Adrian Nutiu

Entrepreneur and investor.

[Adrian Nutiu @AdrianNutiu]

@matvelloso Do one for every 10k new followers.

[Mat Velloso @matvelloso]

Dear 40 thousand followers,

I promised that at this number I'd release a song about this agentic security dialog.

Here it goes. It's named Vibe Check.

I regret nothing.

[Adrian Nutiu @AdrianNutiu]

This week in AI

[Tibor Blaho @btibor91]

https://t.co/jY8NrVKBz2

[Adrian Nutiu @AdrianNutiu]

This.

[Guinness Chen @guinnesschen]

Bro it’s June 2026. Stop hand editing your prompts. Hold down the dictation button and ramble for 10 minutes. Give the model every fragment, caveat, example, and vibe in your head. It is literally a large language model. If it’s superhuman at anything, it’s reconstructing latent intent from language.

[Adrian Nutiu @AdrianNutiu]

@jmrphy Please DM me.

[ModelScope @ModelScope2022]

🚀 Build with GLM-5.2 and DeepSeek-V4-Pro for FREE!
We're giving developers 8 hours of FREE API inference on ModelScope.
👉GLM-5.2: https://t.co/RPMMB2oMsx
👉DeepSeek-V4-Pro: https://t.co/957zgpKCw7

[Adrian Nutiu @AdrianNutiu]

Big: Anthropic is pausing the Agent SDK credit change.

-p will keep working.

[JJ Englert @JJEnglert]

The Claude Fable 5 Review: One Billion Tokens, Judged by a Non-Engineer

I spent a billion tokens testing Claude Fable 5 on real projects: UI and UX, writing, strategy, security, engineering, and knowledge work. The kind of work I actually needed to ship. And I will be straight with you. It truthfully felt like I had an unfair advantage. Here is why:

First, the lens. I am not an engineer. Most model reviews come from engineers running engineering benchmarks. This one comes from a non-engineer who used Claude Fable 5 to do work that used to require a team of them. If you do knowledge work and you want to know whether this model changes your day, this is written for you.

A note on naming: Claude Fable 5 is the first model in Anthropic's new Claude 5 family, a new tier that sits above Claude Opus and the most advanced Claude model generally available. I had access to it before launch, so everything here comes from real work, not a demo.

Why the eye test

Most reviews drown you in benchmarks. Scores on tests you will never run, against tasks that look nothing like your actual work. They tell you a model is smart. They do not tell you whether it earns its keep.

To be clear, the benchmarks are not in question this time. Claude Fable 5 is state of the art on essentially everything it was tested on, and by a real margin. This is a genuinely exciting release. But that is not the reason I am writing. Qualitatively, this is a step change that earns its major version bump, the same order of leap I felt when 4.5 landed last November, and that is exactly what no benchmark can show you.

I evaluate differently. I put a model into real work and watch what happens. Does it save me hours or cost me them? Does it catch what I missed? Does it feel like a partner or a tool I have to babysit? That is the eye test, and it is the standard I am holding Claude Fable 5 to here.

The short version: this is the first model in a long time that passed on every dimension that matters. Not by a little.

The lens: what I actually measure

I threw all of that work at it. Here is what I look for when I judge the results:

1. Big model feel: Does it feel like a real step up, or a slightly better version of last month?
2. Building and shipping: Can it take an idea to a working, shippable result?
3. Writing and voice: Can it sound like a person, and like me specifically?
4. Finding what others miss: Does it catch the hard, hidden problems?
5. The human factor: Does it anticipate what I need before I ask?

Then I weigh all of that against cost, with real numbers. Here is how Claude Fable 5 scored.

1. Big model feel

I have not felt this since Opus 4.5. From the first serious task, Claude Fable 5 gave me that big model feel. The sense that you have an unfair advantage just by using it. It is a major step up, not an incremental one. Reasoning, writing, building, security. It is strong across the board, and it shows up the moment you start working.

You can also feel it thinking longer and working a problem more deliberately than other models do. The clearest sign: even when I handed it solid prep materials, it did not just stay inside them. It read my files, read the actual situation, and then went and found a better path outside the box I had drawn, instead of grinding away inside the environment I told it to work in. That initiative led to a noticeably better result than I would have gotten if it had just followed my setup.

2. Building and shipping (UI/UX)

This is where it announced itself.

I was rebuilding our Tenex site to modernize the stack for agents. Not a cosmetic rebrand. The goal was to move off the old setup onto a foundation built for the agentic era, with the tech stack, agent stack, and AEO it takes to win where the work is heading. The site is very custom, which made it hard. Here is the ladder I climbed before Claude Fable 5.

GPT 5.5 and Claude 4.8 tried the build on their own. Neither came close. So I brought the design into Figma, then pulled Figma into Claude Design. Claude Design got the closest yet, around 90 percent of the look, better than the models working alone, but it missed a lot of the motion and the special design touches. Good enough for a v1 pass, so I handed that file to 4.8 and GPT 5.5 to turn into the real site. Even then they struggled to match the Claude Design file. I had to push hard, and they landed around 85 to 90 percent, with the original Figma files to reference the whole time. At that point I was not sure I could rebuild this thing at all.

