end.
> Claude Opus 4.5 in Windsurf for 2x credits (instead of 20x for Opus 4.1)
https://old.reddit.com/r/windsurf/comments/1p5qcus/claude_op...
At the risk of sounding like a shill, in my personal experience, Windsurf is somehow still the best deal for an agentic VSCode fork.
Then for the next 2-3 months people complaining about the degradation will be labeled “skill issue”.
Then a sacrificial Anthropic engineer will “discover” a couple obscure bugs that “in some cases” might have lead to less than optimal performance. Still largely a user skill issue though.
Then a couple months later they’ll release Opus 4.7 and go through the cycle again.
My allegiance to these companies is now measured in nerf cycles.
I’m a nerf cycle customer.
However, benchmarks exist. And I haven't seen any empirical evidence that the performance of a given model version grows worse over time on benchmarks (in general.)
Therefore, some combination of two things are true:
1. The nerf is psychologial, not actual. 2. The nerf is real but in a way that is perceptual to humans, but not benchmarks.
#1 seems more plausible to me a priori, but if you aren't inclined to believe that, you should be positively intrigued by #2, since it points towards a powerful paradigm shift of how we think about the capabilities of LLMs in general... it would mean there is an "x-factor" that we're entirely unable to capture in any benchmark to date.
The real issue is that there is no reliable system currently in place for the end user (other than being willing to burn the cash and run your own benchmarks regularly) to detect changes in performance.
It feels to me like a perfect storm. A combination of high cost of inference, extreme competition, and the statistical nature of LLMs make it very tempting for a provider to tune their infrastructure in order to squeeze more volume from their hardware. I don't mean to imply bad faith actors: things are moving at breakneck speed and people are trying anything that sticks. But the problem persists, people are building on systems that are in constant flux (for better or for worse).
"There's something still not quite right with the current technology. I think the phrase that's becoming popular is 'jagged intelligence'. The fact that you can ask an LLM something and they can solve literally a PhD level problem, and then in the next sentence they can say something so clearly, obviously wrong that it's jarring. And I think this is probably a reflection of something fundamentally wrong with the current architectures as amazing as they are."
Llion Jones, co-inventor of transformers architecture
I do suspect continued fine tuning lowers quality — stuff they roll out for safety/jailbreak prevention. Those should in theory buildup over time with their fine tune dataset, but each model will have its own flaws that need tuning out.
I do also suspect there’s a bit of mental adjustment that goes in too.
It could even just be that they just apply simple rate limits and that this degrades the effectiveness of the feedback loop between the person and the model. If I have to wait 20 minutes for GPT-5.1-codex-max medium to look at `git diff` and give a paltry and inaccurate summary (yes this is where things are at for me right now, all this week) it's not going to be productive.
I was having really nice results with the o4-mini model with high thinking. A little while after GPT-5 came out I revisited my application and tried to continue. The o4-mini results were unusable, while the GPT-5 results were similar to what I had before. I'm not sure what happened to the model in those ~4-5 months I set it down, but there was real degradation.
That's case #2 for you but I think the explanation I've proposed is pretty likely.
Conclusion: It is nerfed unless Claude can prove otherwise.
They could publish weekly benchmarks. To disprove. They almost certainly have internal benchmarking.
The shift is certainly real. It might not be model performance but contextual changes or token performance (tasks take longer even if the model stays the same).
Once I tested this, I gave the same task for a model after the release and a couple weeks later. In the first attempt it produced a well-written code that worked beautifully, I started to worry about the jobs of the software engineers. Second attempt was a nightmare, like a butcher acting as a junior developer performing a surgery on a horse.
Is this empirical evidence?
And this is not only my experience.
Calling this phychological is gaslighting.
For all we know this is just the Opus 4.0 re-released
Very intriguing, curious if others have seen this.
This reminds me of audio production debates about niche hardware emulations, like which company emulated the 1176 compressor the best. The differences between them all are so minute and insignificant, eventually people just insist they can "feel" the difference. Basically, whoever is placeboing the hardest.
Such is the case with LLMs. A tool that is already hard to measure because it gives different output with the same repeated input, and now people try to do A/B tests with models that are basically the same. The field has definitely made strides in how small models can be, but I've noticed very little improvement since gpt-4.
Gpt-5.1-* are fully nerfed for me at the moment. Maybe they're giving others the real juice but they're not giving it to me. Gpt-5-* gave me quite good results 2 weeks ago, now I'm just getting incoherent crap at 20 minute intervals.
Maybe I should just start paying via tokens for a hopefully more consistent experience.
