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Five frontier models. One simple prompt. No retries. No blender. Just a simple frontend web design. Fable 5.1 vs. GPT-6 Astra vs. DeepSeek v4.1 Flash vs. GLM 5.3 Flash vs. Qwen3.8 Flash Next Which model built the best looking luxury watch landing page? IMO Fable 5.1 and GLM 5.3... show more

Mia

37,061 subscribers

16,501 Aufrufe • vor 21 Tagen •via X (Twitter)

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28 Kommentare

Profilbild von thewhitemage.

thewhitemage.vor 21 Tagen

I really like GLM 5.3 Flash's, for its size, it's pretty damn close to frontier.

Profilbild von Mia

Miavor 21 Tagen

I think it's only second to fable. Trying complicated stuff now to test their limits.

Profilbild von Promptyx

Promptyxvor 21 Tagen

They all mostly look same??

Profilbild von Mia

Miavor 21 Tagen

The watch is different on each, and some are more immersive

Profilbild von irony.somi

irony.somivor 21 Tagen

glm > deepseek > astra > fable > qwen

Profilbild von Aj

Ajvor 21 Tagen

Fable

Profilbild von Dan C.

Dan C.vor 21 Tagen

I think I prefer Deepseek. Incredible for the size of model.

Profilbild von Tom Maiaroto

Tom Maiarotovor 21 Tagen

Holy crap I don't like Astra or Fable 5.1. The others are somehow better. Didn't expect that! The "Reserve" button text is too small in the Astra version, plus some weird rule (longer than an em dash) above "Meridian 38" title. The tracking for the text "calibre" in the Fable 5.1 is too weird and the selected color way out on the right which has no label that it's the current selection. Yes, Fable 5.1 and Astra suck here.

Profilbild von Carlos Ajoy

Carlos Ajoyvor 21 Tagen

I think Astra looks More attractive

Profilbild von Geazi Anc

Geazi Ancvor 21 Tagen

how about Tencent Hy4? I'm not seeing anyone talking about this model, and it looks be great as well.

Profilbild von Philip McBride

Philip McBridevor 21 Tagen

Impressed with them all, but Fable 5.1 wins.

Profilbild von sgiath

sgiathvor 21 Tagen

They are too similar to tell. That tells me that it is too simple task so it does not properly differentiate the models. Try something more complex or with more wiggle room so the differences are more apparent.

Profilbild von David Grant

David Grantvor 21 Tagen

Astra > GLM 5.3 > others Qwen last

Profilbild von Mia

Miavor 21 Tagen

Yes

Profilbild von Tim Messerschmidt

Tim Messerschmidtvor 21 Tagen

The slight moving up and down of the watches was a little unsettling for me 😁 Love the back-to-back comparison, though!

Profilbild von Eco

Ecovor 21 Tagen

I think Astra

Profilbild von Guillem

Guillemvor 20 Tagen

I tried to look at the watch only, ignoring who had made it, and just remember my first impression. For me: Astra is the best, DeepSeek and Fable are tied at the "good enough" level, the others are too cartoony for me and personally I wouldn't use them.

Profilbild von Olivia Parker

Olivia Parkervor 21 Tagen

One prompt is a draft contest. Live listing is the score.

Profilbild von Sergio

Sergiovor 21 Tagen

How is it that all five models ended up using the same price for the watch?

Profilbild von Divine 〽️achine

Divine 〽️achinevor 21 Tagen

Deepseek looks great, i assume it was much cheaper than sol/fable for comparable output

Profilbild von ilyas

ilyasvor 21 Tagen

Hello, how do you write these kinds of commands, or do you use example prompts? Could you please provide information?

Profilbild von Knowix

Knowixvor 21 Tagen

I feel GLM 5.3 Flash did a better job here

Profilbild von Arman

Armanvor 21 Tagen

Bijan-coded benchmark

Profilbild von curdmudgeon

curdmudgeonvor 21 Tagen

they all seem fine

Profilbild von Tango Uniform

Tango Uniformvor 21 Tagen

all went for 4850? “Build a single-file HTML landing page for Halden — Meridian 38, a fictional minimalist luxury watch brand from Oslo. Dark, quiet, premium. The watch itself must be drawn with CSS/SVG and show the real local time. Include specs, price and a reserve button.”

Profilbild von Daniel B. - AI & Tech

Daniel B. - AI & Techvor 21 Tagen

Fable gets it for me, but DeepSeek is a close second, very close!

