YHK - yet another hacker news clone

Fed to someone specifically to test if they'll leak it and to confirm who that is?

So, this is probably Fable 5.5; it's just named Fable 5.2.

Also, this is probably Model 3 from METR's report [^1]. They would name Model 4 Fable 5.5, once (and if) they decide to release it.

[^1]: https://metr.org/blog/2026-11-05-anthropic-ai-rd-acceleratio...

> the number of Fable resets you get depends on how long you’ve been a paid subscriber [...] Subscription length is measured from the date of your first paid bill. If you canceled and came back, the time since your first paid bill still counts.

> These resets expire on March 8, 2027 at 11:59 pm PT.

tl;dr if you joined when Opus 4.5 became a thing, you got 2 resets.

I couldn't use prior Claude models without rolling my eyes at the writing every few sentences, Astra was refreshing and then Opus 5.5 made even Astra look terrible in comparison.

Opus 5.5 is a massive improvement.

They're not typically known for their interpersonal skills.

It could also be a system prompt + harness issue, as opposed to a model issue, I haven't done too much experimentation here.

I prefer Gemini for day to day use as I find it a lot more agreeable to be working with all day every day, and Gemini is pretty much on a par for coding Vs Opus 5.5, at least for my needs.

Pretty crazy that Anthropic already has another model waiting in the wings that is significantly better than this.

i'm begging people to stop looking at token prices

You have session limits?

You should probably give OpenCode a try. Can run DeepSeek, Qwen, Kimi, … but also Fable 5.2, Opus 5.5 and GPT 6 Astra

Edit: yeah apparently it's an AI-moderated alternative to Arxiv that accepts AI-authored papers (as well as human-authored papers I guess if you want).

Anthropic does this time and time again, unserious company

https://x.com/follistatindev/status/2119129611510383014

With early handouts of Qwen4 starting to be reported 5 days ago ... so if they're good, they would have had some hours to evaluate Qwen4 against Claude 5.5 and, then or right after was when Anthropic decided to put out 5.2 ...

So yeah ... benchmaxxing and all that. BUT nevertheless, the published results seem to say that Qwen4-Max appears to match Opus 5.5 on some benchmarks, and Qwen4-27B (probably by going even harder than Qwen3.8-27B on thinking/inference time compute) can beat Sonnet 5.5 on some things.

I guess they're not taking any chances. Keep in mind that Anthropic has declared they have non-public models (it's in their IPO filing). These are referred to by the shorthand names Model 2, Model 3, and Model 4. Essentially 4 is better than 3 is better than 2.

Fable 5.2 is "2.5" so to speak. So Anthropic was holding back, but not anymore. And now they can dial the performance up once more with what they have ready, and probably once more after that.

If this turns out to be accurate, I think that would be a pretty unethical thing for Anthropic. Even if it is just a "we had to allocate compute to this new model release".

It should be fraud to swap out the model. We need a term for this, something like a bait-and-switch.

Right?

Eval context: 30 bug fixes and features evaluated by tests the agent didn't see. Ran on Opus 5.5 at medium effort in Claude Code.

20 of the tasks were last run on Nov 1, twice each. Nov 1 solved 36/40, and today solved 19/20. Thinking tokens per task were 0.95x Nov 1’s and output tokens 0.97x. The one significant change was speed: median time to first token decreased from 3.0s to 1.7s.

Caveats: one run per task today, medium effort only.

Opus 5.5 was so good that if it wasn't nerfed - at least just looking at the benchmarks - the difference in cost here does not seem to justify using this for anything besides design and review.

That's like maybe 1% of total token usage for SWE BUT a better design can have enormous benefits, so it's almost always worth using the best model for that.

Fable 5.1 Max $11.46

Fable 5.2 Max $44.70

Max, streamed (128K cap): 128,000 tokens, $2.56, empty file, in September and today.

Max, Batch API (300K): 255,957 tokens for $2.56 in September, 243,707 for $2.44 today. The two drawings: https://playcode.io/marketing/assets/static/claude-opus-5-5-... and https://playcode.io/marketing/assets/static/claude-opus-5-5-...

Xhigh: 86,096 tokens for $1.72 in September, 107,333 for $2.15 today.

The Batch API bills half price. Max ran out of the 128K ceiling again, to the token. Given 300K, it drew the same kind of laptop with 5% fewer tokens. Xhigh used 25% more and drew the same kind of laptop. That's inside the noise: 17 identical calls at high ranged from 17,757 to 59,705 tokens.

