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Gemini Flash 3.8 beats Opus 5 at at 15× lower price the test: four self-contained three.js physics scenes — a balloon popped by a needle, a ball dropping into water, a bomb with a mushroom cloud, a live atom model. 5 seconds each, real gravity, real impact. cost: Gemini... show more

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106,158 次观看 • 1 天前 •via X (Twitter)

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Gemini 3.8 Flash vs Opus 5 same prompt to each model and i wasn't expecting gemini 3.8 flash to be this fast! build time: > Opus 5: 24 minutes > 3.8 Flash: 37 seconds 💀 if gemini is given same time as opus 5 it will mog the hell out of opus in terms of quality too which one would you choose here?

Gemini 3.8 Flash vs Opus 5 same prompt to each model and i wasn't expecting gemini 3.8 flash to be this fast! build time: > Opus 5: 24 minutes > 3.8 Flash: 37 seconds 💀 if gemini is given same time as opus 5 it will mog the hell out of opus in terms of quality too which one would you choose here?

Tim Jayas

74,316 次观看 • 1 天前

Gemini 3.8 Flash is hardwired for complex reasoning. ⚙️ To test its skills, we built an interactive 3D visualizer with 3.8 Flash and Three.js in Google AI Studio. Watch the model generate realistic, physically-proportioned teardowns for hardware devices. It automatically decomposes devices into layers that users can explode and inspect with a deconstruction slider.

Gemini 3.8 Flash is hardwired for complex reasoning. ⚙️ To test its skills, we built an interactive 3D visualizer with 3.8 Flash and Three.js in Google AI Studio. Watch the model generate realistic, physically-proportioned teardowns for hardware devices. It automatically decomposes devices into layers that users can explode and inspect with a deconstruction slider.

Google AI Developers

34,667 次观看 • 1 天前

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.

23,951 次观看 • 6 天前

Free is the part people are underpricing. Google just made Gemini 3.6 Flash the new default. Not a toy model hiding in a free tier. A near-frontier coding model normal users can try in Gemini and AI Studio without touching enterprise pricing. This is landing while people compare paid frontier APIs: > GPT-5.6 Sol at $5/$30 > Claude Sonnet 5 at $3/$15 > Grok 4.5 at $2/$6 > Gemini 3.5 Flash at $1.50/$9 Benchmarks matter. But a model people can actually use for free changes behavior faster than a model they only admire from pricing tables.

Free is the part people are underpricing. Google just made Gemini 3.6 Flash the new default. Not a toy model hiding in a free tier. A near-frontier coding model normal users can try in Gemini and AI Studio without touching enterprise pricing. This is landing while people compare paid frontier APIs: > GPT-5.6 Sol at $5/$30 > Claude Sonnet 5 at $3/$15 > Grok 4.5 at $2/$6 > Gemini 3.5 Flash at $1.50/$9 Benchmarks matter. But a model people can actually use for free changes behavior faster than a model they only admire from pricing tables.

ＺＥＦＩ

13,074 次观看 • 1 个月前

Are AI agents shape rotators? In this new benchmark, we let the models play campaign puzzles in Opus Magnum, a puzzle game by Zachtronics. Ironically, Claude Opus 4.8 performed poorly, being beaten by GPT-5.5, Gemini 3.5 Flash, and GLM 5.2. Claude Fable 5 crushed them all.

Are AI agents shape rotators? In this new benchmark, we let the models play campaign puzzles in Opus Magnum, a puzzle game by Zachtronics. Ironically, Claude Opus 4.8 performed poorly, being beaten by GPT-5.5, Gemini 3.5 Flash, and GLM 5.2. Claude Fable 5 crushed them all.

