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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

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16,501 görüntüleme • 20 gün önce •via X (Twitter)

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28 Yorum

thewhitemage. profil fotoğrafı

thewhitemage.20 gün önce

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

Mia profil fotoğrafı

Mia20 gün önce

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

Promptyx profil fotoğrafı

Promptyx20 gün önce

They all mostly look same??

Mia profil fotoğrafı

Mia20 gün önce

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

irony.somi profil fotoğrafı

irony.somi20 gün önce

glm > deepseek > astra > fable > qwen

Aj profil fotoğrafı

Aj20 gün önce

Fable

Dan C. profil fotoğrafı

Dan C.20 gün önce

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

Tom Maiaroto profil fotoğrafı

Tom Maiaroto20 gün önce

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.

Carlos Ajoy profil fotoğrafı

Carlos Ajoy20 gün önce

I think Astra looks More attractive

Geazi Anc profil fotoğrafı

Geazi Anc20 gün önce

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

Philip McBride profil fotoğrafı

Philip McBride20 gün önce

Impressed with them all, but Fable 5.1 wins.

sgiath profil fotoğrafı

sgiath20 gün önce

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.

David Grant profil fotoğrafı

David Grant20 gün önce

Astra > GLM 5.3 > others Qwen last

Mia profil fotoğrafı

Mia20 gün önce

Yes

Tim Messerschmidt profil fotoğrafı

Tim Messerschmidt20 gün önce

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

Eco profil fotoğrafı

Eco20 gün önce

I think Astra

Guillem profil fotoğrafı

Guillem20 gün önce

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.

Olivia Parker profil fotoğrafı

Olivia Parker20 gün önce

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

Sergio profil fotoğrafı

Sergio20 gün önce

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

Divine 〽️achine profil fotoğrafı

Divine 〽️achine20 gün önce

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

ilyas profil fotoğrafı

ilyas20 gün önce

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

Knowix profil fotoğrafı

Knowix20 gün önce

I feel GLM 5.3 Flash did a better job here

Arman profil fotoğrafı

Arman20 gün önce

Bijan-coded benchmark

curdmudgeon profil fotoğrafı

curdmudgeon20 gün önce

they all seem fine

Tango Uniform profil fotoğrafı

Tango Uniform20 gün önce

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.”

Daniel B. - AI & Tech profil fotoğrafı

Daniel B. - AI & Tech20 gün önce

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

Tyler Folkman profil fotoğrafı

Tyler Folkman20 gün önce

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.

Carmelo schepis profil fotoğrafı

Carmelo schepis19 gün önce

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

Benzer Videolar

Qwen 3.8 vs GLM 5.3 vs Gemini 3.8 Flash vs DeepSeek v4.1 Flash Prompt: Build an Angry Birds clone Qwen 3.8 took $0.49 in 3 min GLM 5.3 took $0.46 in 2 min Gemini 3.8 Flash took $0.29 in 2 min 23 sec DeepSeek v4.1 Flash took $0.11 in 1 min 28 sec Which one wins for you?

Qwen 3.8 vs GLM 5.3 vs Gemini 3.8 Flash vs DeepSeek v4.1 Flash Prompt: Build an Angry Birds clone Qwen 3.8 took $0.49 in 3 min GLM 5.3 took $0.46 in 2 min Gemini 3.8 Flash took $0.29 in 2 min 23 sec DeepSeek v4.1 Flash took $0.11 in 1 min 28 sec Which one wins for you?

Bhavy☄️

27,799 görüntüleme • 27 gün önce

Fable 5.1 xHigh vs GLM 5.3 Flash Max With Zai’s current promo, GLM 5.3 Flash is around 58x cheaper than Fable 5.1… and just look at the quality It finished the same task in about half the time, while still delivering a genuinely impressive result 58x cheaper. 2x faster. And the quality is this close That’s honestly crazy, Huge credit to Zhipu for bringing us models this efficient

Fable 5.1 xHigh vs GLM 5.3 Flash Max With Zai’s current promo, GLM 5.3 Flash is around 58x cheaper than Fable 5.1… and just look at the quality It finished the same task in about half the time, while still delivering a genuinely impressive result 58x cheaper. 2x faster. And the quality is this close That’s honestly crazy, Huge credit to Zhipu for bringing us models this efficient

Iam_

110,563 görüntüleme • 1 ay önce

DeepSeek v4.1 Flash vs GLM 5.3 Flash I asked both models to recreate the Psychonauts 2 and Metroid Prime game menus as closely as possible to the originals. Attached are full-sized videos from both models for both games. All were done in a single shot, both at Max thinking, same harness. Tokens used: DeepSeek v4.1 Flash - Psychonauts 2: 412k - Metroid Prime: 466k GLM 5.3 Flash - Psychonauts 2: 134k - Metroid Prime: 111k That's between 3-4x MORE used by DS4.1F. Who do you think won? IMO GLM 5.3 Flash won by FAR on both.