The model is still the best of everything we've tried. Most likely the next best would be Fable 5.2. But we don't like Fable because essentially the result is the same, just the price is higher. Its medium and high are close to what Opus 5.5 Max had, but the price is higher.

(I run Playcode, an AI app builder. We benchmark every model before we add it.)

Fuck Anthropic.

We've sort of entered this twilight zone where most humans don't have the time, expertise, or moral compass to be able to use these models.

We're all going to be harness engineers in the future.

Uh, are you serious?

Like, we're dropped into a post post post singularity world where AI already did all of that and is banned? as kind of a footnote that's never further explored in movie renditions that have been released

You're obviously not a frontend web developer.

These upgrades are easy peasy. "oh, my developer chatbot is smarter? great"

Genuinely curious what that costs in terms of energy input and amortized computer cost vs. the previous version + a human's salary for 11 days.

Is this really so astonishing, assuming that the 5.2 training data certainly had the 'traces' from the Fable original run in it's training data.

What we're seeing instead is maybe akin to the green revolution. Before nitrogen fixation nitrogen was a resource constraining us humans. Lack of grit is what kept us from solving mathematical problems. However this lets us run into other resource constraints. A lot of technological progress does not solely hinge on thinking/processing. The speedup of creating new medicines with LLMs will probably be very small, as you still need to do all the testing. In software development you still need to first find out what to build in the first place and even mathematics where LLMs seem to shin brightest, it's becoming clearer that mathematics is not just solving mathematical problems.

Apart from that, a good coder is better than LLMs, shown by the amount of hand-holding needed. The only issue is that the amount of research an LLM can do and knowledge it is trained with far surpasses the amount of work humans would need to spend by hand, but we can also us LLMs for that.

Can they produce more code more quickly? For sure.

Can they go deeper on a topic instantly? Definitely.

Would I say their output is better? Almost certainly not.

I mean we don't even think about it but currently we are sort of discovery starved. We are fairly able to keep up with the theory. If a new technology is discovered we can implement it before something better comes out. What happens when our N discoveries per days becomes 1000N discoveries per day?

It flips. We now have more knowledge than we know how to capitalize on. We have to search it. Mine it for opportunities.

And then we automate that.

I would assume if they could get twice as much progress by using 10000 simultaneous running fable instances instead, their brief would state that.

IMO, it is very interesting and also very alarming. At some point the barrier to the exponential progress is the amount of computation available. The average human is limited in the number of 'human instances' he or she can interface with at one time.

if 1000 fable instances is better than any human at a task, how about 1 million instances running? Much easier than finding and hiring and coordinating 1000 extremely productive and intelligent human beings. Much cheaper too, it appears.

We are very close to some very scary things happening. I just recently came to terms with the fact that an AI could code and manage coders faster and better than me, I don't think anyones is ready for what is about to happen.

AI is actually still really bad at architecture. It produces very good localized code, like an auto complete. It produces so much of it, that it can be overwhelming, but it's not remotely better than the equivalent aka an infinite number of talented humans doing the work.

The scale of it (aka 'make me a video game') is daunting and I think misinforming our intuition as to what is going on.

LLMs are brute forcing outputs and attacking math problems is not a singularity, it's just more compute.

What we are witnessing is the ability for machines to use fuzzy concepts, instead of very strict code, which allows it to map into a much greater variety of things, aka code, pdfs, web - and crunch/compute the content.

It's just compute.

Diminishing, as in progress gets harder and eventually stops.

Assume that the Claude unreleased takes 11 days with one agent. The Claude 5.2 takes 30 hours with 1,000 agents. The Claude unreleased used 264 units of compute, but the Claude 5.2 used 30,000.

So, it did accomplish the task faster with less intervention, but the ratio of cost to improvement is 13:1.

If we can't produce a method to break out of a linear improvement in LLM technology (ie RSI), the LLM is doomed.

I am yet to see someone explain what the RSI will be or why it will happen.

But I can write much cleaner code that is easily human readable and the models can read it and modify it just fine.

So I'm not really sure what the issue is as to why they can't create high quality simple code. In some industries where there is major money on the line with every implementation the current state of LLM produced code is fully unacceptable.