Then Claude Fable 5. It looked at all the files and said it could do better. It went straight to the source, the original Webflow site, downloaded every asset, and rebuilt the whole experience one page at a time. It nearly one-shot the entire thing.

I did not stop there though. I then built a second, entirely new site, with a fresh design: modern tech stack, agent stack, skills, SEO and AEO optimized, 80 pages ready to ship over a weekend and it turned out incredible. I would have easily charged $50k for this in the past as an agency owner. Fable legit built it in a weekend.

I also had Fable build a full programmatic clip factory, and it wired the whole stack together: @HeyGen for avatars, @HyperFrames_ for motion graphics and editing, @ElevenLabs for audio, Cloudflare Workers, and a VPS. It is not perfect yet, but it got me much further than I expected. It runs the entire pipeline: finds the topics, writes the scripts, makes the thumbnail, edits the video, composes the music, adds the motion graphics, and posts to social. I ran it in the background while I pushed through my other builds. It worked for long stretches on its own, and at one point it built itself a fetching system with webhooks to monitor renders across the different platforms. It even took clear visual direction from reference material and matched it. This is the long-horizon, run-on-its-own work that earlier models could not hold together.

3. Writing and voice

I had been rebuilding our brand voice with a combination of GPT 5.5 and Claude 4.8: the voice style guide, the tone we write in, all of it, using our website as the reference. Both 5.5 and 4.8 did a commendable job turning the site into a voice doc.

Claude Fable 5 replicated that voice doc almost identically, then did the thing the others could not. It took the style guide and wrote with it across 80 pages of the new site: features, case studies, blog articles, playbooks. Once it was trained properly on what I wanted, it gave the most honest nod I have seen to the original reference material, and then expanded that voice cleanly across brand-new surfaces without losing it.

Two things stood out. First, it wrote like a person, not the flat AI default that everyone can now spot from a mile away. Second, it held the voice across a whole site instead of drifting after a few paragraphs, which is usually where models fall apart.

The test I use for AI writing is simple: how much do I have to redo. Most models save you the blank page and then quietly cost the time back in edits. Claude Fable 5 was the rare case where the draft was close enough to actually use.

4. Finding what others miss (security)

This one I expected but not at this level.

I had a very large repo. Both Claude 4.8 and GPT 5.5 have been working in it without ever flagging this risk. Claude Fable 5 found a serious bug on its first go with the repo. Sneaky, well hidden, the kind two frontier models had just told me was not there. Then Fable patched it on the spot.

Sit with what that means. The bug was going to ship. Two of the best models available had signed off on the code. If I had stopped there, like most people would, it goes to production and I find out the hard way. Claude Fable 5 did not just match the other two, it caught what they missed, on the exact kind of work I am least equipped to check myself as a non-engineer. That is the value that is hard to price until the day it saves you. One catch like it can pay for the whole tool.

5. The human factor

The thing that stuck with me most was small. I asked it a question while I was waiting on a cron job to finish. It answered, then added on its own that I had about 10 minutes left on the timer and that it would let me know when it was done. I never asked about the timer. It just knew I would want to know and gave it to me.

That is not AGI, but it is the closest thing I have felt to a model that anticipates you instead of just responding to you. That is what makes it feel less like software and more like working alongside someone sharp.

The receipts

I tracked this, so here are the real numbers. Start with cost, which depends entirely on which models do the work.

Cost for this workload: Claude Fable 5 | $1,442 (1.04 Billion tokens)

But that badly undersells what I actually got. Over a few days I built a shit load of things, including a new website, all of its infrastructure, and a working agent package. As an agency, I would have charged a client $30,000 to $50,000 for that alone, easily.

So here is the question that cuts through the math: if I had to pay $1,450 in tokens the the result I achieved? 100 percent. Without hesitating. The quality was that good.

That is the lens that matters. On hours alone, even at full price, it already pays for itself several times over. Measured against what the finished work is actually worth, it is not close. The cache-heavy volume still drives the bill, which is why how you run it matters. But do not let the math fool you into thinking this is marginal. It is the best money I have spent on tooling.

Where it frustrates: with that being said, you feel the meter more than any other model, and the meter is real

The receipts above are why cost is still worth watching, even though the work was worth every dollar. Anthropic does not hide this. They call Fable 5 token-intensive by design, built to think longer and verify more, and it runs through usage limits about twice as fast as Opus or Sonnet.

That is the case for the one thing I want most: an auto-router for task complexity. Right now I have to shift gears by hand mid-conversation to conserve tokens, and I do not want to think about that. If I ask for something simple, the model should downshift on its own and handle it, saving the expensive intelligence for the work that actually needs it. This is not just about flow. It is the economics. A smart router keeps the simple work on cheap models and only escalates to Claude Fable 5 when the task earns it, which is the whole difference between 2.5 efficiency and 9.7. Until that exists, using a frontier model well means doing the routing in your own head with active shifting in model effort levels.