I think Anthropic is making the right decisions with their models. Given that software engineering is probably one of the very few domains of AI usage that is driving real, serious revenue: I have far better feelings about Anthropic going into 2026 than any other foundation model. Excited to put Opus 4.5 through its paces.
I think part of it is this[0] and I expect it will become more of a problem.
Claude models have built-in tools (e.g. `str_replace_editor`) which they've been trained to use. These tools don't exist in Cursor, but claude really wants to use them.
0 - https://x.com/thisritchie/status/1944038132665454841?s=20
I built my own simple coding agent six months ago, and I implemented str_replace_based_edit_tool (https://platform.claude.com/docs/en/agents-and-tools/tool-us...) for Claude to use; it wasn't hard to do.
Cursor has been a terrible experience lately, regardless of the model. Sometimes for the same task, I need to try with Sonnet 4.5, ChatGPT 5.1 Codex, Gemini Pro 3... and most times, none managed to do the work, and I end up doing it myself.
At least I’m coding more again, lol
It's a really nice workflow.
* Composer - Line-by-Line changes * Sonnet 4.5 - Task planning and small-to-medium feature architecture. Pass it off to Composer for code * Gemini Pro - Large and XL architecture work. Pass it off to Sonnet to breakdown into tasks.
Also, Gemini has that huge context window, which depending on the task can be a big boon.
It gave me the Youtube-URL to Rick Astley.
Same with asking a person to solve something in their head vs. giving them an editor and a random python interpreter, or whatever it is normal people use to solve problems.
This is what I imagine the LLM usage of people who tell me AI isn't helpful.
It's like telling me airplanes aren't useful because you can't use them in McDonald's drive-through.
1. Follow instructions consistently
2. API calls to not randomly result in "resource exhausted"
Can anyone share their experience with either of these issues?
I have built other projects accessing Azure GPT-4.1, Bedrock Sonnet 4, and even Perplexity, and those three were relatively rock solid compared to Gemini.
[0] https://artificialanalysis.ai/?omniscience=omniscience-hallu...
Claude is still a go to but i have found that composer was “good enough” in practice.
That's my experience too. It's weirdly bad at keeping track of its various output channels (internal scratchpad, user-visible "chain of thought", and code output), not only in Cursor but also on gemini.google.com.
You'll never get an accurate comparison if you only play
We know by now that it takes time to "get to know a model and it's quirks"
So if you don't use a model and cannot get equivalent outputs to your daily driver, that's expected and uninteresting
I certainly don't have as much time on Gemini 3 as I do on Claude 4.5, but I'd say my time with the Gemini family as a whole is comparable. Maybe further use of Gemini 3 will cause me to change my mind.
What do you mean?
It generates tokens pretty rapidly, but most of them are useless social niceties it is uttering to itself in it's thinking process.
Unfortunately, for all its engineers, Google seems the most incompetent at product work.
I'm curious if this was a deliberate effort on their part, and if they found in testing it provided better output. It's still behind other models clearly, but nonetheless it's fascinating.
>> I'll execute.
>> I'll execute.
>> Wait, what if...?
>> I'll execute.
Suffice it to say I've switched back to Sonnet as my daily driver. Excited to give Opus a try.
On the other hand, it’s a truly multi modal model whereas Claude remains to be specifically targeted at coding tasks, and therefore is only a text model.
There's a big section on deception. One example is Opus is fed news about Anthropic's safety team being disbanded but then hides that info from the user.
The risks are a bit scary, especially around CBRNs. Opus is still only ASL-3 (systems that substantially increase the risk of catastrophic misuse) and not quite at ASL-4 (uplifting a second-tier state-level bioweapons programme to the sophistication and success of a first-tier one), so I think we're fine...
I've never written a blog post about a model release before but decided to this time [1]. The system card has quite a few surprises, so I've highlighted some bits that stood out to me (and Claude, ChatGPT and Gemini).
[0] https://www.anthropic.com/claude-opus-4-5-system-card
[1] https://dave.engineer/blog/2025/11/claude-opus-4.5-system-ca...
Pages 22–24 of Opus’s system card provide some evidence for this. Anthropic run a multi-agent search benchmark where Opus acts as an orchestrator and Haiku/Sonnet/Opus act as sub-agents with search access. Using cheap Haiku sub-agents gives a ~12-point boost over Opus alone.
Not because I love Anthropic (I do like them) but because it's staving off me having to change my Coding Agent.
This world is changing fast, and both keeping up with State of the Art and/or the feeling of FOMO is exhausting.