DeepSeek v4.1 Flash vs GLM 5.3 Flash I asked both models to recreate the Psychonauts 2 and Metroid Prime game menus as closely as possible to the originals. Attached are full-sized videos from both models for both games. All were done in a single shot, both at Max thinking, same harness. Tokens used: DeepSeek v4.1 Flash - Psychonauts 2: 412k - Metroid Prime: 466k GLM 5.3 Flash - Psychonauts 2: 134k - Metroid Prime: 111k That's between 3-4x MORE used by DS4.1F. Who do you think won? IMO GLM 5.3 Flash won by FAR on both.

Mia

23,835 görüntüleme • 19 gün önce

Meet Wally. Our inference stack for open frontier models, built to be the fastest place to run them. Performance snapshot: GLM-5.3 Flash: 380 tok/s. GLM-5.3 Max: 790 tok/s Qwen3.8-27B: 485 tok/s DeepSeek-V4.1 Flash: 615 tok/s 1/7

Meet Wally. Our inference stack for open frontier models, built to be the fastest place to run them. Performance snapshot: GLM-5.3 Flash: 380 tok/s. GLM-5.3 Max: 790 tok/s Qwen3.8-27B: 485 tok/s DeepSeek-V4.1 Flash: 615 tok/s 1/7

RunAnywhere

63,630 görüntüleme • 14 gün önce

I ran this locally with GLM-5.3 Flash EXL3 on my DGX Sparks, and I think it beats both Fable 5.1 and GPT-6 Astra. The crazy part: it was faster to make create this on my sparks than it was with either Fable 5.1 or Astra. Live link here:

I ran this locally with GLM-5.3 Flash EXL3 on my DGX Sparks, and I think it beats both Fable 5.1 and GPT-6 Astra. The crazy part: it was faster to make create this on my sparks than it was with either Fable 5.1 or Astra. Live link here:

Mia

48,031 görüntüleme • 27 gün önce

Fable 5.1 vs Gemini 3.8 Flash - Anime animation test We know Fable 5.1 is the most capable model for reasoning but now Google launched Gemini 3.8 flash Did it do a good job?

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maxfusion.ai

48,406 görüntüleme • 1 ay önce

Ran FlappyBench on DeepSeek V4.1-Flash, GLM 5.3, and Kimi K3 with the same /design prompt. 🔹 DSV4.1-Flash: 9/10 · $0.0089 one-shot with the lowest cost 🔹 GLM-5.3: 9.5/10 · $0.0184 · nailed the UI but cost 2x more 🔹 Kimi K3: 8/10 · $0.0740 · hard-coded gameplay and 8x more

Ran FlappyBench on DeepSeek V4.1-Flash, GLM 5.3, and Kimi K3 with the same /design prompt. 🔹 DSV4.1-Flash: 9/10 · $0.0089 one-shot with the lowest cost 🔹 GLM-5.3: 9.5/10 · $0.0184 · nailed the UI but cost 2x more 🔹 Kimi K3: 8/10 · $0.0740 · hard-coded gameplay and 8x more

Command Code

20,527 görüntüleme • 24 gün önce

deepseek v4.1 flash is now 100% free 😳 a new coding agent is using ads to keep the whole thing free. > DeepSeek V4.1 Flash > GPT 6 Luna > GLM 5.3 Flash > More model How to get start → go to [ → sign up with GitHub → generate your API key these models be used for six hours per day.

deepseek v4.1 flash is now 100% free 😳 a new coding agent is using ads to keep the whole thing free. > DeepSeek V4.1 Flash > GPT 6 Luna > GLM 5.3 Flash > More model How to get start → go to [ → sign up with GitHub → generate your API key these models be used for six hours per day.