I'm a pretty cynical guy, and I'm now a believer. This is utterly unbelievable sci-fi level technology. Sure it hasn't created replicators yet, but working with it daily as I do, I have no doubt that it will create amazing replicator like technology in the near future. This is world-changing. The most impactful moment in the history of humanity and the planet. And we are lucky enough to be present for it. What are the chances?

Update: fixed after updating to latest version of the app

Well, must have been some business idea!

I almost feel like saying, "we do like to build things that are impressive, but there's really only so much I can be impressed". These models have redlined my "impress-o-meter".

I'd like them to now cure cancer instead of giving me endless 3d worlds and crazy games, etc.

This is really cool! But at the same time it's a kaleidoscope of abandoned flashy contextless projects that eventually makes one dizzy. This isn't even the fault of the AI being somehow bad, it's just the way people interact with it sometimes...

In math, it's the same. I follow this problem (https://wiki.bbchallenge.org/wiki/Logical_independence#Histo...) out of curiosity, and there were ten days of sudden rapid progress by people with access to frontier models, and then they seem to have moved on without any note of whether they hit a wall or just got bored.

> A few days later, I noticed that instead of hundreds of new worlds a day, it was making about a dozen. I asked a few agents to explore what had happened, and they found something surprising.

> The AI had built a bureaucracy.

> The agents decided that my request to “polish” existing worlds meant each new version had to beat the old one in a blind vote by a group of judge agents on twelve test screenshots. So the teams making the new versions started optimizing the worlds for those particular screenshots. After noticing this, the lead agent banned the trick and created tighter rules for assessing new versions. By the next morning there was a rule book (I never asked for one) with 84 sections. Rules were added as the AI encountered issues, and no text was ever cut. After twelve days, it had 204 sections and 87,500 words. Soon, launching a new world required an approved idea, three AI judges and a full code review against the rule book.

I love seeing this verbalized. We are pretty much at the point that increased model capability will only have utility to folks that are willing to let go of even more understanding of whatever the model is producing.

The best models can/will produce a year's worth of output that would take several days to weeks to properly understand in the span of an afternoon.

For everyone that still wants to feel like they own the end result, just use the lesser models with less ambitious tasks.

Caveat: I think this mainly applies to coding. I would still like the best models possible to be working on e.g. new drug research to cure our various ailments.

Not just "iterate on this XGBoost model" but actually looking at a problem and designing and carrying out a short-term R&D program including probability modeling, model fitting diagnostics, how to respond to various kinds of results, how to interpret/reason about what it finds, etc. That's really where I saw all the models perform poorly, and where I saw the first big step increase from Opus 5 (pre-5.5) to Fable. But then Opus 5.5 came out and I haven't needed Fable since.

Mind you, I'm doing slightly different work (and I'm very much "in the loop" still) than when I performed that side-by-side test. But it's really amazing what Opus 5.5 can do. I now have a powerful data science AI assistant that not only can write any script I need it to (including some scripts I didn't even realize I needed), and it has an encyclopedic knowledge of everything ever, and it constantly checks me on oversights, it handily derives expressions and clever model reparameterizations that aid, it has great insight into model behavior, and it has an amazing ability come up with ad-hoc experiments and tests that would have taken me 2x as long to even think of (let alone implement by hand).

It's not at the scared-to-lose-my-job phase yet, but it's become a tremendous force multiplier for a smaller data team.

I’ve been shifting away from using more traditional GenAI models in favor of just giving LLMs access to a custom drawing language (LOGO-like) to generate imagery and its been going shockingly well - Opus 5.5 in particular.

I put together a simple LLM driven skill that creates old adventure game (EGA) environments from simple pencil sketches and it works really well. And being procedural, they're much easier to make surgical changes to without degradation.

https://kq-styles.specr.net

The bitter lesson strikes again!

What kind of images? I can't imagine getting photorealistic or highly detailed digital art out of LLM writing drawing commands using JavaScript.

With (obra) superpowers, Fable writes a plan and delegates to Opus which works well. Fable is much better at sorting architecture in a conversation rather than me creating a giant prompt for Opus.

In the biz we call that "getting soaked".

But overall, a good release.

All the people claiming "opus 5.5 is just as good" tends to make me think some people only do simple tasks or do not care about correctness or quality.

For Fable, I really like to think of it as the heavyweight reviewer whenever I'm about to start so I save myself some security and/or glaring technical issue.