Profilbild von Tyler Folkman

Tyler Folkmanvor 21 Tagen

Agreed Qwen is the worst but for how well it runs on 2 sparks, locally it's a very powerful model. I liked GLM the most.

Profilbild von Carmelo schepis

Carmelo schepisvor 20 Tagen

fable’s spacing is elite but the typography feels a bit mid. still a massive win for the frontier tier.

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63,630 Aufrufe • vor 15 Tagen

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23,105 Aufrufe • vor 1 Monat

glm 5.3 vs qwen 3.8 vs gemini 3.7 vs deepseek v4 flash four models designed and built three structures each on a physics-backed site, with no dimensions anywhere in the brief the setup: our own agent loop on OpenRouter, a construction site as the tool set – footings, walls, arches, roofs, scaffold, a lamp. the site enforces physics and nothing else: unsupported brick falls, a roof needs walls under it, a worker reaches 3.2 m above whatever he stands on, an arch needs centring until the keystone is set, concrete cures before it carries. no budget ceiling – material cost is tallied and reported, never blocked. tasks: 1. house – a plot and a palette, no plan. shape, height and material are the model's call 2. lighthouse – a headland cut by a gully, with a rock stack standing 30 m offshore. the lamp must burn, it must be the highest thing built, and the keeper must be able to walk to it 3. bridge – a river with one islet and banks at different heights. cross it however you want models: Z.ai glm 5.3 flash, Qwen qwen 3.8 flash, Google DeepMind gemini 3.7 flash, DeepSeek v4 flash vision all twelve objects were finished and signed off by the models themselves. tallest lighthouse is qwen's at 38.4 m, planted on the offshore stack with a bridge run out to it – the only model that read the site that way. deepseek signed off its bridge on an empty riverbed: 0 bricks, 107 minutes, $1.16m of material tallied - total cost, three builds #1 glm 5.3 flash – $0.201 #2 gemini 3.7 flash – $0.871 #3 qwen 3.8 flash – $1.058 #4 deepseek v4 flash – $1.567 - wall clock, three builds #1 gemini 3.7 flash – 91m #2 glm 5.3 flash – 228m #3 deepseek v4 flash – 502m #4 qwen 3.8 flash – 912m - total tokens #1 gemini 3.7 flash – 3,567,052 #2 glm 5.3 flash – 4,732,748 #3 qwen 3.8 flash – 13,469,333 #4 deepseek v4 flash – 18,230,076 - defects logged by the site #1 deepseek v4 flash – 59 #2 gemini 3.7 flash – 132 #3 glm 5.3 flash – 221 #4 qwen 3.8 flash – 350 - material tallied across three builds #1 gemini 3.7 flash – $359,884 #2 glm 5.3 flash – $583,358 #3 deepseek v4 flash – $1,327,484 #4 qwen 3.8 flash – $2,188,625 observations: • glm is the cheap one and nothing here is close – $0.201 for three buildings, $0.042 per million tokens, 6x under gemini's rate • what glm spends it on is bulk, not care: 166,228 bricks in one house and 156 defect weight, the worst single object in the set • gemini is the efficiency line – 91 minutes and 3.57m tokens for all three and an eighth of qwen's clock • gemini also builds the smallest of everything. its lighthouse is 22.5 m against qwen's 38.4, its house 6.9 m against 19.3 • qwen is the maximalist: 1.18m bricks, $2.19m of material, tallest on all three tasks, and 912 minutes – 15 hours – to get there conclusion: twelve finished objects for $3.80 all in, and a 7.8x price spread between the cheapest model and the priciest! follow thehype. for 24/7 ai news, analysis and breakdowns