BARIK

24,236 görüntüleme • 20 gün önce

GLM 5.3 Flash vs Hybrid (GLM 5.3 Flash + Jev) on a turn-based strategy game (Halite 2) The hybrid approach is 13x faster, 56% of the pure GLM 5.3 Flash API cost, and gains a slight edge in performance. In the hybrid approach: -GLM 5.3 Flash handles strategic judgment of which planets matter, who expands, who fights, when to re-plan. -Jev handles the ship-by-ship execution. GLM 5.3 Flash alone defeats Jev alone 82% of the time. This is obviously just a single example and some of the first things I've tried to make use of Jev as an augmentation to an LLM. Could potentially make even more gains in cost, speed, and performance, but using what I've figured out here, I am compelled to try some ideas with robotics next.

GLM 5.3 Flash vs Hybrid (GLM 5.3 Flash + Jev) on a turn-based strategy game (Halite 2) The hybrid approach is 13x faster, 56% of the pure GLM 5.3 Flash API cost, and gains a slight edge in performance. In the hybrid approach: -GLM 5.3 Flash handles strategic judgment of which planets matter, who expands, who fights, when to re-plan. -Jev handles the ship-by-ship execution. GLM 5.3 Flash alone defeats Jev alone 82% of the time. This is obviously just a single example and some of the first things I've tried to make use of Jev as an augmentation to an LLM. Could potentially make even more gains in cost, speed, and performance, but using what I've figured out here, I am compelled to try some ideas with robotics next.

Harrison Kinsley

53,159 görüntüleme • 15 gün önce

You can now choose which model builds your app on Caffeine. The latest models are all in there: Opus 5.5 Fable 5.1 GPT-6 Astra GPT-6 Sol Kimi K3 Sonnet 5 DeepSeek V4.1 Flash Plan a whole new app with a frontier model, then switch to a faster, cheaper one for quick questions and small changes. It works on every plan, starting today.

You can now choose which model builds your app on Caffeine. The latest models are all in there: Opus 5.5 Fable 5.1 GPT-6 Astra GPT-6 Sol Kimi K3 Sonnet 5 DeepSeek V4.1 Flash Plan a whole new app with a frontier model, then switch to a faster, cheaper one for quick questions and small changes. It works on every plan, starting today.

caffeine

15,833 görüntüleme • 11 gün önce

We asked Fable 5.1, GPT-6 Astra, and Gemini 3.8 Flash to write a video prompt from the same brief. All rendered with Seedance 2.5 on each::labs. Which one is your favorite? ⤵

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each::labs

120,823 görüntüleme • 1 ay önce

GLM 5.3 Flash vs Kimi K3 tested both models on same prompt at highest reasoning available but results came out really different > 5.3 flash took 20 minutes to complete this and costed $0.7 via AIhubMix API > kimi k3 took 30 minutes to complete this and costed $4.32 via Kimi Plan which one do you think did better here?

GLM 5.3 Flash vs Kimi K3 tested both models on same prompt at highest reasoning available but results came out really different > 5.3 flash took 20 minutes to complete this and costed $0.7 via AIhubMix API > kimi k3 took 30 minutes to complete this and costed $4.32 via Kimi Plan which one do you think did better here?

J A Z I I

23,105 görüntüleme • 1 ay önce

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 görüntüleme • 1 ay önce

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?

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?

J A Z I I

39,045 görüntüleme • 25 gün önce

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?

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?

Bhavy☄️

33,971 görüntüleme • 1 ay önce

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

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

Tim Jayas

82,975 görüntüleme • 1 ay önce

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 görüntüleme • 1 ay önce

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 görüntüleme • 25 gün önce

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 görüntüleme • 25 gün önce

🚨 You can use GPT-6 Astra as the orchestrator and DeepSeek V4.1 Flash as the agents to cut costs by more than 88% while maintaining the same level of quality. And now it’s even better, since DeepSeek V4.1 Flash is one of the best models for cybersecurity at an incredibly low price

🚨 You can use GPT-6 Astra as the orchestrator and DeepSeek V4.1 Flash as the agents to cut costs by more than 88% while maintaining the same level of quality. And now it’s even better, since DeepSeek V4.1 Flash is one of the best models for cybersecurity at an incredibly low price

Iam_

32,627 görüntüleme • 25 gün önce