Anthropic’s new top-tier model (Claude Fable 5/Mythos 5) is way too pricey and comes with access restrictions, but most of us don’t need it anyway.

What is it?

Fable 5/Mythos 5 is Anthropic’s latest model and a step change for frontier LLMs.

Before we move on, let’s clear up the whole Fable vs. Mythos thing, so I don’t have to keep writing them in tandem:

• Fable 5: Is the same underlying model as Mythos 5 but with additional safeguards in place that automatically reroute certain sensitive queries (e.g. cybersecurity and biology) to Opus 4.8 instead. Available to everyone.

• Mythos 5: The “full” version without any safeguards. Available only to a vetted group of “cyberdefenders and infrastructure providers.”

Claude Fable 5 leads the pack on virtually every benchmark self-reported by Anthropic:

But you don’t have to take Anthropic’s word for it.

The emerging consensus is that Fable 5 truly is a leap forward across the board.

Opinions aren’t nearly as divided as they’ve been for Anthropic’s latest releases, and even the notoriously critical Reddit crowd seems broadly positive.

You can find real-world tests and examples below in “Further reading & watching,” but the short of it is: Fable 5 is a state-of-the-art model that excels at the hardest, most complex long-horizon tasks and agentic coding work.

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How do you use it?

Do you have a paid Claude subscription?

Congratulations: You already have access to Claude Fable 5.

Just open your Claude app or claude.ai, and you’ll see Fable 5 in the model picker:1

Go crazy!

Ah, but you must hurry, fellow explorer: Regular Claude subscribers only have until June 22 to test this mythical model. For from June 23 onward, Fable 5 shall only reveal its powers to those willing to pay its legendary per-usage fees.2

Even before the June 23 switcheroo, Fable 5 will burn through your daily/weekly rate limits at twice the speed of the already expensive Opus 4.X model family.

To summarize, Fable 5 is:

• “Mythos 5 Lite” with safety guardrails that limit its range of use cases.

• Only available in existing paid Claude plans until June 22.

• Anthropic’s most expensive model (double the cost of Opus 4.8).

Looks like the “AI rich vs. AI poor” future predicted is finally here.

But wait…how much does this really matter to you?

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Why should you (not) care?

Okay, real talk.3

For the overwhelming majority of us, Fable 5 is pure overkill.

Unless you do frontier research or work on complex software engineering projects like Stripe’s reported codebase migration, you simply don’t need Fable 5’s wizard skills.

Take a look at these ELO scores for GDPval-AA, which “evaluates AI models on real-world, economically valuable tasks across a wide range of occupations”:

Now be honest: Do Fable’s extra 42 ELO points over Opus 4.8 really matter for your daily tasks? How would you even measure it? Are those marginal gains worth the 2x price hike?

I’ll take a wild swing here and say: No, they aren’t. Not for the average person.

For instance, I consider myself to be at least an intermediate AI user. I work with and test AI tools daily to keep up with all the madness for this newsletter.

I run Claude Code together with Codex, hooked up to my Obsidian vault as their command center.

I’m in the weeds with this stuff, is what I’m saying.

But for most of my use cases, I find that the good old Opus 4.64 is more than plenty. Hell, even Sonnet 4.6 works well for much of what I need done.

(I do ground-breaking scientific research only occasionally and have to date cured at most two or three cancers.)

And it’s not just me, either.

In their recent Fable 5 “vibe check,” Dan Shipper and the crew at Every wrote:

…we found that users who were highly adept with AI—at Level 7 or 8 on our AI adoption ladder—found [Fable 5] paradigm-shifting for their hardest tasks. Users who were lower down on the curve, however, struggled to find something to use it for.

For reference, here are the 8 levels they’re talking about:

So if you’re not “managing multiple long-running agents at the same time,” it’s perfectly fine to give Fable 5 a pass.

You don’t need Fable 5 to draft a quick work email or suggest entertainment ideas for your pet iguana’s birthday party.

Fable 5 is also way overqualified for most of your everyday knowledge work.

It’s like grabbing a bazooka to take down a mosquito.

It’s like taking a Formula 1 car to drive your kids to school.

It’s like asking Fable 5 to come up with a third beat for this three-part analogy.

There’s also early anecdotal evidence that on certain white-collar tasks like writing and front-end design, Fable 5 fares worse than cheaper models due to its dense engineer-style lingo and verbosity.

“But Daniel, I saw that Fable 5 can flawlessly code entire games and build advanced tools from a single prompt! Isn’t that worth something?” you ask, conveniently setting up my upcoming response.

And yes, that appears to be a legitimately magical use of this tech. But:

a) Let’s face it: Most of us aren’t sitting on a backlog of brilliant ideas we’re itching to will into existence. (Although now that the option is available, this may gradually change, which is a net positive for all of us.)

b) One-shotting complex software is bound to push your token spend into the stratosphere, which brings us full circle to square one.5

As it stands, Fable 5 is likely not for me and you…and that’s okay.

Take Fable 5 for a spin on your regular Claude plan until June 22. Poke at it, see what it can do and how it feels compared to what you’re used to.

Then ask yourself if you’re willing to pay double the price—and wait longer for Fable to painstakingly think through every request—instead of sticking to Sonnet or Opus.

My guess?

We won’t be too heartbroken when it’s time to say goodbye to Fable 5 in two weeks.

Further reading & watching

• “Anthropic Just Split the Frontier in Two“ - Product with Attitude

• “Claude Fable 5 and Claude Mythos 5“ - Anthropic

• “Claude Fable 5 – is this Mythos model worth the wait?“ [VIDEO] - How I AI

• “Nine Things About Claude Mythos 5 That Matter If You’re Not an Enterprise Customer“ - The Algorithmic Bridge

• “Vibe Check: Fable 5 Is the Best Coding Model in the World“ - Every

• “What it feels like to work with Mythos“ - One Useful Thing

🫵 Over to you…

Have you tried Fable 5 yet? If so, how much of a difference did you notice on your tasks and projects? Is my hot take way off? Is it way on? Am I rambling?

Leave a comment or drop me a line at whytryai@substack.com.

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If you enjoy my writing, here’s how you can help:

• ❤️Like this post if it resonates with you.

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• 🗣️Comment below—I love hearing your opinions.

Why Try AI is a passion project, and I’m grateful to those who help keep it going. If you’d like to support my work and unlock cool perks, consider a paid subscription:

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Google launched the world’s best model with all the fanfare of a firmware update for a toaster, but the consensus speaks for itself.

What is it?

Simply put, Gemini 3 Pro is the best language model by virtually every measure. This isn’t a subjective value judgement. Here are the benchmarks:

Artificial Analysis Intelligence Index tells the same story:

So do the votes from real users on LMArena:

Even the normally hype-averse, high-signal-to-noise channel AI Explained opens with, “For me, [Gemini 3] genuinely marks a new chapter in the race to true artificial intelligence.”:

Reddit, typically the first place to surface real-world critical takes, is also largely positive:

I could keep going, but I’m sure I’ve made my point: Gemini 3 is genuinely, provably impressive across the board.

No, it isn’t flawless. Yes, it still hallucinates and makes silly errors. But it’s hard to argue with the fact that Gemini 3 Pro is currently the best model available.