Rob Haisfield

499,968 次观看 • 2 个月前

I gave a lil slice of lime and asked Gemini 3.7 Flash, Kimi K3, Claude Opus 5, and GPT 5.6 Sol to draw a human pose integrated with the object and here is how the human pose looks through their eyes 😆

I gave a lil slice of lime and asked Gemini 3.7 Flash, Kimi K3, Claude Opus 5, and GPT 5.6 Sol to draw a human pose integrated with the object and here is how the human pose looks through their eyes 😆

Ann Nguyen

576,506 次观看 • 17 天前

🚨 Gemini Flash 3.2/5 just coded a 1900-line voxel castle in under a minute video shows the full result + physics holding up at scale. could be 3.2 or 3.5 available on antigravity go test it yourself holy speeeddd

🚨 Gemini Flash 3.2/5 just coded a 1900-line voxel castle in under a minute video shows the full result + physics holding up at scale. could be 3.2 or 3.5 available on antigravity go test it yourself holy speeeddd

Chetaslua

28,544 次观看 • 3 个月前

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☄️

31,868 次观看 • 23 小时前

Muse Spark 1.3 is not that good, and Gemini Flash 3.8 is just pure benchmark-maxxing. Gemini even choked on the first run and needed a second attempt just to produce a working scene. By stripping colliders and enforcing rigorous backend-level logic in 3D space, this benchmark immediately separates true reasoning from trained test shortcuts.

Muse Spark 1.3 is not that good, and Gemini Flash 3.8 is just pure benchmark-maxxing. Gemini even choked on the first run and needed a second attempt just to produce a working scene. By stripping colliders and enforcing rigorous backend-level logic in 3D space, this benchmark immediately separates true reasoning from trained test shortcuts.

OmedTheVibeCoder

29,403 次观看 • 1 天前

Claude fable 5 is operating in a completely different dimension 🤯 Compared it with Claude Opus 4.8, Gemini 3.1 Pro, and GPT 5.5, the visual gap is insane. From 3D fluid dynamics to volumetric animation and complex physics, Fable 5 crushes the game. It’s not even close.

Claude fable 5 is operating in a completely different dimension 🤯 Compared it with Claude Opus 4.8, Gemini 3.1 Pro, and GPT 5.5, the visual gap is insane. From 3D fluid dynamics to volumetric animation and complex physics, Fable 5 crushes the game. It’s not even close.

GMI Cloud

320,134 次观看 • 2 个月前

New Fable 5 beats Opus 4.8 on real world physics simulations We gave both models the same three prompts and asked them to build self contained HTML5 sims with real physics and no libraries: 1. Chaotic double pendulum 2. Galton board 3. Water in a spinning drum (WCSPH) Generation cost Fable 5: $3.35 on 68.7k tokens, time 14m 47s Opus 4.8: $0.93 on 38.9k tokens, time 8m 10s Fable clearly did better on the water simulation, producing a much more solid and continuous body of water. Opus left larger gaps near the walls, scattered particles around the scene, and struggled to keep the fluid stable.

New Fable 5 beats Opus 4.8 on real world physics simulations We gave both models the same three prompts and asked them to build self contained HTML5 sims with real physics and no libraries: 1. Chaotic double pendulum 2. Galton board 3. Water in a spinning drum (WCSPH) Generation cost Fable 5: $3.35 on 68.7k tokens, time 14m 47s Opus 4.8: $0.93 on 38.9k tokens, time 8m 10s Fable clearly did better on the water simulation, producing a much more solid and continuous body of water. Opus left larger gaps near the walls, scattered particles around the scene, and struggled to keep the fluid stable.

atomic.chat

1,587,969 次观看 • 2 个月前

Opus 4.6 vs Gemini Pro 3.1 (7/9) I wanted to do a physics simulation test, overall both models did a good job in this department, however Opus added nice animations to things like "Fan", "Magnet" whilst gemini just rendered them out as a red circle. Opus also included way more effects in its toolbox, like rocket, connecting shapes with rope, springs etc. Should note that in all of these tests, Opus one shotted all of them, whilst gemini took an extra 3-5 prompts to get the project compile :) prompt: Build a polished physics sandbox toy using React + Vite with a real physics engine (Matter.js or Rapier), where users can spawn, grab, throw, and connect objects with springs, hinges, and ropes, plus interactive gadgets like fans and thrusters.