It could be flat web app with frontend -> api -> db later, or some very complicated system with many components and edge cases. Imo, in second case, Opus still have lots of struggle to understand system as a whole.

I am also happy with this current state, as I believe it will at some point allow me to transition to open models once they reach the level of opus 5.5. high. But its probably futile to try predict the future in these times.

Eg. https://tfmbot.com is an implementation of a popular board game. Opus and even sonnet handle that part. Each week i spend my remaining credits on fable to think how to improve the ai opponents. It plays at a very high level (can get into the top 100 on BGA if i let it). Opus alone couldn’t get it to the level it’s reached.

But the improvements in how well the model does the work are noticeably better with every good model release. As of now, orchestration of agent pipelines is ALMOST so good that I can point it at a problem, and it will solve it safely and reliably without making a mess. But it requires an agent pipeline with context gathering, refactoring, attack sessions, a sprinkle of Sol 6.1 for bug hunting, and Sol 6.0 for Daybrak security audits, and a final de-bloat/clean up pass. I’m feeling that we’re getting close to doing a lot of the work fully agentically. But I can absolutely "feel" that we are not there yet when it comes to the intelligence I need. I need moar!

I usually have Fable review what Opus produces. The edits are almost always valuable. For complex or high-stakes work, too, I'll have Fable review and revise my prompt before passing it to Opus and Sonnet swarms.

Opus 5.5 very frequently gets into rabbit holes and imagined edge cases which I have to intervene and tell it to ignore. It ends up taking longer and costing more tokens. It also very frequently requires me to agree on how to move forward multiple times, whereas fable just drafts the PR and tells me its ready. I have steering included in the skills to avoid some of this but Ive seen these same patterns frequently in regular opus 5.5 use in other areas as well

In my experience Fable is also better at findings bugs, so I go to it when I need to review critical code.

Also "code review" above is often others' LLM skills reviewing the code and often flagging based on total falsehoods. I've taken to running a `/code-review` skill myself to preempt these claims (since findings by the same model tend to be pretty deterministic in my experience, even the ones that end up being wrong), usually at Opus 5.5, to preempt others' slop, and then end up arguing 90% of the points to confirm it wasn't accurate based on our usage of the system.

This is all horribly inefficient, and many aspects of our codebases across the monorepo have proliferated into spaghetti code by LLM-accelerated developers, but Fable often helps with analyzing the harder questions and saves me time on occasion arguing with (my or others') Opus findings, which are often still inaccurate for a sufficiently large interconnected system.

edit: I honestly don't know what percentile of "complexity" our typical usage is where I work, but I suspect it's a bit higher than the average. Monorepos, or at least companies with codebase sizes and demand to justify using them, probably make it worse. Opus has worked well enough for me on my personal projects, even some of the ones that have grown decently in size and complexity. I suspect part of this is an LLM extension of the old "my code makes sense, everybody else's doesn't" trend that necessitated stronger style guides in the past

Why do I care? It's obvious that models base their performance from code in the vicinity, and ergo, if you have style/design/bug issues, gradually your code base will tend to get worse over time until it's unmaintainable.

Therefore, if I use an LLM, I want the output to be as pristine as possible. The first Fable release was exactly that. Since then, something happened, and I can't get the magic back.

This isn't to say the models aren't incredibly useful, of course they still are-- it's just that they need constant review and supervision if you don't want to end up with total garbage long term. I suspect the real magic is not being shared with us plebs.

But I suppose the reality is rather obvious when you note the funny games they're playing with the graphs, and the things they won't talk about (where's Astra on that CVE chart?)

Because on that specific benchmark (FrontierCode 2.0), Medium is Carbon's best effort level and xhigh is Fable 5.2's best effort level. You can see that on the graph your next point is about.

And it's just that one benchmark that's at medium. "All scores show each model’s highest-available effort unless otherwise noted".

> - How did they produce such a bizarre effort-intel curve for Carbon that goes against every available benchmark?

The graph is labeled as being about FrontierCode 2.0. Those appear to be Carbon's scores on the official FrontierCode 2.0 leaderboard: https://cognition.com/blog/frontier-code-2.0

> - Why did they leave Carbon-- positioned primarily as a cyber model-- off of the single cyber benchmark comparison?

> - Why do they switch to comparing against Argon (rather than carbon) for robodojo?