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How do you use it?

Good news: Gemini 3 Pro has already rolled out to all Gemini app users.

So just head to gemini.google.com and log in with your Google account:

If you hit a message limit or simply prefer using Google AI Studio, you can also try Gemini 3 over at aistudio.google.com:

Go ahead: Ask Gemini 3 to vibe code an app, perform research, review your work, analyze images, or however you prefer to put models through their paces.

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Why should you care?

When OpenAI was about to announce GPT-5, the hype couldn’t get any louder.

Sam Altman was teasing the launch for days, culminating with this (in)famous and confusing tweet one day before the livestream:

The livestream itself lasted almost 1.5 hours, with the team showcasing their new model’s capabilities like proud parents parading their child at a beauty pageant:

Many have since speculated that this level of pre-launch publicity and showmanship was a big reason for what ultimately ended up being an underwhelming rollout.

When Gemini 3 launched, Google…quietly published a few text-heavy blog posts.

That’s it.

No flashy livestreams. No presentations. No pre-launch hype cycle.

Not even a single premature Death Star tweet by Sundar Pichai.

As you know, I’ve been railing against excessive hype for as long as this newsletter has existed.

So to me, it’s nice to see a company letting a model’s real-world performance do the heavy lifting without having to hype it up. Google’s approach proves that this path works, as long as your model delivers the goods.

I really wish this were the default in the GenAI space.

Who knows, maybe the launch of Gemini 3 marks the moment we all collectively take a chill pill and start talking about major launches in a calm and measured way.

Right?

Damn it!

Further reading & watching

• “Gemini 3 and Google’s Antigravity Trajectory” - AI Ignorance

• “Google Gemini 3 Is a Powerhouse” [YouTube] - Theoretically Media

• “Google Gemini 3 Is the Best Model Ever. One Score Stands Out Above the Rest” - The Algorithmic Bridge

• “Gemini 3 just crushed everything” [YouTube] - AI Search

• “Three Years from GPT-3 to Gemini 3” - One Useful Thing

• “Vibe Check: Gemini 3 Pro, A Reliable Workhorse With Surprising Flair” - Every

🫵 Over to you…

What are your first impressions of Gemini 3 Pro? How does it compare to your current go-to model? Do you have any practical use cases to share?

Leave a comment or drop me a line at whytryai@substack.com.

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If you enjoy my writing, here’s how you can help:

• ❤️Like this post if it resonates with you.

• 🔄Share it to help others discover this newsletter.

• 🗣️Comment below—I love hearing your opinions.

Why Try AI is a passion project, and I’m grateful to those who help keep it going. If you’d like to support my work and unlock cool perks, consider a paid subscription:

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Sora 2 is a genuinely impressive video model, but it’s baked into a social media app built for ultra-short meme clips, which undersells its creative potential.

What is it?

When OpenAI first teased its original Sora model back in February 2024, people went nuts. But Sora wouldn’t launch to the public until December, and by that time, it was well behind many top-tier video models.1

With Sora 2, OpenAI is convincingly back in the game:

There’s a lot to like about Sora 2.

It can handle complex physics, including intricate scenes like gymnastics and fight sequences. It can create videos in many different styles. And, like Veo 3, Sora 2 natively generates its own audio effects and dialogue.

While there are no third-party benchmarks or leaderboard rankings yet, Sora 2 feels roughly on par with Veo 3 (depending on the use case).

Here are three quick comparisons of my own:

Prompt #1: Over-excited blonde influencer is holding a smartphone with the Substack feed on it. She near-screams: “You guys have to check out Why Try AI. It’s so, so good. But what do I know? I don’t even exist!”

Veo 3:

Sora 2:

I don’t know why both models decided to throw in a semi-psychotic giggle at the end, entirely unprompted, but here we are.

Prompt #2: A drunken 1800s pirate tries to use a modern laptop but can’t figure it out. In frustration, he bangs the keys and says “Blast this shiny chest o’ letters—won’t open no matter how I pound it!”

Veo 3:

Sora 2:

I love the bonus details, from Veo 3’s exploding laptop to Sora 2’s off-script method acting improv.

Prompt #3: A horse wearing a top hat tap dances to music on its hind legs

Veo 3:

Sora 2:

Veo 3 looks more realistic, but I like how Sora 2 came up with an entire jingle. No true dancing on hind legs from either model, however.

The bottom line is that both are impressive and not too far apart.

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How do you use it?

First, a whole bunch of caveats:

• The Sora app is only available for iOS for now. We Android users can go fish.

• It’s only out in the US and Canada. (But a VPN works.)

• Access is invite-only, so you’ll have to wait or actively hunt for codes.

Here’s the general process:

1. Download the iOS app or visit sora.com.

2. Sign in with your ChatGPT account.

3. If you don’t have an invite code, see below.

4. Once you have an invite code, enter it to gain access:

   

5. Now you get the familiar prompt box to generate videos. All clips default to 9 seconds. The only thing you can tweak is the orientation (portrait or landscape):

6. Enjoy your Sora videos:

So, how do you get an invite code?

You’ve got a few options:

1. Wait patiently until OpenAI rolls out Sora 2 more broadly.

2. Hunt for new codes on forums like this Reddit mega thread.

3. Try obsessively refreshing sora-invite.vercel.app (that’s how I got my code).

4. Ask a friend who has invite codes to share.

On that note, I’ve been given 4 invite codes when I signed up. Send me a message if you want one. My paid subscribers get priority, but if none of them want a code, I’ll happily give it to any of my readers.

Why should you care?

As much as I like Sora 2 itself, two things bug me about this launch.

First, the invite-only rollout.

This predictably annoyed existing paying customers, especially those on a $200 / month Pro account:

But beyond that, it triggered a proper frenzy with Reddit code-sharing megathreads and shady “codes for money” schemes.

Not exactly a feel-good start.

Secondly, and more importantly, OpenAI has chosen to lean heavily into the “social media app” aspect with this rollout.

On the Sora 2 announcement page, OpenAI has some lofty words about general-purpose world models and how life-changing they will be:

Since then, the Sora team has been focused on training models with more advanced world simulation capabilities. We believe such systems will be critical for training AI models that deeply understand the physical world.

[…]

General-purpose world simulators and robotic agents will fundamentally reshape society and accelerate the arc of human progress. Sora 2 represents significant progress towards that goal. In keeping with OpenAI’s mission, it is important that humanity benefits from these models as they are developed.

But here’s how they position the mobile app:

“Accelerate the arc of human progress,” indeed.

The team spends most of the Sora 2 livestream talking about the social app and its many gimmicks, from video remixing to “Cameos” that let you insert yourself and friends into short video clips.

Look, I like silly shenanigans as much as anyone.

My Sora 2 tests are drunk 1800s pirates and tap-dancing horses.

I’m also bullish on AI's potential to unlock our inner creators and help us tell stories we otherwise couldn’t. Here’s my first-ever post on this very Substack:

But I’m not sure we need yet another social media app with an endless feed of hyper-short video clips.

It’s the “Studio Ghibli version of my face” moment all over again, except this time OpenAI is actively encouraging it.

We have so many short-form dopamine machines already, do we need one built exclusively around AI slop?