Opus 4.6 vs Gemini Pro 3.1 (7/9) I wanted to do a physics simulation test, overall both models did a good job in this department, however Opus added nice animations to things like "Fan", "Magnet" whilst gemini just rendered them out as a red circle. Opus also included way more effects in its toolbox, like rocket, connecting shapes with rope, springs etc. Should note that in all of these tests, Opus one shotted all of them, whilst gemini took an extra 3-5 prompts to get the project compile :) prompt: Build a polished physics sandbox toy using React + Vite with a real physics engine (Matter.js or Rapier), where users can spawn, grab, throw, and connect objects with springs, hinges, and ropes, plus interactive gadgets like fans and thrusters.

Dev Ed

28,179 次观看 • 6 个月前

Opus 5 crushed Fable 5 at 3D destruction physics for 2x cheaper! We gave four models the same task: build three self-contained HTML scenes with real physics Prompts: - A tornado that sucks in a whole field - A wrecking ball taking down an apartment block - An overloaded truck collapsing a truss bridge Outputs: - Opus 5: 55.9K tokens, $1.40 - Fable 5: 55.1K tokens, $2.82 - Kimi K3: 35.7K tokens, $0.55 - GPT 5.6: 20.1K tokens, $0.31 Opus got all three right unlike the other models. Houses fly up the funnel and out the top, the wall breaks where the ball hits and the rubble piles up, the bridge drops the truck into the river. Fable had almost nothing on the ground for the tornado to pick up, its building collapsed on its own before the ball even touched it and its bridge blew into sticks all at once. GPT is the cheapest here but its ball never reached the building at all and its bridge fell apart in a way nothing falls apart in real life. Kimi K3, the new Chinese frontier model, ended up in the same place as GPT

Opus 5 crushed Fable 5 at 3D destruction physics for 2x cheaper! We gave four models the same task: build three self-contained HTML scenes with real physics Prompts: - A tornado that sucks in a whole field - A wrecking ball taking down an apartment block - An overloaded truck collapsing a truss bridge Outputs: - Opus 5: 55.9K tokens, $1.40 - Fable 5: 55.1K tokens, $2.82 - Kimi K3: 35.7K tokens, $0.55 - GPT 5.6: 20.1K tokens, $0.31 Opus got all three right unlike the other models. Houses fly up the funnel and out the top, the wall breaks where the ball hits and the rubble piles up, the bridge drops the truck into the river. Fable had almost nothing on the ground for the tornado to pick up, its building collapsed on its own before the ball even touched it and its bridge blew into sticks all at once. GPT is the cheapest here but its ball never reached the building at all and its bridge fell apart in a way nothing falls apart in real life. Kimi K3, the new Chinese frontier model, ended up in the same place as GPT

atomic.chat

1,481,334 次观看 • 1 个月前

Gemini 3.5 Flash is a step forward for Google on speed and agentic capabilities but comes at a trade-off of being higher cost than prior models We have measured up to ~280 output tokens/sec, placing it on the speed/intelligence Pareto frontier and well ahead of Gemini 3 Flash. It also shows a major uplift on agentic tasks, reaching ~1650 ELO on GDPVal-AA. The trade-off: cost is up ~5x versus Gemini 3 Flash, driven by higher token prices (3x higher than Gemini 3 Flash) and higher token usage. In this video, Declan Jackson, Member of Technical Staff at Artificial Analysis, breaks it down.

Gemini 3.5 Flash is a step forward for Google on speed and agentic capabilities but comes at a trade-off of being higher cost than prior models We have measured up to ~280 output tokens/sec, placing it on the speed/intelligence Pareto frontier and well ahead of Gemini 3 Flash. It also shows a major uplift on agentic tasks, reaching ~1650 ELO on GDPVal-AA. The trade-off: cost is up ~5x versus Gemini 3 Flash, driven by higher token prices (3x higher than Gemini 3 Flash) and higher token usage. In this video, Declan Jackson, Member of Technical Staff at Artificial Analysis, breaks it down.

Artificial Analysis

16,055 次观看 • 3 个月前

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.

15,946 次观看 • 8 天前

gemini 3.7 flash vs deepseek v4 pro 0813 vs muse spark 1.2 – on voxel city dioramas three models each built three crossy road-style 3d scenes – a construction site, a nyc intersection, a river with a drawbridge – as single self-contained html files the setup: Nous Research's hermes agent cli on OpenRouter, three.js skills preloaded, identical prompts per scene tasks: 1. construction site – tower crane on a working lift loop, paver laying fresh road, roller compacting it behind 2. nyc crossing – four-way intersection with a traffic light state machine, queuing cars, pedestrians crossing on the walk signal 3. river drawbridge – double-leaf bascule that lifts for tall boats, cars queuing at the barriers, animated water every scene: Three.js r185, box geometry only, a locked 20-color palette, four camera presets, and a day/night mode with bloom. one file, no build step, no assets models: Google DeepMind gemini 3.7 flash, DeepSeek v4 pro 0813, AI at Meta muse spark 1.2 muse and gemini finished every scene in two to three minutes. deepseek took 15 to 41 minutes per scene - build time, all three scenes #1 gemini 3.7 flash – 6m 43s #2 muse spark 1.2 – 7m 20s #3 deepseek v4 pro – 91m 25s - total tokens #1 muse spark 1.2 – 440,279 #2 gemini 3.7 flash – 713,855 #3 deepseek v4 pro – 20,957,568 - total price #1 muse spark 1.2 – $0.53 #2 gemini 3.7 flash – $0.56 #3 deepseek v4 pro – $4.57 - agent calls across the three builds #1 muse spark 1.2 – 12 #2 gemini 3.7 flash – 18 #3 deepseek v4 pro – 143 observations: • muse won two of the three scenes on looks with the smallest files in the test – 887 to 1,042 lines against gemini's 1,934 to 2,377. cheapest, fastest to a good frame, and shortest turned out to be the same column • deepseek burned 20.96m tokens – 29x gemini, 48x muse – across 143 agent calls. prompt caching is the only reason that cost $4.57: the cache discount absorbed roughly $30 of resent context • gemini was the only model whose files needed zero fixes to render – and the only one whose night mode is cosmetic. the sky never darkens and one camera button does nothing. clean code for a scene it never looked at follow thehype. for 24/7 ai news, analysis and breakdowns

gemini 3.7 flash vs deepseek v4 pro 0813 vs muse spark 1.2 – on voxel city dioramas three models each built three crossy road-style 3d scenes – a construction site, a nyc intersection, a river with a drawbridge – as single self-contained html files the setup: Nous Research's hermes agent cli on OpenRouter, three.js skills preloaded, identical prompts per scene tasks: 1. construction site – tower crane on a working lift loop, paver laying fresh road, roller compacting it behind 2. nyc crossing – four-way intersection with a traffic light state machine, queuing cars, pedestrians crossing on the walk signal 3. river drawbridge – double-leaf bascule that lifts for tall boats, cars queuing at the barriers, animated water every scene: Three.js r185, box geometry only, a locked 20-color palette, four camera presets, and a day/night mode with bloom. one file, no build step, no assets models: Google DeepMind gemini 3.7 flash, DeepSeek v4 pro 0813, AI at Meta muse spark 1.2 muse and gemini finished every scene in two to three minutes. deepseek took 15 to 41 minutes per scene - build time, all three scenes #1 gemini 3.7 flash – 6m 43s #2 muse spark 1.2 – 7m 20s #3 deepseek v4 pro – 91m 25s - total tokens #1 muse spark 1.2 – 440,279 #2 gemini 3.7 flash – 713,855 #3 deepseek v4 pro – 20,957,568 - total price #1 muse spark 1.2 – $0.53 #2 gemini 3.7 flash – $0.56 #3 deepseek v4 pro – $4.57 - agent calls across the three builds #1 muse spark 1.2 – 12 #2 gemini 3.7 flash – 18 #3 deepseek v4 pro – 143 observations: • muse won two of the three scenes on looks with the smallest files in the test – 887 to 1,042 lines against gemini's 1,934 to 2,377. cheapest, fastest to a good frame, and shortest turned out to be the same column • deepseek burned 20.96m tokens – 29x gemini, 48x muse – across 143 agent calls. prompt caching is the only reason that cost $4.57: the cache discount absorbed roughly $30 of resent context • gemini was the only model whose files needed zero fixes to render – and the only one whose night mode is cosmetic. the sky never darkens and one camera button does nothing. clean code for a scene it never looked at follow thehype. for 24/7 ai news, analysis and breakdowns

thehype.

29,033 次观看 • 16 天前

Here's a comparison i never thought i would make Qwen-3.8-27b vs Gemini-3.7-flash Yes, you read it right! I don't know what to say, this is soo damn impressive. the difference is hugeee🤯 Take a look at this result 😍 (watch it in highest quality) Same prompt, both one shot on web-chat ui Qwen-3.8-27b produces consistent beautiful, visually stunning results. This is one of the best water surface simulations i have seen across every local models i have run and at the same time beating Gemini-3.7-flash result. And this is running on my 3090. And yes its Q5, not even f8.

Here's a comparison i never thought i would make Qwen-3.8-27b vs Gemini-3.7-flash Yes, you read it right! I don't know what to say, this is soo damn impressive. the difference is hugeee🤯 Take a look at this result 😍 (watch it in highest quality) Same prompt, both one shot on web-chat ui Qwen-3.8-27b produces consistent beautiful, visually stunning results. This is one of the best water surface simulations i have seen across every local models i have run and at the same time beating Gemini-3.7-flash result. And this is running on my 3090. And yes its Q5, not even f8.

AJ

295,840 次观看 • 20 天前

Free Opus 4.8, GPT 5.5, and Gemini 3.1 Pro. 50 credits, no card. The platform behind it is valued at $4 billion. Most people are burning API credits testing prompts across three different subscriptions. Opus for one thing, GPT for another, Gemini when the other two fail. > ClickUp just made that unnecessary. Sign up with Google or a corporate email. 50 credits land immediately. 1 message costs 1 credit - that's 50 real conversations with Opus 4.8 before you spend a dollar. Models available: Opus 4.8, GPT 5.5, Gemini 3.1 Pro, Gemini 3.5 Flash. Custom MCP support if you want your own tools wired in. This isn't a startup burning VC money on a growth hack. ClickUp was valued at $4 billion in 2021, raised $535M, and runs ~100,000 paying clients - McDonald's, Booking, Netflix, Papa Johns. The AI pivot landed in November 2025. Same infrastructure, same userbase, new models bolted on top. → Go to app(.)clickup(.)com → Sign up with Google → 50 credits land automatically → Pick your model and start Bookmark this before the free tier gets capped.

Free Opus 4.8, GPT 5.5, and Gemini 3.1 Pro. 50 credits, no card. The platform behind it is valued at $4 billion. Most people are burning API credits testing prompts across three different subscriptions. Opus for one thing, GPT for another, Gemini when the other two fail. > ClickUp just made that unnecessary. Sign up with Google or a corporate email. 50 credits land immediately. 1 message costs 1 credit - that's 50 real conversations with Opus 4.8 before you spend a dollar. Models available: Opus 4.8, GPT 5.5, Gemini 3.1 Pro, Gemini 3.5 Flash. Custom MCP support if you want your own tools wired in. This isn't a startup burning VC money on a growth hack. ClickUp was valued at $4 billion in 2021, raised $535M, and runs ~100,000 paying clients - McDonald's, Booking, Netflix, Papa Johns. The AI pivot landed in November 2025. Same infrastructure, same userbase, new models bolted on top. → Go to app(.)clickup(.)com → Sign up with Google → 50 credits land automatically → Pick your model and start Bookmark this before the free tier gets capped.

Atenov int.

10,981 次观看 • 2 个月前

We ran a test between GPT-5.6 Sol, Kimi K3, and Gemini 3.6 Flash. One prompt. Across all three models. Using our /design command. Results (gameplay, UX, and UI): 🔹 Kimi K3 performed well across all three. 🔹 GPT-5.6 Sol is good, but it gets annoying when the ball hits the wall. 🔹 Gemini 3.6 Flash is bad (took 5–6 attempts), but it has features the others don’t, like dropping coins for power-up balls and increasing the bar length. Ranking (DX, features, and cost): 🔹 Kimi K3: 10/10 · $0.10 🔹 GPT-5.6 Sol: 9/10 · $0.52 🔹 Gemini 3.6 Flash: 6/10 · $0.45

We ran a test between GPT-5.6 Sol, Kimi K3, and Gemini 3.6 Flash. One prompt. Across all three models. Using our /design command. Results (gameplay, UX, and UI): 🔹 Kimi K3 performed well across all three. 🔹 GPT-5.6 Sol is good, but it gets annoying when the ball hits the wall. 🔹 Gemini 3.6 Flash is bad (took 5–6 attempts), but it has features the others don’t, like dropping coins for power-up balls and increasing the bar length. Ranking (DX, features, and cost): 🔹 Kimi K3: 10/10 · $0.10 🔹 GPT-5.6 Sol: 9/10 · $0.52 🔹 Gemini 3.6 Flash: 6/10 · $0.45

Command Code

61,412 次观看 • 1 个月前

Qwen 3.8 Max beat Fable 5 at building 3D physics scenes for 7x cheaper! We gave two models the same task. Build three self-contained 3D scenes, each one HTML file with real physics that runs itself. Prompts: - A marble machine that lifts marbles up a wheel and drops them on a loop - A car factory assembly line - A sawmill cutting logs into planks Outputs: Qwen 3.8 Max: 46.6K tokens, $0.28 Fable 5: 38.7K tokens, $1.93 Qwen worked much harder on the details. Its textures and shadows look real. Fable 5 built a fast prototype that works and left it there. In the marble machine, Fable's marbles show up from nowhere at the top of the wheel. In Qwen's, you can see them go up. On the car line, Qwen's cars look much better and more real. Fable's sawmill is very strange. The saw looks wrong, and the log goes right through a beam before the saw cuts it. Atomic Chat will have day zero support to run Qwen 3.8 Max locally!

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Qwen 3.8 Max beat Fable 5 at building 3D physics scenes for 7x cheaper! We gave two models the same task. Build three self-contained 3D scenes, each one HTML file with real physics that runs itself. Prompts: - A marble machine that lifts marbles up a wheel and drops them on a loop - A car factory assembly line - A sawmill cutting logs into planks Outputs: Qwen 3.8 Max: 46.6K tokens, $0.28 Fable 5: 38.7K tokens, $1.93 Qwen worked much harder on the details. Its textures and shadows look real. Fable 5 built a fast prototype that works and left it there. In the marble machine, Fable's marbles show up from nowhere at the top of the wheel. In Qwen's, you can see them go up. On the car line, Qwen's cars look much better and more real. Fable's sawmill is very strange. The saw looks wrong, and the log goes right through a beam before the saw cuts it. Atomic Chat will have day zero support to run Qwen 3.8 Max locally!

atomic.chat

210,569 次观看 • 1 个月前