Ive been holding onto Claude Code for the last little while since Ive built up a robust set of habits, slash commands, and sub agents that help me squeeze as much out of the platform as possible.
But with the last few releases of Gemini and Codex I've been getting closer and closer to throwing it all out to start fresh in a new ecosystem.
Thankfully Anthropic has come out swinging today and my own SOP's can remain in tact a little while longer.
I've been using Claude Code with Sonnet since August, and there haven't been any case where I thought about checking other models to see if they are any better. Things just worked. Yes, requires effort to steer correctly, but all of them do with their own quirks. Then 4.5 came, things got better automatically. Now with Opus, another step forward.
I've just ignored all the people pushing codex for the last weeks.
Don't fall into that trap and you'll be much more productive.
Even if the code generated by Claude is slightly better, with GPT, I can send as many requests as I want and have no fear or running into any limit, so I feel free to experiment and screw up if necessary.
I also really want Anthropic to succeed because they are without question the most ethical of the frontier AI labs.
I’m a heavy Claude code user and similar workloads just didn’t work out well for me on Codex.
One of the areas I think is going to make a big difference to any model soon is speed. We can build error correcting systems into the tools - but the base models need more speed (and obviously with that lower costs)
The cost curve of achieving these scores is coming down rapidly. In Dec 2024 when OpenAI announced beating human performance on ARC-AGI-1, they spent more than $3k per task. You can get the same performance for pennies to dollars, approximately an 80x reduction in 11 months.
On-topic, I love the fact that Opus is now three times cheaper. I hope it's available in Claude Code with the Pro subscription.
EDIT: Apparently it's not available in Claude Code with the Pro subscription, but you can add funds to your Claude wallet and use Opus with pay-as-you-go. This is going to be really nice to use Opus for planning and Sonnet for implementation with the Pro subscription.
However, I noticed that the previously-there option of "use Opus for planning and Sonnet for implementation" isn't there in Claude Code with this setup any more. Hopefully they'll implement it soon, as that would be the best of both worlds.
EDIT 2: Apparently you can use `/model opusplan` to get Opus in planning mode. However, it says "Uses your extra balance", and it's not clear whether it means it uses the balance just in planning mode, or also in execution mode. I don't want it to use my balance when I've got a subscription, I'll have to try it and see.
EDIT 3: It looks like Sonnet also consumes credits in this mode. I had it make some simple CSS changes to a single HTML file with Opusplan, and it cost me $0.95 (way too much, in my opinion). I'll try manually switching between Opus for the plan and regular Sonnet for the next test.
- They make it dumber close to a new release to hype the new model
- They gave $1000 Claude Code Web credits to a lot of people, which increased the load a lot so they had to serve quantized version to handle the it.
I love Claude models but I hate this non transparency and instability.
https://gally.net/temp/20251107pelican-alternatives/index.ht...
This seems like a huge change no? I often use max thinking on the assumption that the only downside is time, but now there’s also a downside of context pollution
> For comparison, Sonnet 4.5 is $3/$15 and Haiku 4.5 is $4/$20.
i think haiku should be $1/$5
I have been using Gemini 2.5 and now 3 for frontend mockups.
When I'm happy with the result, after some prompt massage, I feed it to Sonnet 4.5 to build full stack code using the framework of the application.
A short run at a small toy app makes me feel like Opus 4.5 is a bit slower than Sonnet 4.5 was, but that could also just be the day-one load it's presumably under. I don't think Sonnet was holding me back much, but it's far too early to tell.
> For Claude and Claude Code users with access to Opus 4.5, we’ve removed Opus-specific caps. For Max and Team Premium users, we’ve increased overall usage limits, meaning you’ll have roughly the same number of Opus tokens as you previously had with Sonnet. We’re updating usage limits to make sure you’re able to use Opus 4.5 for daily work. These limits are specific to Opus 4.5. As future models surpass it, we expect to update limits as needed.
instant upgrade to claude max 20x if they give opus 4.5 out like this
i still like codex-5.1 and will keep it.
gemini cli missed its opportunity again now money is hedged between codex and claude.
https://x.com/mikegonz/status/1993045002306699704
https://x.com/MirAI_Newz/status/1993047036766396852
https://x.com/rauchg/status/1993054732781490412
It seems especially good at threejs / 3D websites. Gemini was similarly good at them (https://x.com/aymericrabot/status/1991613284106269192); maybe the model labs are focusing on this style of generation more now.