Is “Remix this viral meme but with me in it” the pinnacle of our creative potential and the best proof-of-concept use case for a video model as powerful as Sora 2?

To their credit, the OpenAI team appears to have given serious thought to what a responsible social app rollout might look like, at least on paper.2 They talk about steps to prevent doomscrolling, encourage creation vs. consumption, and so on.

Then again, I’m sure the Jurassic Park staff also had seemingly robust safety procedures in place. Also, here’s something that applies equally to both:

Google has always positioned Veo 3 as a tool for serious filmmakers with its Flow platform.

OpenAI, instead, is betting on mainstream appeal and shareability with a meme slop slot machine.

As far as winning the attention game goes, OpenAI’s play is clearly the right move.

But as someone who tries to see AI as a force for good, I can’t help but feel this is a step in the wrong direction.

🫵 Over to you…

What’s your take on OpenAI’s social media play? Does it discredit Sora’s promise by turning it into a viral gimmick, or is it just a fun way to reach more users? Do we need more endlessly scrolling social apps in our lives? Is the “Cameo” feature awesome or dystopian? Am I just an old man yelling at clouds?

Leave a comment or drop me a line at whytryai@substack.com.

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Thanks for reading!

If you enjoy my writing, here’s how you can help:

• ❤️Like this post if it resonates with you.

• 🔄Share it to help others discover this newsletter.

• 🗣️Comment below—I love hearing your opinions.

Why Try AI is a passion project, and I’m grateful to those who help keep it going. If you’d like to support my work and unlock cool perks, consider a paid subscription:

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ByteDance’s Seedream 4.0 gives Nano Banana a run for its money with top-tier image generation and editing.

What is it?

Just when you thought we were done, folks!

It’s only been two weeks since Google’s Nano Banana (Gemini 2.5 Flash Image) “killed” Photoshop, but we already have a shiny new kid on the block: Seedream 4.0.

The latest model from ByteDance goes toe-to-toe with Nano Banana on image editing tasks, while also being on par with GPT image generation at making new images.

This video has plenty of helpful side-by-side image editing tests:

As you can see, Seedream 4.0 handles many niche challenges better than either GPT image generation or Nano Banana (often both).

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How do you use it?

Right now, there’s no official free way to try Seedream 4.0.1

Update 15-09-2025: You can now use Seedream 4.0 for free on LMArena:

Simply follow this guide I wrote for Nano Banana but select seedream-4 or seedream-4-high-res from the dropdown:

Several shady third-party sites claim to offer free Seedream 4.0 generations, but the ones I tested all use outdated models under the hood. ByteDance’s China-facing platform jimeng.ai does offer free daily Seedream 4.0 credits, but I couldn’t make it work without a local telephone number.

I expect ByteDance might soon add Seedream 4.0 to its Dreamina site.2

For now, if you’re outside China, your best option is a paid subscription to one of the following:

• krea.ia

• freepik.com

If you don’t want to commit to a subscription, fal.ai lets you try Seedream 4.0 on a pay-per-use basis (3 cents per image):

1. Navigate to fal.ai

2. Sign up (or sign in with e.g. your Google or GitHub account)

3. Purchase credits over at https://fal.ai/dashboard/usage-billing/credits

4. Pick the Seedream 4.0 model you want to use:

   1. For image generation:
      https://fal.ai/models/fal-ai/bytedance/seedream/v4/text-to-image

   2. For image editing:
      https://fal.ai/models/fal-ai/bytedance/seedream/v4/edit

5. Type in your prompt and click “Run” to generate or edit the image

   

I used the long test prompt from my GPT-4o showdown article:

Candid photo of a purple steampunk platypus with wings on stage in a comedy club. To the left of the platypus is a cyberpunk goose playing a saxophone. Behind them is a show banner with the words “Top Billed.” In front of the stage, in the audience, is a dieselpunk duck. The duck is holding a handwritten show program that says “Welcome to Top Billed! No beaks were harmed in the making of this lineup. Prepare for laughs, feathers, and unpaid sax solos.”

Here’s the best of four attempts by Seedream 4.0:

It sticks to the prompt and almost nails the text (apart from the last word). Here’s a side-by-side view of Seedream 4.0, Nano Banana, and GPT-4o—click any image to open it in full resolution:

Seedream 4.0 is also the only model trying to honor the “candid photo” look.

Take Seedream 4.0 for a spin with your own prompts, especially those you felt were too tricky for other image models. See if it’s an improvement.

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Why should you care?

ByteDance has just joined the ranks of OpenAI and Google with a context-aware image model that has built-in reasoning abilities. This means Seedream 4.0 doesn’t just blindly follow instructions but truly understands the request and has the world knowledge to act on it.

So I can ask it for something as broadly defined as this:

Four-panel cartoon that explains how coffee makes it to the shop

And it might return something like this:

With Seedream 4.0, China now has a top-tier contender in yet another GenAI field, which reminds me of when Kling and Hailuo AI came seemingly out of nowhere to dominate AI video at the end of 2024. (Several months later, reasoning models had their DeepSeek R1 moment.)

Every time I feel we’re near the peak of what’s possible with AI image generation, a new model comes out to raise the bar.

And it looks like we’re not quite done for this year, because OpenAI is allegedly gearing up to update its own image model, fueling wild speculation:

As someone who originally started Why Try AI because of AI image models, I’m excited to see where it’s all heading.

🫵 Over to you…

Have you tried Seedream 4.0 yet? Are you following the AI image field closely? What is your favorite image model or platform?

Leave a comment or drop me a line at whytryai@substack.com.

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Thanks for reading!

If you enjoy my writing, here’s how you can help:

• ❤️Like this post if it resonates with you.

• 🔄Share it to help others discover this newsletter.

• 🗣️Comment below—I love hearing your opinions.

Why Try AI is a passion project, and I’m grateful to those who help keep it going. If you’d like to support my work and unlock cool perks, consider a paid subscription:

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It’s time for another Thursday “Hot Take.”

TL;DR

Gemini 2.5 Flash Image (aka “Nano Banana”) is now the world’s best image model that also excels at detail-preserving edits, but let’s not equate “good enough for the average Joe” with “Photoshop extinction event.”

What is it?

Gemini 2.5 Flash Image is Google's latest image model, which made quite a splash last week under its pre-release nickname, "Nano Banana."1 Two days ago, Google officially claimed ownership of the model.

While Nano Banana is topping leaderboards for text-to-image generation, what truly makes it special is its ability to make precise edits to existing images while keeping characters consistent and preserving details.

Here's a quick taste:

It can blend separate characters and insert them into novel settings:

And it can handle multi-turn edits without losing key details of the original image:

In short, Nano Banana is pretty damn neat.

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How do you use it?

If you want to try Nano Banana for yourself, you've got three options...and all of them are free!

Option 1: Gemini app

1. Go to gemini.google.com or open the Gemini app on your phone

2. Start a new chat.

3. Upload the image(s) you want to edit.

4. Request your changes by simply describing them.

That's it!