glm 5.3 vs qwen 3.8 vs gemini 3.7 vs deepseek v4 flash four models designed and built three structures each on a physics-backed site, with no dimensions anywhere in the brief the setup: our own agent loop on OpenRouter, a construction site as the tool set – footings, walls, arches, roofs, scaffold, a lamp. the site enforces physics and nothing else: unsupported brick falls, a roof needs walls under it, a worker reaches 3.2 m above whatever he stands on, an arch needs centring until the keystone is set, concrete cures before it carries. no budget ceiling – material cost is tallied and reported, never blocked. tasks: 1. house – a plot and a palette, no plan. shape, height and material are the model's call 2. lighthouse – a headland cut by a gully, with a rock stack standing 30 m offshore. the lamp must burn, it must be the highest thing built, and the keeper must be able to walk to it 3. bridge – a river with one islet and banks at different heights. cross it however you want models: Z.ai glm 5.3 flash, Qwen qwen 3.8 flash, Google DeepMind gemini 3.7 flash, DeepSeek v4 flash vision all twelve objects were finished and signed off by the models themselves. tallest lighthouse is qwen's at 38.4 m, planted on the offshore stack with a bridge run out to it – the only model that read the site that way. deepseek signed off its bridge on an empty riverbed: 0 bricks, 107 minutes, $1.16m of material tallied - total cost, three builds #1 glm 5.3 flash – $0.201 #2 gemini 3.7 flash – $0.871 #3 qwen 3.8 flash – $1.058 #4 deepseek v4 flash – $1.567 - wall clock, three builds #1 gemini 3.7 flash – 91m #2 glm 5.3 flash – 228m #3 deepseek v4 flash – 502m #4 qwen 3.8 flash – 912m - total tokens #1 gemini 3.7 flash – 3,567,052 #2 glm 5.3 flash – 4,732,748 #3 qwen 3.8 flash – 13,469,333 #4 deepseek v4 flash – 18,230,076 - defects logged by the site #1 deepseek v4 flash – 59 #2 gemini 3.7 flash – 132 #3 glm 5.3 flash – 221 #4 qwen 3.8 flash – 350 - material tallied across three builds #1 gemini 3.7 flash – $359,884 #2 glm 5.3 flash – $583,358 #3 deepseek v4 flash – $1,327,484 #4 qwen 3.8 flash – $2,188,625 observations: • glm is the cheap one and nothing here is close – $0.201 for three buildings, $0.042 per million tokens, 6x under gemini's rate • what glm spends it on is bulk, not care: 166,228 bricks in one house and 156 defect weight, the worst single object in the set • gemini is the efficiency line – 91 minutes and 3.57m tokens for all three and an eighth of qwen's clock • gemini also builds the smallest of everything. its lighthouse is 22.5 m against qwen's 38.4, its house 6.9 m against 19.3 • qwen is the maximalist: 1.18m bricks, $2.19m of material, tallest on all three tasks, and 912 minutes – 15 hours – to get there conclusion: twelve finished objects for $3.80 all in, and a 7.8x price spread between the cheapest model and the priciest! follow thehype. for 24/7 ai news, analysis and breakdowns

thehype.

26,360 Aufrufe • vor 1 Monat

Opus 5 vs DeepSeek v4.1 Flash tested both models with same frontend prompt at highest reasoning available but results came out really different > opus took 80 minutes to complete and costed $20 > v4.1 flash took 60 minutes and costed $0.2 which one did better here?

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J A Z I I

39,045 Aufrufe • vor 26 Tagen

I tested Fable 5.1 vs Kimi K3 vs GPT 5.6 Sol vs Gemini 3.8 Flash I capped Fable, Kimi & Sol at $2 and asked them to generate this beach scene. Fable 5.1: Looks good at first glance, but made many mistakes, like people sitting on the volleyball court. Kimi K3: It did well. There were no major bugs like in Fable 5.1, and everything looks good. Sol: Bad at 3D scenes as usual, but Astra will definitely fix that. Gemini 3.8 Flash: I ran it in a loop for 35+ mins and it cost $0.95. It didn’t understand what it was fixing. The output here is the enhanced version. IMO, not even Terra level. Gemini's benchmarks say Opus 5-level, but maybe not for spatial understanding. Gemini has other use cases. Which one did the best job?

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Bhavy☄️

33,971 Aufrufe • vor 1 Monat

GLM-5.3 Flash in already nerfed? I compared Ox Alpha in it's prime vs today just before it got revealed as GLM-5.3 Flash using the same prompt to build 3d raptor engine using three.js OxAlpha on 24th August gave far more accurate output compared to today which looks way worse prompt i used: Create a 3d SpaceX raptor engine using three.js, use white theme background so the main object is clearly visible