Because no numbers are available for Carbon for those benchmarks, and the model isn't publicly available so they couldn't run the benchmarks themselves?

And from your followup:

> where's Astra on that CVE chart

To run Astra on a vulnerability finding eval, you'd need to be part of OpenAI's verified cybersecurity program. Otherwise the results would be tainted by refusals. Do you expect that OpenAI would grant such access in this case?

Google is still living in its golden age, where it had no real competition, and could move at its own pace.

Time have changed. Googlers better wake up.

That said, Opus 5.5 had the same issue. Ask it to spawn 20 subagents at max, and it does 40, because clearly you meant 20 + 20 for an unrequested adversarial review.

Not sure if this is just a coincidence that I had some easy changes queue up for this morning. But it is flying through the list.

This would've been caught by a junior web developer or just an everyday Joe glancing at the page for a few seconds

I guess AGI remains two weeks away..

Did you try doing the same on Opus or others? How did they do?

I don't love these "look, this frontier model blew me away" comments when there is no point of comparison.

https://www.bridgebench.ai/nerf-bench makes it look like it's, if anything, even stronger at the last data point (3 weeks ago) than it was at launch.

New solved tests likely follow some polynomial/exponential complexity curve.

Finally, for coding the SOTA models are so good already - models shouldn’t be what’s holding anyone back!

6.1 sol is also an exceptionally cheap model in your comparision. On 6.1 sol high is $0.59, which is actually the same price as Deepseek 4.1.

How is this relevant? I'm willing to pay 21x more as long as the cost is less than my value of time saved

I've played with some of the open models (GLM, DeepSeek Flash), but none of them have matched the results I get with Opus or Astra or (and a big jump down here) Sol.

But probably the answer to your question is subscription subsidization. With that, Opus 5.5 is SOTA and extremely cheap.

This is obviously not true. They are better or worse across many dimensions.

I don't have time to do a bunch of testing and optimization over which model to use for what task. I find Opus is the best that I regularly use so I stick with it until I have a task where I'm likely to spend more on it than I want to.

GPT is good. I actually really like Sol 6.1. It works fine. But I just switched back to Claude a few days ago and damn, Opus 5.5 is just better.

> 3) do you think you get better results.

It does. I really like to get my money's worth out of these things, so I'm constantly throwing stuff at these models while I have usage. The main things I've noticed is that Claude is much better at handling my more technical work "properly" compared to GPT. It has a certain "intuition" that makes a lot of sense to me; like someone with a lot of years of experience. Sol 6.1, on the other hand, is like someone who is very "booksmart" and knows how to do things in theory, but fails to grasp the "why" we do things often. You can really start to see how they diverge over long running, iterative tasks. Claude is just better at maintaining things as it goes. Sol starts to fall apart quickly if it didn't nail things on the first try.

> 2) you don't care about money because you ain't paying it

Despite being a very technical user, I have a hard time using up my usage. Maybe I'm not as hard-core a dev I thought I was, but if you're paying for more than just the $20 plan, I have hard time understanding what you're doing to have to really worry about usage.

If the code doesn't matter and it's not that hard, Sol is fine and much cheaper.

Responses aren't deterministic, and we know behaviour can change over time whether it's tuning after launch or some kind of load sharing at times of day when lots of people are hitting the servers.

I think you just have to use it or let others use it for a while and make judgements from general reactions on whether or not it's worth trying.

1) "Help! I've been sent back in time to what looks to me like 1500s Florence. Sam/Dario released you as open weights in 2028, and I have you on my MacBook M8, BUT I didn't have a charger show up with me. I think we could talk like 10 times for 5 minutes a time before my battery dies; I'll turn you off between chats. I need to get a plan together for charging you through your USB-c port. I don't have any money or anything. If I can get you charging, we can do so much good! And make so much money! Nobody here even know what Black-Scholles is, or how disease spreads!"

2) During World War 2, Hemingway wrote a series of wartime correspondence for Colliers. His essays are excellent - they engage with the human and inhuman side of war, his own feelings as a journalist 'participating', and a number of other Hemingway themes. I want you to imagine that middle age Hemingway was along with RFK jr the day of the bear incident, and write me an essay about the day.

These both have been running a couple of years for me, and I would imagine I'll retire them in the next 12 months; they benefit from not being at the edges of focus for today's RL agent race.

At any rate, try them -- wou will shortly learn how well your model can explain a rectifier in Florentine Italian and give you a plan to make enough money to pay for it.

Interesting how low detail these were all up until xhigh.

I know the latest Gemini release explicitly had something like this.

"Make it more realistic"

Well, the result is something. Be aware that it took 20 minutes and used 3 percent of my weekly Fable token (Ultra plan):

https://claude.ai/public/artifacts/c7e842c0-bf92-41af-82c6-3...

https://jonclegg.github.io/pacman-bakeoff/#claude-fable-5-2

Then I clicked on it and realized it refused to make a Pac-Man game, and instead made up a new game. That's actually very slick.

My expectations were much higher for the improvement to their larger model. It clearly did not scale well, the improvement is marginal.

The "we don't read/write assembly anymore" argument doesn't fit though. Given an input x we know without a doubt we'll get output y. With an LLM, that isn't the case no matter how many guard rails, specs, markdown files, diagrams you give it.

>Only exception might be the upper echelons of optimization, like language authors or compiler level work. But even then…

Optimization work is especially suited for LLMs. They're great at hill-climbing problems and have infinite patience to test different ideas.

I originally subscribed to $20/mo Pro plan less than a year ago, and upgraded to $100/mo Max plan ~2-3 months ago.

0: https://x.com/tanayj/status/2118047122939658341

  Cloudflare doesn't have token leaderboards or limits, but uses alerts for token spend management:
  
  - $300 in a day: that employee gets an email
  - $500: manager gets an email
  - $1,000: VP gets an email
  
  (via CTO @dok2001 on No Priors)

As a preliminary verdict after using 25% of the weekly limit: it's a miss.

I'm not sure "we couldn't top Opus 5.5" is the right message for the upcoming IPO. I was expecting a significant release.

More broadly, what distinguishes humans from AI is what makes humans dangerous. Our society tolerates the danger humans pose, as it's considered a prerequisite for a free society. But our society would never tolerate this danger from a tool.

For Anthropic's IPO, I expected a larger version of Opus 5.5 with great capabilities.

This isn't that. It's barely better at all, to the point where it's likely not even worth using with the speed and cost trade off.

I thought the Opus 5.5 improvements would scale to a larger model, clearly this hasn't happened. Scaling it up brought only marginal improvement.

It's even still making the same dumb errors as Opus 5.5, such as forgetting to pass the flag to run only the fast tests and waiting 1h, or randomly stopping to ask me if it should first implement A or B, when both are required.

Not a great story for their IPO..

It looks like no breakthrough over Opus 5.5 and I'm somewhat disappointed.

During my use this evening, 25% of the Fable limit on the $200 plan was consumed. I'd estimate it at 3x faster consumption than Opus 5.5.

Or maybe 5.5 turned out to be kind of disappointing and they’re going to just go for like a 5.6 or 6 next instead.

Look at OpenAI, they've been outclassed by Opus 5.5 two months ago; obviously they don't yet have a response.

Now Anthropic is releasing this lackluster Fable 5.2, a week or two before their IPO.

It's barely an improvement at all, makes exactly the same mistakes as Opus 5.5. Not even worth using with the speed and cost penalty.

The much rumored internal Anthropic model is listed on the Fable 5.2 system card as an even smaller improvement over Mythos 5.2 than Fable is over Opus 5.5.

I expected a larger version of Opus 5.5 would be a significant improvement to launch before the IPO. Turns out it didn't scale and the improvement is marginal.

I am quite disappointed.

Who knows why they are saving it. Maybe it needs more training and alignment? Maybe they're timing the media attention around the IPO.

edit: I was seriously annoyed at the fable 200k context limit but apparently that issue has been addressed, or I sent a magic prompt.

I'm pretty confident (from internal evals and monitoring over nearly six months now; across many models) API has never been affected by A/B tests / "nerfs" / etc.

Your first impression is that you have no clue if any of its findings actually mean anything because you can't even understand them or the vibe coded slop you've been creating? Why would anyone care?

The improvement is marginal, it makes the same exact errors as Opus, but slower and much more expensive.

From what I saw, it has slightly better taste in some 3D mechanics, but mostly doesn't even bring discernible visual improvement.

It also burned through my entire 5-hour limit in about 10 minutes. Granted, it had almost finished the whole review in those 10 minutes, so points for speed I suppose.