If you just want to create new images, select "Create images" in the Tools menu, then describe the image you want:

Like so:

Option 2: Google AI Studio

1. Go to aistudio.google.com and sign in with your Google account

2. Select Chat in the left-hand column

   

3. Select "Gemini 2.5 Flash Image Preview" from the right-hand model picker

   

4. Now you can upload images to edit or request new ones as above

   

Like so:

Option 3: LM Arena

1. Head to lmarena.ai

2. Select "Direct Chat" in the top dropdown

   

3. Click on "Generate Images" under the prompt box

   

4. Select "gemini-2.5-flash-image-preview" in the top dropdown

   

5. Now you can upload images to edit or request new ones as above.

Important note: Unlike Gemini and Google AI Studio, LM Arena reserves the right to make your prompts public for research purposes.

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Why should you care?

Character consistency has long been the holy grail of AI image and video generation. It allows for long-form storytelling, precise image remixing, different shots of the same setting, and more.

Right now, no other AI model is as good at keeping characters and image details consistent as Nano Banana.

The previous best image model, GPT-4o image generation, struggled with this. In fact, it was so bad at preserving details and characters over multiple turns that it spawned a short-lived-but-fun trend: People would upload an image and repeatedly ask ChatGPT to "Create a replica of this image. Don’t change anything." The results were disturbing:

So yes, Nano Banana is the closest the average user can get to making nuanced image edits in natural language.

But in its typical fashion, the Internet jumped from that to "Photoshop is dead" in a split second.

Seriously, go type in “nano banana photoshop” into your search bar.

I’ll wait.

No? Fine, I’ll do it for you:

Why must we constantly pair the arrival of one awesome tool with the death of something else? Is it the drama?

We already killed Google back in early 2023:

Now, Google is back from the grave to murder Adobe?

Will this cycle of violence ever end?!

Jokes aside, there's quite a gap between "This AI tool lets people make quick and precise image edits" and "Professional Photoshop workflows are now obsolete.”

For one, Nano Banana still has many limitations. Google itself highlights that the model struggles “with small faces, accurate spelling, and fine details in images.”

When I threw my “long text” prompt from this image model showdown at Nano Banana, it couldn’t nail the show program text after multiple tries—something that GPT-4o image generation did quite consistently:

This AI Search video, while very positive about Nano Banana, has a whole section dedicated to tests where other models do better:

But also—and I know this is a very controversial statement—there’s probably a tiny difference between making quick AI-powered edits and doing professional-grade visual design work.

How about we enjoy playing with this new, super cool tool without instantly proclaiming the death of all professional design?

Have fun out there!

🫵 Over to you…

Have you tried editing images with Nano Banana? What’s been your experience? Have you run into other obvious limitations? I’d love to hear your thoughts!

Leave a comment or drop me a line at whytryai@substack.com.

Leave a comment

Thanks for reading!

If you enjoy my writing, here’s how you can help:

• ❤️Like this post if it resonates with you.

• 🔄Share it to help others discover this newsletter.

• 🗣️Comment below—I love hearing your opinions.

Why Try AI is a passion project, and I’m grateful to those who help keep it going. If you’d like to support my work and unlock cool perks, consider a paid subscription:

Subscribe now

HeyGen’s Avatar IV creates lifelike, expressive talking avatars from a single image that can lip-sync to any audio or script—are deepfakes too easy now?

What is it?

Avatar IV is a new AI avatar model from HeyGen:

Not so long ago, creating a custom avatar with HeyGen or Synthesia required you to record a training video and submit it for professional processing.

Now, it takes one image, one script (written or recorded), and a few minutes. That’s it:

As you can see, the avatars don’t just sync their lips to the voice track—their movements and microexpressions accurately match the tone, etc.

It’s quite uncanny.

How do you use it?

The process is disarmingly simple.

On the main dashboard, click the Photo to Video with Avatar IV option:

This brings up a pop-up where you can complete the entire procedure in one go:

From here, you just:

1. Upload a single image.

2. Type out your script (or upload a pre-recorded voice clip).

3. Select a template voice (if not using the pre-recorded audio above).

Here’s how that might look:

Now you just click “Generate video” and wait a minute or so. Then you get your video:

Ignore the frozen background and the voice mismatch for a second.

This is pretty solid for close to zero effort on my part.

Anyone can create up to three 10-second clips a month for free, while paid accounts can make 30-second videos.

Why should you care?

Because deepfakes are quickly heading into “So easy, your grandma’s goldfish can make one” territory.

To be sure, talking avatars are far from a brand-new concept.

D-ID has let people create custom talking avatars for years. So has Synthesia.

Even the single-image-to-talking-avatar tech isn’t that new.

Last year alone, I wrote about three such models:

• March 3, 2024: Alibaba’s Emote Portrait Alive (EMO)

• March 17, 2024: Google’s VLOGGER

• April 21, 2024: Microsoft’s VASA-1

But even then, the feature struck me as borderline creepy.

I could see why those models were research papers rather than consumer products.

To wit, here’s exactly what I said about VASA-1 (emphasis added):

“VASA-1 is outright scary. Given just a single image of a person paired with an audio clip, VASA-1 makes a realistic talking head of that person. Optional controls let you adjust the speaker’s emotion, camera view, and more. Understandably, Microsoft is not planning to make the model available at this time.”

Just one year later, thanks to Avatar IV, this technology is now mainstream.

HeyGen’s version stands out in several ways:

• It’s highly accessible through a clean point-and-click interface.

• It can feel quite realistic thanks to lifelike microexpressions.

• It exists inside a popular product with at least 3 million monthly active users (and the potential to reach 130 million more as a native app inside Canva).

As such, Avatar IV reaches the average user in a way above research demos and previews never could.

But why take my word for it?

Everything I say above is the literal sales pitch, straight from the horse’s mouth:

“A video that feels real, not rendered.”

Indeed.

Look, I’m a big-time AI enthusiast.

I even have a newsletter about AI; you might’ve heard of it.

And while I tend to be cautiously optimistic about tech, sometimes it feels like we’re making increasingly high-impact tools available too broadly, too quickly.

Sure, my test video above isn’t going to fool anyone. I picked an image with a busy background that doesn’t get properly animated and a random, generic AI voice.

But what happens when you pair HeyGen’s Avatar IV with a better starting image and recent voice-cloning tech? Here are just a few that I covered:

• ElevenLabs Voice Cloning

• PlayHT Voice Changer

• Zyphra Zonos-v0.1

Many of them can clone a voice from only 10 seconds of input audio.

It doesn’t take a huge mental leap to imagine all sorts of unpleasant shenanigans:

• Fake UGC influencers

• Political or celebrity deepfakes

• Scams featuring friends and family

Last February, a Zoom deepfake convinced a finance worker to pay $25 million to scammers. This year, a would-be scammer needs just one image.

Even if you don’t think HeyGen’s avatars are that convincing, how long will it take for them to evolve? At the current pace of AI developments, next Tuesday is a safe bet.

So while I appreciate all the positive use cases like guided tutorials or personalized messages, our guardrails better soon catch up with this new reality.

What do you think?

🫵 Over to you…

Have you tried Avatar IV yet? If so, what did you think of the output?

What’s your general take on this tech? Are you worried about it enabling deepfakes and scams? Or do you trust that we’ll figure it out? Share your hopes and your fears!

Leave a comment or drop me a line at whytryai@substack.com.

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Yet another Thursday post in the “Hot Takes” format.