Pro tip #1: run it as a hybrid

Here is how I keep the cost in check without giving up the intelligence. Do not run everything on Claude Fable 5. Run a relay across models.

1. Think with Claude Fable 5: Use it for the expensive thinking: high-level planning, strategy, architecture, mapping the whole approach before a line of work gets done. This is where its edge is biggest and the token count is smallest.

2. Build with 4.8, GPT5.5 or Sonnet 4.6: Hand the plan to a cheaper model for the legwork: the implementation, the repetitive passes, the high-volume grunt work. That is the work that runs up the bill, and it does not need a frontier brain.

3. Review with Claude Fable 5: Bring it back to Claude Fable 5 to check the result. This is where it earns its keep a second time, catching what the cheaper models miss, the way it did on the security scan.

You get the deep strategy and a frontier second set of eyes, and you keep the expensive model off the high-volume work that drives most of the cost. Frontier thinking, cheaper hands, frontier review. It is the closest thing to an auto-router until the real one shows up.

Pro tip 2: match the effort setting to the task

Fable 5 has effort settings, and they matter more than you would expect. Effort controls how hard it thinks before it answers, which means it also controls your bill.

1. High is the sweet spot for most work. Start here.
2. Extra high for the hardest, long-running tasks where you want it to grind.
3. Low or medium: for quick, back-and-forth sessions where you do not need the full engine.

Reaching for extra high on simple work is how you burn tokens for nothing. Dialing down to low or medium on interactive chats keeps the cost sane. It is the closest thing to the auto-router I want, just done by hand. You pick the gear, the model does the rest.

Pro tip #3: let it audit your own setup

One more move that paid off: point Fable 5 at your own setup. Have it review your most important skills, your CLAUDE.md files, and your configs to make sure they still make sense.

Most of that scaffolding was written for weaker models. It is full of hand-holding steps, workarounds, and assumptions a smarter model does not need and can be held back by. This is a major jump in intelligence, and you do not want to cap it with outdated instructions or stale data. Let the smarter model clean up the rules it has to follow, then get out of its way.

Pulling back

Let me be honest about where I am coming from. I use every tool out there. Claude is my daily driver, but I am constantly in Codex and Cursor too, and they each have real strengths. I am not a one-model person.

But the moment I got access to Claude Fable 5, I could not put it down. I disappeared into it all weekend. I could feel the level of intelligence I had in my hands and how far ahead of the current options it was, and I used it to do as much work as I possibly could: running many agents at once, remote controlling it from my phone when I was away from the desk, completely hooked.

I do not know how long this window stays open. Others will catch up. But until they do, this model is a real competitive advantage sitting on the table, and I would approach that as deliberately as you can. Because it really is that good.

The verdict

Claude Fable 5 is an excellent model. It is the first one in a while that genuinely feels like more intelligence than what came before, and that gap is the whole game right now. We are at the point where access to more intelligence than the person next to you is the advantage. This is the first model that makes that real. I did engineer-level work without being an engineer. Even priced entirely at frontier rates, the workload still cleared a profit, and run with any care about routing, the return is enormous.

So here is my recommendation. If you can afford it, use it, and use it now, especially on the work where a real quality jump changes the outcome. The first month at full capacity is where the advantage lives, so move fast. Be deliberate about what you run on it until the routing catches up, because the bill is driven by volume, not by the few hard prompts that justify the model.

What an incredible model! 💙

[Claude @claudeai]

Introducing Claude Fable 5: a Mythos-class model that we’ve made safe for general use.

Its capabilities exceed those of any model we’ve ever made generally available.

[Mike Krieger @mikeyk]

It's state of the art on nearly every benchmark we tested and the lead grows the longer the task. Made safe for general release: cyber & bio requests fall back transparently to Opus 4.8 and 95%+ of sessions never see one. $10/$50 on the API, in paid Claude plans today.

[Adrian Nutiu @AdrianNutiu]

@aryanXmahajan OS

[Adrian Nutiu @AdrianNutiu]

@ryancarson Cookied playwright profiles?

[Adrian Nutiu @AdrianNutiu]

@businessbarista AI-native

[Adrian Nutiu @AdrianNutiu]

@alex_whedon @Zephyr_hg Can I please get access to test it? Form filled using my X handle. Thanks.

[Adrian Nutiu @AdrianNutiu]

@tibo_maker SEO

[Adrian Nutiu @AdrianNutiu]

@TheRohanVarma The presentation from your team a few days ago https://t.co/UyeKiM5gxZ

[World of Science @Science_TechTV]

When you change the way you look at things, the things you look at change.

- Max Planck

[Matt Shumer @mattshumer_]

https://t.co/ivXRKXJvQg

[Adrian Nutiu @AdrianNutiu]

@lukas_kf Is this a Merchant of Record solution?

[Adrian Nutiu @AdrianNutiu]

@adcrookes Backlink