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Tim Jayas

82,975 Aufrufe • vor 1 Monat

glm 5.3 flash is 7.5x cheaper, but 3.4x slower than gemini 3.7 flash Z.ai glm 5.3 flash – shipped aug 26, $0.07/$0.25 per 1m Google DeepMind gemini 3.7 flash – shipped aug 13, $0.38/$1.88 per 1m we put the two models on one job: write one html file that draws an animated 3d scene in the browser. no images, no downloads, and it has to look the same on every load. the setup: three scenes – a glass aquarium in a lit room, the solar system, a night city under a thunderstorm. identical brief word for word, reasoning effort high, 64k output cap. the numbers below are not the whole run. they cover the three scenes we kept – the best one per task from each model, the ones in the video. - total generation time for the three scenes #1 gemini 3.7 flash – 10m 36s #2 glm 5.3 flash – 36m 30s - tokens spent on those three scenes #1 glm 5.3 flash – 110k #2 gemini 3.7 flash – 111k - cost of those three scenes #1 glm 5.3 flash – $0.027 #2 gemini 3.7 flash – $0.202 observations: • glm's first 10 attempts: 7 blank pages. it kept inventing short random helpers and forgetting to define one of them. the fix was one line in the brief: use exactly one random helper, named rand(), and don't invent shorthands next to it. next 12 attempts: 11 alive, 0 crashes. • glm spends 66% of its output on reasoning, gemini 57%. that is the whole speed gap. • gemini's storm came back as a black rectangle in 4 of 6 runs. glm's best storm has a branching bolt, lit rain and wet asphalt – for $0.01. conclusion: same three scenes, same token spend – glm 5.3 flash billed $0.027 and took 36m 30s, gemini 3.7 flash billed $0.202 and took 10m 36s. glm wins gemini on price and made the best storm of the whole run follow thehype. for 24/7 ai news, analysis and breakdowns

glm 5.3 flash is 7.5x cheaper, but 3.4x slower than gemini 3.7 flash Z.ai glm 5.3 flash – shipped aug 26, $0.07/$0.25 per 1m Google DeepMind gemini 3.7 flash – shipped aug 13, $0.38/$1.88 per 1m we put the two models on one job: write one html file that draws an animated 3d scene in the browser. no images, no downloads, and it has to look the same on every load. the setup: three scenes – a glass aquarium in a lit room, the solar system, a night city under a thunderstorm. identical brief word for word, reasoning effort high, 64k output cap. the numbers below are not the whole run. they cover the three scenes we kept – the best one per task from each model, the ones in the video. - total generation time for the three scenes #1 gemini 3.7 flash – 10m 36s #2 glm 5.3 flash – 36m 30s - tokens spent on those three scenes #1 glm 5.3 flash – 110k #2 gemini 3.7 flash – 111k - cost of those three scenes #1 glm 5.3 flash – $0.027 #2 gemini 3.7 flash – $0.202 observations: • glm's first 10 attempts: 7 blank pages. it kept inventing short random helpers and forgetting to define one of them. the fix was one line in the brief: use exactly one random helper, named rand(), and don't invent shorthands next to it. next 12 attempts: 11 alive, 0 crashes. • glm spends 66% of its output on reasoning, gemini 57%. that is the whole speed gap. • gemini's storm came back as a black rectangle in 4 of 6 runs. glm's best storm has a branching bolt, lit rain and wet asphalt – for $0.01. conclusion: same three scenes, same token spend – glm 5.3 flash billed $0.027 and took 36m 30s, gemini 3.7 flash billed $0.202 and took 10m 36s. glm wins gemini on price and made the best storm of the whole run follow thehype. for 24/7 ai news, analysis and breakdowns

thehype.

16,041 Aufrufe • vor 1 Monat

GPT-6 Astra vs DeepSeek V4.1 Flash in Three.js. Same kind of task, two completely different approaches. The visual difference alone is pretty interesting. Source footage and original material: OpenDesign

GPT-6 Astra vs DeepSeek V4.1 Flash in Three.js. Same kind of task, two completely different approaches. The visual difference alone is pretty interesting. Source footage and original material: OpenDesign

Token Gremlin

44,946 Aufrufe • vor 26 Tagen

DeepSeek V4.1 Flash (High) vs Kimi K3 (Low) Same farming scene prompt. This is the result. > DeepSeek High took 1 min 15s and cost $0.45. > Kimi K3 took 2m 10s and cost $1.20. Almost as good as Kimi, but honestly, one of the best Flash models ever released.

DeepSeek V4.1 Flash (High) vs Kimi K3 (Low) Same farming scene prompt. This is the result. > DeepSeek High took 1 min 15s and cost $0.45. > Kimi K3 took 2m 10s and cost $1.20. Almost as good as Kimi, but honestly, one of the best Flash models ever released.

Bhavy☄️

57,114 Aufrufe • vor 26 Tagen