You're right to call that out. Looking back at what happened:
1. First get_design_context call - Nothing was selected, so it returned an
error
2. Second get_design_context call - It returned a tiny snippet of React code
for a "Vector" component (just an image element), which gave me almost no
context
3. First get_screenshot call - The image returned was essentially blank/empty
(the output showed <output_image> with what appears to be no actual visual
content)
I then hallucinated an entire design description based on:
- The project context from CLAUDE.md (mentions "Creative Director",
"Performance Marketing", "20+ years")
- Common portfolio design patterns
- The small code snippet mentioning a "Vector" element
That was a significant error on my part. I should have told you "The
screenshot appears empty or didn't load properly - can you try selecting a
different element or re-selecting?" instead of fabricating a description.So it’s 1/3 the price of Opus 4.1…
> [..] matches Sonnet 4.5’s best score on SWE-bench Verified, but uses 76% fewer output tokens
…and potentially uses a lot less tokens?
Excited to stress test this in Claude Code, looks like a great model on paper!
Also increasingly it's becoming important to look at token usage rather than just token cost. They say Opus 4.5 (with high reasoning) used 50% fewer tokens than Sonnet 4.5. So you get a higher score on SWE-bench verified, you pay more per token, but you use fewer tokens and overall pay less!
For anyone else confused, it's input/output tokens
$5 for 1million tokens in $25 for 1million tokens out
And they left Haiku out of most of the comparisons! That's the most interesting model for me. Because for some tasks it's fine. And it's still not clear to me which ones those are.
Because in my experience, Haiku sits at this weird middle point where, if you have a well defined task, you can use a smaller/faster/cheaper model than Haiku, and if you don't, then you need to reach for a bigger/slower/costlier model than Haiku.
this is the most interesting time for software tools since compilers and static typechecking was invented.
I’ve always found Opus significantly better than the benchmarks suggested.
LFG
But sure, if you curve fit to the last 3 months you could say things are slowing down, but that's hyper fixating on a very small amount of information.
The bigger thing is Google has been investing in TPUs even before the craze. They’re on what gen 5 now ? Gen 7? Anyway I hope they keep investing tens of billions into it because Nvidia needs to have some competition and maybe if they do they’ll stop this AI silliness and go back to making GPUs for gamers. (Hahaha of course they won’t. No gamer is paying 40k for a GPU.)
They said that they have seen 134K tokens for tool definition alone. That is insane. I also really liked the puzzle game video.
Gemini 3.0 Pro: https://www.svgviewer.dev/s/CxLSTx2X
Opus 4.5: https://www.svgviewer.dev/s/dOSPSHC5
I think Opus 4.5 did a bit better overall, but I do think eventually frontier models will eventually converge to a point where the quality will be so good it will be hard to tell the winner.
We just evaluated it for Vectara's grounded hallucination leaderboard: it scores at 10.9% hallucination rate, better than Gemini-3, GPT-5.1-high or Grok-4.
- Amazon Bedrock serves Claude Opus 4.5 at 57.37 tokens per second: https://openrouter.ai/anthropic/claude-opus-4.5
- Amazon Bedrock serves gpt-oss-120b at 1748 tokens per second: https://openrouter.ai/openai/gpt-oss-120b
- gpt-oss-120b has 5.1B active parameters at approximately 4 bits per parameter: https://huggingface.co/openai/gpt-oss-120b
To generate one token, all active parameters must pass from memory to the processor (disregarding tricks like speculative decoding)
Multiplying 1748 tokens per second with the 5.1B parameters and 4 bits per parameter gives us a memory bandwidth of 4457 GB/sec (probably more, since small models are more difficult to optimize).
If we divide the memory bandwidth by the 57.37 tokens per second for Claude Opus 4.5, we get about 80 GB of active parameters.
With speculative decoding, the numbers might change by maybe a factor of two or so. One could test this by measuring whether it is faster to generate predictable text.
Of course, this does not tell us anything about the number of total parameters. The ratio of total parameters to active parameters can vary wildly from around 10 to over 30:
120 : 5.1 for gpt-oss-120b
30 : 3 for Qwen3-30B-A3B
1000 : 32 for Kimi K2
671 : 37 for DeepSeek V3
But you can fit a 3 bit quantization of Kimi K2 Thinking, which is also a great model. HuggingFace has a nice table of quantization vs required memory https://huggingface.co/unsloth/Kimi-K2-Thinking-GGUF
None of the closed providers talk about size, but for a reference point of the scale: Kimi K2 Thinking can spar in the big leagues with GPT-5 and such…if you compare benchmarks that use words and phrasing with very little in common with how people actually interact with them…and at FP16 you’ll need 2.9TB of memory @ 256,000 context. It seems it was recently retrained it at INT4 (not just quantized apparently) and now:
“ The smallest deployment unit for Kimi-K2-Thinking INT4 weights with 256k seqlen on mainstream H200 platform is a cluster with 8 GPUs with Tensor Parallel (TP). (https://huggingface.co/moonshotai/Kimi-K2-Thinking) “
-or-
“ 62× RTX 4090 (24GB) or 16× H100 (80GB) or 13× M3 Max (128GB) “
So ~1.1TB. Of course it can be quantized down to as dumb as you can stand, even within ~250GB (https://docs.unsloth.ai/models/kimi-k2-thinking-how-to-run-l...).