TL;DR

Genspark’s Super Agent is a genuinely competent general agent that reasons and performs complex tasks, but it’s been overlooked in the avalanche of agent hype.

What is it?

Last week, Genspark announced its “fast and reliable” Super Agent:

That video currently sits at a criminally low 32K views compared to Manus AI’s 700K+, certainly not helped by the monotonous presentation accompanied by a generic 1990s TV infomercial soundtrack.

But under the hood, Super Agent is surprisingly effective at orchestrating dozens of moving parts to complete complex tasks.

Super Agent appears to be powered by a reasoning model—perhaps o1 or o3-mini—which processes your request, then calls on Genspark’s specialized agents, LLMs, image models, video models, and other tools based on the task.

Among other things, Super Agent can use:

• Image models to make visuals

• Video models to create short clips

• Text-to-speech models to generate voices

• Coding skills to write functioning code

• Deep research agents for, uh, deep research

• …and lots of other stuff.

It also has a “Call for me” option that uses a voice agent to call up and talk to real people and businesses on your behalf, but I haven’t tested this one myself.1

Super Agent beats both Manus AI and OpenAI Deep Research on the GAIA Benchmark for general assistants:

Let’s be honest, though: We’ve been inundated with such benchmarks lately.

These days, my eyes usually glaze over when I see another comparison chart, which helps explain why the announcement got only a bland mention in my last roundup.

But having now used Super Agent for several real-world tasks, I can say that this is the first time I’ve been impressed by a general AI agent.

In the past week, Genspark Super Agent:

• Helped me research, consolidate, and code the GPT-4o swipe file for my paid subscribers.

• Found a fast charger for my phone in a Danish online store. It nailed this task, arriving at the same recommendation I did after longer manual research. (Read the chat.)

• One-shot coded a landing page2 based on a simple request, done as a top-level test of its capabilities. (Read the chat.)

• …and did equally well in several other minor test tasks.

In short, it just works!

Genspark lists 11 real-world examples, and I’d say they’re representative of Super Agent’s actual capabilities.

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How do you use it?

It’s stupid simple:

1. Go to genspark.ai (sign up for a free account if needed).

2. Type your task or request into the big box on the front page:

   

3. Watch Super Agent break down its thinking as it works through your task.

   

At each step, you can click the “View” button to expand the details and see the agent’s thinking.

Once the initial task is finished, you can request changes via chat.

Here’s Super Agent’s one-take output for the above request:

Genspark's page about Genspark

Genspark gives you 200 daily credits for free, which is decent for testing text-based and coding tasks but probably won’t be enough for stuff that requires more advanced third-party models.

Why should you care?

AI agents have been hyped relentlessly since 2023 when BabyAGI and AutoGPT were making a splash.

But the gap between flashy hypothetical demos and agents that actually work has been hard to close.

In October 2024, we started getting first glimpses of functioning general agents3 when Anthropic launched “Computer Use.”

Soon, we had OpenAI’s “Operator” in January 2025, Convergence AI’s “Proxy” in February, and finally “Manus” in March.

But they all came with caveats like waitlists (Manus), invite-only or expensive research previews (OpenAI), the need to install and run clunky local scripts (Anthropic), and so on. Proxy is excellent, but it’s primarily confined to web browsing tasks.

To me, Genspark’s Super Agent is the first general agent that runs entirely inside a virtual environment, doesn’t require access to your computer, can reason and take many diverse actions, and is readily accessible by everyone.

It’s one of those rare moments in AI where reality exceeds the marketing hype.

To be sure, Super Agent is not immune to the usual AI blind posts like hallucinations, imperfect web browsing, and so on. At the same time, I find that it can usually fix things based on simple feedback without messing up something else in the process.

The best part? You don’t have to take my word for any of this.

Just sign up for a free account and take Genspark’s Super Agent for a quick spin.

You might be as positively surprised as I was.

🫵 Over to you…

Have you tried Genspark Super Agent? What were your impressions? Have you had the chance to compare it to Manus or other AI agents?

I’d love to hear your thoughts about it.

Leave a comment or drop me a line at whytryai@substack.com.

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TL;DR

ChatGPT can now see, reason about, create, and edit images right inside your chat—stealing the thunder from traditional image models.

What is it?

It’s OpenAI’s answer to Gemini 2.0 Flash with native image generation, which I covered just two weeks ago:

In that post, I dropped this pretty spot-on prediction:

If I were a betting man, I’d say we’re about to see OpenAI follow suit. We already know that GPT-4o can do the same stuff:

After all, the “o” in GPT-4o stands for “omni” or “omnimodal.”

It’s just that most of us weren’t given access to all of the modalities yet.

In a recent Reddit AMA, OpenAI’s Chief Product Officer Kevin Weil confirmed that multimodal image generation was coming:

Now that Google’s version is out, the pressure is on OpenAI to catch up.

The landscape is changing fast.

We may soon wave goodbye to the era of separate features stitched into unholy amalgams. Instead, we’ll have truly omnimodal models handling everything on their own.

Yesterday, my prediction came true with this announcement post and a live stream:

But unlike Gemini 2.0, images created by 4o are of really high quality with an even more precise level of detail and text accuracy.1

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How do you use it?

There are two ways.

1. Directly in ChatGPT

This is the most intuitive option for most ChatGPT users. Native image generation is currently rolling out to all accounts.

Simply go to ChatGPT and ask the default GPT-4o model to create an image.

Here’s how you’ll know whether native image generation has kicked in for you:

1. You’ll see the image appearing line by line, instead of DALL-E 3 working on it behind the scenes as before:

2. You’ll see a different mix of buttons when clicking on the finished image. In the past, you were able to see the prompt that ChatGPT fed to DALL-E 3:

   

   Now, you’ll see the rating and download options and the “Select” tool that lets you pick specific areas of an image to edit.

   

   There is no DALL-E 3 prompt: GPT-4o creates the image all by itself based on your request.

3. You’ll be able to request complex, precise changes in natural language and GPT-4o will handle these like a pro:

   

   ….and then….

   

Go ahead and give it a try!

2. On Sora.com

If you pay for ChatGPT, you can also use sora.com.

Simply navigate to the site, then switch the format from “Video” to “Image”:

You can even work with a reference image. I uploaded this lil’ guy made in Midjourney:

…asked Sora to throw him into an epic sci-fi battle…

…and got this:

Not too shabby at all.

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Why should you care?

First off, native image generation is an entirely new paradigm and a huge deal in its own right, as I explained in the Gemini 2.0 Flash piece.

But the implications become even greater when you pair it with output quality that can suddenly compete with many text-to-image diffusion models.

Would you rather work with an old-school image model, which:

• Doesn’t follow directions as precisely

• Struggles to spell stuff accurately

• Can’t actually “see” what it generates and make seamless tweaks

• Has limited understanding of the context of your request and can’t discuss it back and forth

Or would you prefer a language model to do all of the above, better, via natural instructions within a familiar chat interface?

It’s a rhetorical question, but you’re welcome to shout the answer out loud at your screen!

I don’t see how regular image tools can compete with just how easy it is to request pictures and make changes via chat.

Many people already treat classic diffusion models like unapproachable black boxes.

Hell, I wrote about the uselessness of “splatterprompting” over two years ago:

I debunked Midjourney photography prompts last December:

…yet I still keep seeing people use both of these constantly, simply copy-pasting random-descriptor-filled prompts at scale without reflection.

When you give users a model that intuitively knows what they want and can accurately create it on demand, they’ll flock to this model at the expense of alternatives.

Perhaps nothing illustrates this better than the launch of a new, state-of-the-art image model, which by cruel fate happened just before the OpenAI announcement.2

I’m talking about Reve AI.

Only a day earlier, Reve AI made a splash by releasing Reve Image: the “best image model in the world”:

Reve Image currently tops text-to-image leaderboards from both Artificial Analysis and imgsys.

By all accounts, it’s an outstanding model.

I ran a few tests and am impressed by Reve’s quality, instruction following, and text rendering.3

Reve is also free to use, so go ahead and try it over at preview.reve.art.

But here’s the thing: While text-to-image nerds like myself will happily try new sites and geek out about marginal improvements in diffusion models, most regular users will want something that “just works” inside a tool they’re already using.

And that’s exactly what the new 4o image creation in ChatGPT does.

My guess?

ChatGPT’s newfound drawing skills will open the floodgates for chat-based image generation by mainstream audiences.

I doubt it will wipe out existing text-to-image prompting methods overnight, but it’ll certainly shift the conversation toward a more intuitive way of doing things.

In fact, I won’t be surprised if we eventually look back at text-to-image prompt input boxes as relics of a bygone era.

Am I wrong?

🫵 Over to you…

Am I too quick to dismiss current image models and their interfaces? Can chat-based image creation and text-to-image prompt boxes coexist? Do they serve different purposes and appeal to different groups of people?

Let’s talk!

Leave a comment or drop me a line at whytryai@substack.com.

Leave a comment

Thanks for reading!

If you enjoy my writing, here’s how you can help:

• ❤️Like this post if it resonates with you.

• 🔗Share it to help others discover this newsletter.

• 🗩 Comment below—I love hearing your opinions.

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Hot Takes are occasional timely posts that focus on fast-moving news and releases, in addition to my regular Thursday and Sunday columns.

If Hot Takes aren’t your cup of tea, simply go to your account at www.whytryai.com/account and toggle the “Notifications” settings accordingly:

TL;DR

Gemini 2.0 Flash Experimental can create and edit images natively.

What is it?

Yesterday, Google’s Logan Kilpatrick announced the release of Gemini 2.0 Flash with native image generation:

Gemini can now create multi-step illustrated stories from a single prompt, edit existing images directly, rework uploaded images, and more.

The best part?

It’s 100% free to try.

How do you use it?

The easiest way to try the new model is via Google AI Studio.

Here’s the step-by-step process:

1. Go to aistudio.google.com and log in with your Google account.

2. Select “Gemini 2.0 Flash Experimental” from the model picker. Note: You want the gemini-2.0-flash-exp model, not the default Gemini 2.0 Flash. (I know, I know.)

   

3. Type your request into the prompt field at the bottom.

   

4. Enjoy your results.

   

Now, if you look closely at the image, you’ll notice that the output quality is underwhelming, to say the least.

You’re not alone:

Far from it:

In a world of so many impressive image models1, Gemini 2.0 Flash image quality is way behind the curve.

But focusing on that understates the real game-changer: It’s the same model handling everything under the hood: text, image understanding, and image generation.

Let’s unpack why that’s a big deal.

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Why should you care?

Because you can now finally hide the elephant!

Bear with me, it’ll all make sense in a moment.

You see, for years now, publicly available AI models have been mostly siloed.

You’d have one model for text generation, a separate model to create images, and a third one for converting speech into text and back again.

When you ask for an image in, say, ChatGPT, here’s what happens behind the scenes2:

1. The language model (e.g. GPT-4o) turns your request into a text-to-image prompt.

2. GPT-4o sends this prompt to OpenAI’s image model: DALL-E 3.

3. DALL-E 3 generates the image based on the prompt from GPT-4o.

4. GPT-4o replies to your request in the chat and attaches the DALL-E 3 image.

This disconnect is the real reason behind the hilarious “hide the elephant” exchange mocked by :

…and then…

The problem here isn’t that GPT-4o doesn’t know what the user wants.

It’s that—when GPT-4o explicitly tells DALL-E 3 to hide the elephant—DALL-E 3 hears “elephant” and adds it to the image instead. Image models don’t do well with negative instructions, which is why special “negative prompt” fields exist in the first place.

Now watch this:

Gemini 2.0 Flash handles the task like a champ—precisely because it combines text understanding, image understanding, and image generation under one umbrella.

Thanks to this, Gemini is also able to keep the rest of the image intact exactly as is!

For comparison, requesting even minor changes in ChatGPT will generate a new, somewhat similar image3:

This true multimodality opens up a whole range of possibilities, such as combining objects across images…

…adding custom text into precisely defined locations…

…manipulating characters in an image…

…and more.

Go ahead: Take Gemini 2.0 Flash for a spin and explore what it’s capable of!

Are we entering a new multimodal era?

Want to hear the crazy part?

On paper, the Gemini family has been natively multimodal since it was first announced one-and-a-half years ago.

Here’s a quote from my December 2023 round-up:

…Gemini is natively multimodal. This means that unlike GPT-4, which is trained purely on text and gets its multimodality from add-on modules, Gemini is trained on different modalities from the start. This should make it far more capable of switching effortlessly between many types of input and output.

As such, Gemini was likely capable of these feats all along.

However, AI labs have initially been hesitant to unlock full multimodality for general audiences.

Things started to change last year when OpenAI rolled out the “Advanced Voice Mode” to ChatGPT users. This mode doesn't use text-to-speech / speech-to-text conversion to enable voice conversations. It natively understands what you’re saying and can respond in kind.

Now, Google is giving us multimodal image generation, too.

If I were a betting man, I’d say we’re about to see OpenAI follow suit. We already know that GPT-4o can do the same stuff:

After all, the “o” in GPT-4o stands for “omni” or “omnimodal.”

It’s just that most of us weren’t given access to all of the modalities yet.

In a recent Reddit AMA, OpenAI’s Chief Product Officer Kevin Weil confirmed that multimodal image generation was coming:

Now that Google’s version is out, the pressure is on OpenAI to catch up.

The landscape is changing fast.

We may soon wave goodbye to the era of separate features stitched into unholy amalgams. Instead, we’ll have truly omnimodal models handling everything on their own.

So yes: You can choose to focus on how Gemini’s current image quality is nothing to write home about.

But if you do, you’ll miss the much bigger shift unfolding right under our noses.

🫵 Over to you…

Have you already tried Gemini 2.0 Flash for image generation? Did you discover any awesome use cases that I haven’t covered above? I’d love to hear what you think!

Leave a comment or drop me a line at whytryai@substack.com.

Leave a comment

Thanks for reading!

If you enjoy my writing, here’s how you can help:

• ❤️Like this post if it resonates with you.

• 🔗Share it to help others discover this newsletter.

• 🗩 Comment below—I love hearing your opinions.

Why Try AI is a passion project, and I’m grateful to those who help keep it going. If you’d like to support my work and unlock cool perks, consider a paid subscription:

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Anthropic just launched the impressive Claude 3.7 Sonnet, but it’s still locked inside the old interface without built-in access to the web.

What is it?

Claude 3.7 Sonnet is a new state-of-the-art model that incorporates a traditional LLM and a reasoning model in one.

It goes toe-to-toe with or outperforms frontier models like Grok 3, DeepSeek-R1, and OpenAI’s o-family on most benchmarks:

How do you use it?

Simple: Just go to the usual claude.ai website.

If you’ve never used Claude before, you’ll need to create an account.

If you have, Claude 3.7 Sonnet will now be the default model:

Now you’ll know just what to do: You’ve seen plenty of these chat interfaces before.

Anthropic calls Claude 3.7 Sonnet “the first hybrid reasoning model on the market.”

If I’m honest, I’m somewhat puzzled by the way this is currently handled.

To me, the word “hybrid” entails a single model that independently switches between standard and reasoning modes without the user’s involvement.

But Anthropic still presents us with a manual thinking mode selection in its interface:

Grok has a “Think” option that does the same:

And here’s OpenAI’s version that currently lets free users tap into o3-mini:

The way I interpret this is that Claude 3.7 Sonnet has a “basic“ thinking mode for free users and a “super” thinking mode reserved for PRO accounts.

So this dropdown decision is driven by marketing considerations.

Nevertheless, I find it unnecessarily confusing to have dropdown selections for an ostensibly hybrid model.

Anthropic did kind of beat OpenAI to the punch here, as Sam Altman recently announced that they’re moving in a similar direction of a unified system without model pickers.

By early accounts, Claude 3.7 Sonnet is fantastic, especially when it comes to coding.

Yet I can’t help but feel that Anthropic is shooting itself in the foot by not giving it Internet access.

Why should you care?

Having a cutting-edge model that can’t browse the web is a bit like taking a genius scientist to a world-class research lab and then shackling them to a corner table next to a bunch of dusty encyclopedias.

Claude can’t look up information about new coding tools and features or visit troubleshooting forums. It can’t apply its reasoning skills to real-time developments or read newly released scientific papers.

All of this might make Claude 3.7 Sonnet less competitive against otherwise weaker models that have direct Internet access.

Of course, this is mostly an issue for regular users, since developers can always use the API to incorporate Claude into existing, web-enabled applications. There are also ways to give Claude indirect web access via tool use or third-party solutions.

Still, Anthropic could turn Claude 3.7 Sonnet into an even more appealing package by granting it out-of-the-box Internet access. Hell, I might even be persuaded to switch from my ChatGPT Plus account if that happens.

To be sure, I’ve been quite skeptical about AI search before.

But we’re starting to see impressive products like OpenAI’s “Deep Research,” which make up for the inherent issues of AI web browsing with their ability to reason through problems and identify quality sources.

Now, I’m not saying that we need yet another Deep Research product on the market.

But I’m also not not saying that.

🫵 Over to you…

Have you had the chance to try Claude 3.7 Sonnet for yourself? What have been your early impressions? I’d love to hear if there are specific use cases in your life that Claude 3.7 Sonnet has been especially well-suited for.

Also: Do you consider the lack of web access as much of a drawback as I do? Or is it pretty negligible, as far as you’re concerned?

Leave a comment or drop me a line at whytryai@substack.com.

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Hot Takes are occasional timely posts that focus on fast-moving news and releases, in addition to my regular Thursday and Sunday columns.

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OpenAI just released a “Deep Research” agent that can autonomously research and reason about complex topics. It’s the third “Deep Research” tool in less than two months, but it’s arguably the most capable.

What is it?

OpenAI describes it as “an agent that uses reasoning to synthesize large amounts of online information and complete multi-step research tasks.”

Basically, you ask Deep Research a question or give it a research task, and it sets off on its own to create a plan, read through relevant literature, explore different research avenues based on its findings, and seek additional information to produce the final report.

By early accounts it’s quite impressive, offering a “near PhD-level analysis” according to Ethan Mollick.

It’s also, confusingly, the third such agent with exactly the same name.

Here’s the timeline:

• December 11, 2024: Google released a “Deep Research” assistant for paying Gemini Advanced users.

• January 27, 2025: Genspark launched a “Deep Research” agent with similar capabilities.

• February 2, 2025: OpenAI announced its own “Deep Research” agent, because naming stuff is hard, you guys!

But while the three tools are conceptually similar, they aren’t equally capable or equally accessible.

Let’s take a quick look at each of them and your options.

How do you use it?

In order from most to least expensive…

1. OpenAI’s deep research

Right now, OpenAI’s “deep research” is available exclusively to people paying $200 per month for a ChatGPT Pro account. Plus and Teams accounts might have to wait a month before trying it.

If you’re one of the fancy people with a Pro account, you’ll want to toggle the “Deep research” button when entering your query:

Ethan Mollick says that OpenAI’s deep research agent truly appears to process and reason about the information, pursue supporting studies, and dig deeper when necessary on its own. It uses a version of OpenAI’s upcoming, most powerful o3 reasoning model to do this.

If you can afford it, it’s the best independent AI research agent currently available.

2. Google’s deep research

Google’s version has been out the longest and costs $19.99 per month, but you can try it for free with a one-month trial of Gemini Advanced.

To use it, select “1.5 Pro with Deep Research” from the model dropdown:

Google’s “Deep Research” is great at quickly scanning lots of sites but appears to have less innate intelligence.

Here’s the summary from Ethan Mollick’s article:

Google surfaces far more citations, but they are often a mix of websites of varying quality (the lack of access to paywalled information and books hurts all of these agents). It appears to gather documents all at once, as opposed to the curiosity-driven discovery of OpenAI’s researcher agent. And, because (as of now) this is powered by the non-reasoning, older Gemini 1.5 model, the overall summary is much more surface-level, though still solid and apparently error-free. It is like a very good undergraduate product.

And then there’s the free version for the rest of us…

3. Genspark’s deep research

Two weeks ago, I looked at Genspark’s image generation agent.

You find the new “Deep Research” agent on the same Genspark Agents page:

After you give “Deep Research” a task, it engages GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro to create a research plan:

You can then edit or approve the plan, at which point the agent independently crawls and summarizes hundreds of sources and sends a report to your inbox. This takes around 20 minutes.

Genspark reports contain mind maps and comparison tables for an easy overview:

If I’m honest, the research quality here is a mixed bag.

In my example, Genspark started strong with the OpenAI and Google examples but then cast a wider net to include DeepSeek, Claude, and other entries that didn’t have “Deep Research” in their name. You can view the final report for yourself.

But as it stands, Genspark’s deep research agent is the only free version on the market. Everyone gets a few uses for free, and you can also use my invite link for a free month of Genspark Plus.

Why should you care?

We appear to be entering an era of narrow yet actually useful agents.

Unlike the hypothetical “do anything” browser-using AI agents, which remain clunky, error-prone, and often require the user’s active involvement,1 deep research agents are capable of completing the task on their own.

Crawling and summarizing information on the Internet lends itself quite well to this kind of fire-and-forget agentic approach.

Note that “Deep Research” agents are not immune to hallucinations. You must still exercise the “trust but verify” approach to any information they gather and conclusions they reach. But even in their current state, they can meaningfully condense hours and days of research into a much shorter timeframe.

As more powerful reasoning models become mainstream, deep research agents are only going to get better.

So I strongly suggest you test drive whichever existing version you can afford.

This way, you’ll be better prepared for what’s soon coming.

🫵 Over to you…

Have you already used any of these Deep Research agents? What’s been your impression? I’d love to hear from all of you Mr. and Mrs. Money Bags who have tried the expensive OpenAI version.

Leave a comment or drop me a line at whytryai@substack.com.

Leave a comment

Hot Takes like the above are occasional timely posts that focus on fast-moving news and releases, in addition to my regular Thursday and Sunday columns.

If Hot Takes aren’t your cup of tea, simply go to your account at www.whytryai.com/account and toggle the “Notifications” settings accordingly:

Subscribe now

Hot Takes are occasional timely posts that focus on fast-moving news and releases, in addition to my regular Thursday and Sunday columns.

If Hot Takes aren’t your cup of tea, simply go to your account at www.whytryai.com/account and toggle the “Notifications” settings accordingly:

Subscribe now

TL;DR

DeepSeek released an image model called Janus-Pro-7B, which some claim makes better images than DALL-E 3 and Stable Diffusion, but it’s nowhere near in my tests.

What is it?

DeepSeek describes Janus-Pro-7B as “a novel autoregressive framework that unifies multimodal understanding and generation.”

In short, this means you can use the same model to process image inputs and generate new images. This makes Janus-Pro-7B quite flexible, combining the capabilities of task-specific models in a single one.

That—and the fact that it’s yet another open-source model—is worthy of praise.

But DeepSeek also shared a few benchmarks that show Janus-Pro-7B outperforming OpenAI’s DALL-E 3 and Stable Diffusion 3 Medium:

Here’s the thing: GenEval and DPG-Bench are benchmarks that measure prompt adherence—how well a model follows directions in the text prompt.

They say absolutely nothing about the aesthetic quality of the model.

Still, this didn’t stop the Internet from instantly proclaiming Janus-Pro-7B the new king of images:

And while better prompt adherence is nothing to sneeze at, you also want your image model to create something that looks good.

Unfortunately for Janus, most of what it generates is hot garbage.

Let me show you.

How do you use it?

To test Janus-Pro-7B for yourself, head on over to its Hugging Face page and select any of the “Spaces” using the model on the right-hand side:

I’ll go with the official deepseek-ai/Janus-Pro-7B. Here’s my 2-minute walkthrough:

If you saw the video, you’ll know that Janus-Pro-7B:

1. Hallucinates nonexistent details in uploaded images.

2. Creates new images that look like Lovecraftian horrors.

Here are three sample prompts and side-by-side comparisons against DALL-E 3 and Stable Diffusion 3, the models DeepSeek chose to benchmark against:

Prompt: “photo of a samurai in a traditional outfit holding a sci-fi blaster, futuristic skyscrapers with neon signs in the background”

Prompt: “cartoon illustration of a blue cat and a green dog wearing party hats, sitting on a park bench and looking up at Saturn”

Prompt: “a cute purple robot holding up a cardboard sign that reads “I can spell better than you!”

Janus—how do I put this mildly—sucks ass.1

If I’m being generous, I’ll put Janus on par with Midjourney V2. (Reminder: We currently have Midjourney V6.1 with Midjourney 7 set to come out soon.)

Curiously, DeepSeek chose to benchmark against the outdated DALL-E 3 and Stable Diffusion 3 Medium instead of the newer, better image models, many of which are also way better at prompt adherence and spelling than DALL-E 3 (see my recent test).

Why should you care?

DeepSeek released the truly impressive DeepSeek-R1 reasoning model just a week ago. Since then, this Chinese AI lab has been the talk of the town, getting much-deserved praise for successfully competing with large, high-profile US labs on a “shoestring budget.”

Not only that but DeepSeek is also seen as more open and transparent:

DeepSeek got so much attention that it skyrocketed to the #1 free app spot on the App Store:

Everybody loves DeepSeek.

So much so that we’re now doing that classic thing where we put a company on a pedestal and automatically give it a free pass.

Let’s not do that.

With DeepSeek feeding so much discourse, we’ll likely see even more hype around it. Some well-earned, some less so.

As always, take it all with a grain of salt and test things yourself where possible.

I certainly can’t rule out that DeepSeek will eventually release competitive image and even video models. If anything, last week has shown it to be an astoundingly capable AI lab.

But let’s not rush to find a new AI darling we can blindly idolize.

🫵 Over to you…

What’s your take? Am I being unreasonably nitpicky? Have you tested Janus and found its multimodality especially helpful for certain use cases?

Leave a comment or drop me a line at whytryai@substack.com.

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Hot Takes are occasional timely posts that focus on fast-moving news and releases, in addition to my regular Thursday and Sunday columns.

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TL;DR

Chinese AI lab DeepSeek just released its newest reasoning model: DeepSeek-R1.

What is it?

DeepSeek-R1 is a free, fully open-sourced reasoning model that performs on par with OpenAI’s o1 across many benchmarks:

How do you use it?

If you know what you’re doing and want to use DeepSeek-R1 for building apps, fine-tuning, etc., you can access it via DeepSeek API or grab the model on Hugging Face.

If you’re a regular user like me, you can chat with DeepSeek-R1 for free at chat.deepseek.com.

Here’s my quick video walkthrough and a showcase of its capabilities:

Why should you care?

As I see it, the launch of DeepSeek-R1 is a big deal for the average user but also has implications for the industry at large.

User-level implications

With DeepSeek-R1, everyone now has access to a reasoning model that:

1. Performs on par with o1 (almost): DeepSeek-R1 has similar scores on many benchmarks, although observers like AI Explained suggest it has its blind spots.

2. Is free to use and cheap to build with: DeepSeek-R1 is free for regular chats, and its API pricing per 1M output tokens is almost 30 times cheaper1 than o1.

3. Provides better insight into its thinking process: DeepSeek-R1 offers a more detailed and transparent picture of its inner monologue than the distilled summary from o1 (see above video). This gives users a better chance to trace the reasoning behind the answer, identify where it goes off the rails, and perhaps steer the model better in subsequent requests.

4. Can render the resulting code: As I’ve shown, DeepSeek-R1 lets you test the apps it creates directly in the chat interface (a la Claude Artifacts). This is helpful for quick back-and-forth iterations.

5. Is fully open-sourced: Developers can fine-tune the model, distill it, and otherwise access the underlying code.

Broader implications

DeepSeek has shown that it’s possible to quickly develop and open-source an o1-level reasoning model2 while making it less expensive and more transparent than OpenAI’s proprietary ones.

We’re only three weeks into January and already have a capable open-source competitor to OpenAI’s o1. I’m starting to think this two-week-old prediction of mine might’ve been too conservative:

By the end of 2025, reasoning models from at least three other players will perform on par with or better than OpenAI’s o3.

🫵 Over to you…

What’s your take? Are we about to see an avalanche of reasoning models from other firms? Have I overlooked some important implications?

If you’ve had the chance to test DeepSeek-R1 and compare it to o1 for your tasks, I’d love to hear what you think!

Leave a comment or drop me a line at whytryai@substack.com.

Leave a comment