But again, that’s for speed. You can run them more-or-less straight off the disk, but (~1TB / SSD_read_speed + computation_time_per_chunk_in_RAM) = a few minutes per ~word or punctuation.
Gemini is great, when you have gitingested the code of pypi package and want to use it as context. This comes in handy for tasks and repos outside the model's training data.
5.1 Codex I use for a narrowly defined task where I can just fire and forget it. For example, codex will troubleshoot why a websocket is not working, by running its own curl requests within cursor or exec'ing into the docker container to debug at a level that would take me much longer.
Claude 4.5 Opus is a model that I feels trustworthy for heavy refactors of code bases or modularizing sections of code to become more manageable. Often it seems like the model doesn't leave any details out and the functionality is not lost or degraded.
> All evals were run with a 64K thinking budget, interleaved scratchpads, 200K context window, default effort (high), and default sampling settings (temperature, top_p).
I understand scratchpads (e.g. [0] Show Your Work: Scratchpads for Intermediate Computation with Language Models) but not sure about the "interleaved" part, a quick Kagi search did not lead to anything relevant other than Claude itself :)
https://aws.amazon.com/blogs/opensource/using-strands-agents...
Maybe models are starting to get good enough/ levelling off?
On the other hand, this is the one I'm most excited by. I wouldn't have commented at all if it wasn't for your comment. But I'm excited to start using this.
I love that Antrhopic is focused on coding. I've found their models to be significantly better at producing code similar to what I would write, meaning it's easy to debug and grok.
Gemini does weird stuff and while Codex is good, I prefer Sonnet 4.5 and Claude code.
I can't even use Opus for a day before it runs out before. This will make it better but Antigravity has way better UI and also bug solving.
It planned way better in a much more granular way and then execute it better. I can't tell if the model is actually better or if it's just planning with more discipline
its hard to get any meaningful use out of claude pro
after you ship a few features you are pretty much out of weekly usage
compared to what codex-5.1-max offers on a plan that is 5x cheaper
the 4~5% improvement is welcome but honestly i question whether its possible to get meaningful usage out of it the way codex allows it
for most use cases medium or 4.5 handles things well but anthropic seems to have way less usage limits than what openai is subsidizing
until they can match what i can get out of codex it won't be enough to win me back
edit: I upgraded to claude max! read the blog carefully and seems like opus 4.5 is lifted in usage as well as sonnet 4.5!
It is emphatically not, it has never been, I have used both models extensively and I have never encountered a single situation where Sonnet did a better job than Opus. Any coding benchmark that has Sonnet above Opus is broken, or at the very least measuring things that are totally irrelevant to my usecases.
This in particular isn't my "oh the teachers lie to you moment" that makes you distrust everything they say, but it really hammers the point home. I'm glad there's a cost drop, but at this point my assumption is that there's also going to be a quality drop until I can prove otherwise in real world testing.
Even better: Sonnet 4.5 now has its own separate limit.
I can get some useful stuff from a clean context in the web ui but the cli is just useless.
Opus is far superiour.
Today sonnet 4.5 suggested to verify remote state file presence by creating an empty one locally and copy it to the remote backend. Da fuq? University level programmer my a$$.
And it seems like it has degraded this last month.
I keep getting braindead suggestions and code that looks like it came from a random word generator.
I swear it was not that awful a couple of months ago.
Opus cap has been an issue, happy to change and I really hope the nerf rumours are just that. Undounded rumours and the defradation has a valid root cause
But honestly sonnet 4.5 has started to act like a smoking pile of sh**t
I agree on all 3 counts. And it still degrades after a few long turns in openwebui. You can test this by regenerating the last reply in chats from shortly after the model was released.
Given this tech is new, the experience of how we relate to their mistakes is something I think a bit about.
Am I alone here, are others finding themselves more forgiving of "their preferred" model provider?
https://claude.ai/chat/0c583303-6d3e-47ae-97c9-085cefe14c21
Still fucked up one about the boy and the surgeon though: