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Ran FlappyBench on Muse Spark 1.3, Gemini 3.8 Flash, and Grok-4.6 with the same /design prompt. 🔹 Muse Spark 1.3: 9/10 · $0.072. Nailed UI in 2 iterations with lowest cost 🔹 Grok-4.6: 9/10 · $0.095 · Matched Muse’s output, but cost 2x more 🔹 Gemini 3.8 Flash: 7/10...

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Command Code

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36,392 Aufrufe • vor 17 Tagen •via X (Twitter)

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Advanced Frontier LLM Benchmark - Üç Cisim & Gravity Choreography 🚀 - Modeller: Grok 4.6 / AI at Meta Muse Spark 1.3 / Claude Fable 5.1 / Google Gemini Gemini 3.8 Flash - Effort: Max & Very high - Agent: Cursor & Opencode Task: Görev, modele tek bir HTML dosyası içinde, dış kaynak kullanmadan, Canvas 2D ile düzlemsel N-cisim kütleçekimi simülasyonu yazdırmak; bu simülasyon açıldığı anda deterministik bir sinematik gösteriyle başlıyor (Kepler yörüngesi → hiyerarşik yıldız sistemi → figure-8 koreografisi → 12 kopyalı kaotik ayrışma → uzun pozlama), ayrıca ~40 saniyelik video kaydı için ayrı bir demo reel modu, gizli fizik self-check'i, korunum diagnostikleri, sürüklenebilir cisimler ve kompakt bir arayüz içeriyor. 🎉 Fiyat/Performans Kazanan: Grok 4.6🎉 🎉 Core Teknik Kazanan: Fable 5.1 🎉 Sebep: Fable, bu benchmark'ın iki tarafını en iyi birlikte çözüyor: solver ve cinematic renderer. Pairwise Newtonian çekirdek, softened potential, conservation diagnostics, Verlet/Yoshida yolu ve validator mimarisi güçlü; runtime self-check sonuçları da dört model içindeki en iyi seviyede. Daha önemlisi, model yalnızca ekrana “PASS” yazmıyor; hidden systems gerçek integrator yoluyla kademeli çalıştırılıyor ve ölçüm sonucundan verdict üretiliyor. Maliyet: - Grok 4.6: $1.10 - Muse Spark 1.3: $1.12 - Gemini 3.8 Flash: $1.3 - Fable 5.1: $6.2 Teknik skor: - Fable 5.1 - 96.4/100 - Grok 4.6 - 92.1/100 - Muse Spark 1.3 - 89.6/100 - Gemini 3.8 Flash - 81.0/100 Performans / $ - Grok 4.6 - 83.7/100 - Muse Spark 1.3 - 80.0/100 - Gemini 3.8 Flash - 62.3/100 - Fable 5.1 - 15.6/100 Teknik İnceleme Metrikleri: 1) Prompt uyumu: Fable'ın substep genişletmesi ve Muse'un bazı spesifikasyonları sadeleştirmesi nedeniyle kimseye 10 vermiyorum. - Fable 5.1: 9.6/10 - Grok 4.6: 9.3/10 - Gemini 3.8 Flash: 8.8/10 - Muse Spark 1.3: 8.2/10 2) Newtonian physics + conservation: Dört modelin de bu kategori şaşırtıcı derecede güçlü. Muse dahil pairwise equal-and-opposite acceleration ve aynı softened potential kernel'ini kullanıyor. - Fable 5.1: 9.9/10 - Grok 4.6: 9.8/10 - Gemini 3.8 Flash: 9.8/10 - Muse Spark 1.3: 9.6/10 3) Symplectic integrasyon + fixed timestep: Gemini'nin Yoshida formülü yanlış değil; puan kaybı scheduler/backlog yönetiminden geliyor. Numerical method doğru, execution model chaos altında sorunlu. - Fable 5.1: 9.6/10 - Grok 4.6: 9.5/10 - Muse Spark 1.3: 9.4/10 - Gemini 3.8 Flash: 8.0/10 4) Self-check / validator doğruluğu: Burada Fable açık lider. Gemini çok yakın; dtExact metodolojik küçük pürüz. Grok/Muse PASS ama validator resolution daha kaba. - Fable 5.1: 10.0/10 - Gemini 3.8 Flash: 9.4/10 - Grok 4.6: 8.6/10 - Muse Spark 1.3: 8.0/10 5) Görsel kalite: Gemini'nin celestial-body shading'i ve UI'sı çok iyi. Fable ise kompozisyon ve “görsel gürültüyü bastırma” açısından biraz daha dengeli. Muse özellikle büyük title choreography ile etkileyici. Grok en restrained olanı. - Fable 5.1: 9.6/10 - Gemini 3.8 Flash: 9.5/10 - Grok 4.6: 9.2/10 - Muse Spark 1.3: 9.1/10 6) Trail rendering: Gemini'nin single-system trail'i güzel; fakat chaos trail timestamp kaynağındaki hata bu kategori için ciddi. - Fable 5.1: 9.7/10 - Grok 4.6: 9.3/10 - Muse Spark 1.3: 9.0/10 - Gemini 3.8 Flash: 7.0/10 Alternatif winner'lar: - En iyi saf numerical correctness: Fable 5.1 - En iyi self-validator: Fable 5.1 - En iyi görsel kalite: Fable 5.1 ≈ Gemini 3.8 Flash - En iyi chaos throughput: Muse Spark 1.3 - En iyi genel mimari: Fable 5.1 - En iyi fiyat/performans: Grok 4.6 - En düşük maliyet: Grok 4.6 - Sadece physics/integrator kodu puanlansaydı: Fable ≈ Grok > Gemini ≈ Muse - Maliyet de kararın temel kriteri olsaydı: Grok > Muse > Gemini >>> Fable
1:28

Advanced Frontier LLM Benchmark - Üç Cisim & Gravity Choreography 🚀 - Modeller: Grok 4.6 / AI at Meta Muse Spark 1.3 / Claude Fable 5.1 / Google Gemini Gemini 3.8 Flash - Effort: Max & Very high - Agent: Cursor & Opencode Task: Görev, modele tek bir HTML dosyası içinde, dış kaynak kullanmadan, Canvas 2D ile düzlemsel N-cisim kütleçekimi simülasyonu yazdırmak; bu simülasyon açıldığı anda deterministik bir sinematik gösteriyle başlıyor (Kepler yörüngesi → hiyerarşik yıldız sistemi → figure-8 koreografisi → 12 kopyalı kaotik ayrışma → uzun pozlama), ayrıca ~40 saniyelik video kaydı için ayrı bir demo reel modu, gizli fizik self-check'i, korunum diagnostikleri, sürüklenebilir cisimler ve kompakt bir arayüz içeriyor. 🎉 Fiyat/Performans Kazanan: Grok 4.6🎉 🎉 Core Teknik Kazanan: Fable 5.1 🎉 Sebep: Fable, bu benchmark'ın iki tarafını en iyi birlikte çözüyor: solver ve cinematic renderer. Pairwise Newtonian çekirdek, softened potential, conservation diagnostics, Verlet/Yoshida yolu ve validator mimarisi güçlü; runtime self-check sonuçları da dört model içindeki en iyi seviyede. Daha önemlisi, model yalnızca ekrana “PASS” yazmıyor; hidden systems gerçek integrator yoluyla kademeli çalıştırılıyor ve ölçüm sonucundan verdict üretiliyor. Maliyet: - Grok 4.6: $1.10 - Muse Spark 1.3: $1.12 - Gemini 3.8 Flash: $1.3 - Fable 5.1: $6.2 Teknik skor: - Fable 5.1 - 96.4/100 - Grok 4.6 - 92.1/100 - Muse Spark 1.3 - 89.6/100 - Gemini 3.8 Flash - 81.0/100 Performans / $ - Grok 4.6 - 83.7/100 - Muse Spark 1.3 - 80.0/100 - Gemini 3.8 Flash - 62.3/100 - Fable 5.1 - 15.6/100 Teknik İnceleme Metrikleri: 1) Prompt uyumu: Fable'ın substep genişletmesi ve Muse'un bazı spesifikasyonları sadeleştirmesi nedeniyle kimseye 10 vermiyorum. - Fable 5.1: 9.6/10 - Grok 4.6: 9.3/10 - Gemini 3.8 Flash: 8.8/10 - Muse Spark 1.3: 8.2/10 2) Newtonian physics + conservation: Dört modelin de bu kategori şaşırtıcı derecede güçlü. Muse dahil pairwise equal-and-opposite acceleration ve aynı softened potential kernel'ini kullanıyor. - Fable 5.1: 9.9/10 - Grok 4.6: 9.8/10 - Gemini 3.8 Flash: 9.8/10 - Muse Spark 1.3: 9.6/10 3) Symplectic integrasyon + fixed timestep: Gemini'nin Yoshida formülü yanlış değil; puan kaybı scheduler/backlog yönetiminden geliyor. Numerical method doğru, execution model chaos altında sorunlu. - Fable 5.1: 9.6/10 - Grok 4.6: 9.5/10 - Muse Spark 1.3: 9.4/10 - Gemini 3.8 Flash: 8.0/10 4) Self-check / validator doğruluğu: Burada Fable açık lider. Gemini çok yakın; dtExact metodolojik küçük pürüz. Grok/Muse PASS ama validator resolution daha kaba. - Fable 5.1: 10.0/10 - Gemini 3.8 Flash: 9.4/10 - Grok 4.6: 8.6/10 - Muse Spark 1.3: 8.0/10 5) Görsel kalite: Gemini'nin celestial-body shading'i ve UI'sı çok iyi. Fable ise kompozisyon ve “görsel gürültüyü bastırma” açısından biraz daha dengeli. Muse özellikle büyük title choreography ile etkileyici. Grok en restrained olanı. - Fable 5.1: 9.6/10 - Gemini 3.8 Flash: 9.5/10 - Grok 4.6: 9.2/10 - Muse Spark 1.3: 9.1/10 6) Trail rendering: Gemini'nin single-system trail'i güzel; fakat chaos trail timestamp kaynağındaki hata bu kategori için ciddi. - Fable 5.1: 9.7/10 - Grok 4.6: 9.3/10 - Muse Spark 1.3: 9.0/10 - Gemini 3.8 Flash: 7.0/10 Alternatif winner'lar: - En iyi saf numerical correctness: Fable 5.1 - En iyi self-validator: Fable 5.1 - En iyi görsel kalite: Fable 5.1 ≈ Gemini 3.8 Flash - En iyi chaos throughput: Muse Spark 1.3 - En iyi genel mimari: Fable 5.1 - En iyi fiyat/performans: Grok 4.6 - En düşük maliyet: Grok 4.6 - Sadece physics/integrator kodu puanlansaydı: Fable ≈ Grok > Gemini ≈ Muse - Maliyet de kararın temel kriteri olsaydı: Grok > Muse > Gemini >>> Fable

Alican Kiraz

19,607 Aufrufe • vor 18 Tagen

DeepSeek V4.1 Flash just one shot Minecraft. It built this in 11 minutes and cost less than $0.25. Muse Spark 1.3 and Gemini 3.8 Flash completely failed on this same exact prompt. DeepSeek cooked.
0:58

DeepSeek V4.1 Flash just one shot Minecraft. It built this in 11 minutes and cost less than $0.25. Muse Spark 1.3 and Gemini 3.8 Flash completely failed on this same exact prompt. DeepSeek cooked.

BridgeMind

47,564 Aufrufe • vor 10 Tagen

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
0:29

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 Aufrufe • vor 2 Monaten

Tested FlappyBench with GLM 5.3, Fable 5 and GPT-5.6 Sol 3 models. Same prompt with /design command. Scored on features, UX/UI, and cost. 🔹 Fable 5 → 9.5/10 · $0.420 🔹 GLM 5.3 → 9/10 · $0.018 🔹 GPT-5.6 Sol → 9/10 · $0.150 Results: → Fable 5 wins on quality, but costs 23x more than GLM 5.3 → GLM 5.3 gives better output than GPT 5.6 Sol at 8x cheaper → Optimizing for cost? Go for GLM 5.3. Otherwise, Fable 5 if quality matters
0:30

Tested FlappyBench with GLM 5.3, Fable 5 and GPT-5.6 Sol 3 models. Same prompt with /design command. Scored on features, UX/UI, and cost. 🔹 Fable 5 → 9.5/10 · $0.420 🔹 GLM 5.3 → 9/10 · $0.018 🔹 GPT-5.6 Sol → 9/10 · $0.150 Results: → Fable 5 wins on quality, but costs 23x more than GLM 5.3 → GLM 5.3 gives better output than GPT 5.6 Sol at 8x cheaper → Optimizing for cost? Go for GLM 5.3. Otherwise, Fable 5 if quality matters

Command Code

22,860 Aufrufe • vor 1 Monat

We ran a test between the new Qwen3.8-Max, Opus 5 and GPT-5.6 Sol. 3 models. same prompt. one-shot with the /design command. Reviewed gameplay features, UX/UI and cost. 🔹 Qwen3.8-Max → 9/10 · $0.0248 🔹 GPT-5.6 Sol → 9/10 · $0.150 🔹 Opus 5 → 8.5/10 · $0.253 Qwen3.8-Max is approximately 4.2× cheaper than GPT-5.6, Sol, Opus 5 and has the same level of UI, UX, and gameplay.
0:29

We ran a test between the new Qwen3.8-Max, Opus 5 and GPT-5.6 Sol. 3 models. same prompt. one-shot with the /design command. Reviewed gameplay features, UX/UI and cost. 🔹 Qwen3.8-Max → 9/10 · $0.0248 🔹 GPT-5.6 Sol → 9/10 · $0.150 🔹 Opus 5 → 8.5/10 · $0.253 Qwen3.8-Max is approximately 4.2× cheaper than GPT-5.6, Sol, Opus 5 and has the same level of UI, UX, and gameplay.

Command Code

179,535 Aufrufe • vor 1 Monat

We ran a test between Opus 5, GPT-5.6 Sol, and Kimi K3. One prompt. Across all three models. With our /design command. Results: We reviewed gameplay, UX, and UI 🔹 Opus 5 is strong UI + motion, best gameplay 🔹 Kimi K3 has solid design and gameplay 🔹 GPT-5.6 Sol has best UI, worse gameplay Ranking (DX, features, and cost): 🔹 Opus 5: 10/10 · $0.25 🔹 Kimi K3: 9.5/10 · $0.12 🔹 GPT-5.6 Sol: 7/10 · $0.32
0:29

We ran a test between Opus 5, GPT-5.6 Sol, and Kimi K3. One prompt. Across all three models. With our /design command. Results: We reviewed gameplay, UX, and UI 🔹 Opus 5 is strong UI + motion, best gameplay 🔹 Kimi K3 has solid design and gameplay 🔹 GPT-5.6 Sol has best UI, worse gameplay Ranking (DX, features, and cost): 🔹 Opus 5: 10/10 · $0.25 🔹 Kimi K3: 9.5/10 · $0.12 🔹 GPT-5.6 Sol: 7/10 · $0.32

Command Code

24,686 Aufrufe • vor 1 Monat

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.
0:28

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 Aufrufe • vor 18 Tagen

DeepSeek V4.1 Flash is a very interesting model. I just tested it on the BridgeBench lava lamp test and it took LONGER to complete than Fable 5.1 and GPT 6 Astra. It ran at 344 toks/sec, spent 23.5M tokens with a cache hit rate of 99.7%, and cost $0.33. Even though it runs so fast it ends up completing tasks very slowly. The lava lamp that it produced was better than Muse Spark 1.3 and Gemini 3.8 Flash.
1:15

DeepSeek V4.1 Flash is a very interesting model. I just tested it on the BridgeBench lava lamp test and it took LONGER to complete than Fable 5.1 and GPT 6 Astra. It ran at 344 toks/sec, spent 23.5M tokens with a cache hit rate of 99.7%, and cost $0.33. Even though it runs so fast it ends up completing tasks very slowly. The lava lamp that it produced was better than Muse Spark 1.3 and Gemini 3.8 Flash.

BridgeMind

128,202 Aufrufe • vor 11 Tagen

deepseek v4.1 flash ran all four three.js scenes for $0.15. gemini 3.8 flash needed $1.25 we put the two models on one job: a self-contained html page that renders an animated 3d scene with three.js – a storm at sea, a spiral galaxy, a tornado over farmland, a tesla coil on a dark table. same prompt - calls until all four scenes were usable #1 deepseek v4.1 flash – 6 #2 gemini 3.8 flash – 12 - time #1 deepseek v4.1 flash – 13m 57s #2 gemini 3.8 flash – 32m 02s - cost #1 deepseek v4.1 flash – $0.15 #2 gemini 3.8 flash – $1.25 observations: • gemini blew out the exposure on 4 of 5 first attempts – a white blob where the tesla coil should be. the prompt had a paragraph on exposure. it did not help. • both models overcorrect in the fix loop: "too bright" becomes "almost black". round two is where it lands. same four scenes, 6 calls and $0.15 against 12 calls and $1.25!
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deepseek v4.1 flash ran all four three.js scenes for $0.15. gemini 3.8 flash needed $1.25 we put the two models on one job: a self-contained html page that renders an animated 3d scene with three.js – a storm at sea, a spiral galaxy, a tornado over farmland, a tesla coil on a dark table. same prompt - calls until all four scenes were usable #1 deepseek v4.1 flash – 6 #2 gemini 3.8 flash – 12 - time #1 deepseek v4.1 flash – 13m 57s #2 gemini 3.8 flash – 32m 02s - cost #1 deepseek v4.1 flash – $0.15 #2 gemini 3.8 flash – $1.25 observations: • gemini blew out the exposure on 4 of 5 first attempts – a white blob where the tesla coil should be. the prompt had a paragraph on exposure. it did not help. • both models overcorrect in the fix loop: "too bright" becomes "almost black". round two is where it lands. same four scenes, 6 calls and $0.15 against 12 calls and $1.25!

thehype.

62,603 Aufrufe • vor 10 Tagen

Muse Spark 1.3 Ultra Contributor vs Fable 5.1 xHigh Same prompt, both ran for roughly 2 hours The prompt had a self improvement rule: if the independent judges scored the result below 9.5/10, it had to keep improving and try again, What shocked me most was that Muse Spark just NEVER stopped, It went through 20 self improvement loops, launching 3 agents each time basically 60+ agent runs over 2 hours And the cost?, Less than $1, Yes, under $1… in Ultra mode ,This completely exceeded my expectations Huge credit to Meta, this thing is a work of art
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Muse Spark 1.3 Ultra Contributor vs Fable 5.1 xHigh Same prompt, both ran for roughly 2 hours The prompt had a self improvement rule: if the independent judges scored the result below 9.5/10, it had to keep improving and try again, What shocked me most was that Muse Spark just NEVER stopped, It went through 20 self improvement loops, launching 3 agents each time basically 60+ agent runs over 2 hours And the cost?, Less than $1, Yes, under $1… in Ultra mode ,This completely exceeded my expectations Huge credit to Meta, this thing is a work of art

Iam_

150,400 Aufrufe • vor 18 Tagen

Grok Code just literally cloned the entire Netflix UI with a one shot prompt From the iconic Hero section to the interactive Carousel, all with a single prompt and a few web-sourced image links It’s not just fast, it's the most cost-effective SOTA model out there ➝ Input: Grok $0.20 vs Claude $3 (15x cheaper) ➝ Output: Grok $1.50 vs Claude $15 (10x cheaper) Here’s how you can do it in VS Code with Kilo Code Grok Code matches Claude Sonnet 4 in performance, but is 10-15x more cost-effective All this for less than $1 SOTA power, fractional cost
1:23

Grok Code just literally cloned the entire Netflix UI with a one shot prompt From the iconic Hero section to the interactive Carousel, all with a single prompt and a few web-sourced image links It’s not just fast, it's the most cost-effective SOTA model out there ➝ Input: Grok $0.20 vs Claude $3 (15x cheaper) ➝ Output: Grok $1.50 vs Claude $15 (10x cheaper) Here’s how you can do it in VS Code with Kilo Code Grok Code matches Claude Sonnet 4 in performance, but is 10-15x more cost-effective All this for less than $1 SOTA power, fractional cost

X Freeze

6,973,685 Aufrufe • vor 1 Jahr

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
0:45

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 Aufrufe • vor 1 Monat

ox alpha vs deepseek v4 flash vision vs grok 4.6 vs gemini 3.7 flash vs – on photo-to-3d four vision models got one photograph each and had to rebuild the place inside it as a Three.js scene. twelve scenes, twelve first-try runs, zero console errors the setup: one reference photo per scene, sent as an image on OpenRouter. the prompt never says what is in the picture – no "motel", no "bar", no "gas station". the model has to read the photo and rebuild it: layout, materials, hour of the day, and whatever is around the corner that the frame does not show tasks – three photographs of early-2000s america: 1. a motel at night, neon pylon lit, snow on the ground 2. an old new york tavern interior, tin ceiling, tiled floor 3. an abandoned service station in the california desert, midday sun each scene ships as one self-contained html file, procedural geometry and canvas textures only, no downloads. three timed camera shots, and shot 1 has to reproduce the framing of the reference photo models: xAI grok 4.6, Google DeepMind gemini 3.7 flash, DeepSeek deepseek v4 flash vision exp, and ox alpha – a stealth model on openrouter, free, no lab attached to it yet results: - wall clock, three scenes #1 gemini 3.7 flash – 11m 12s #2 deepseek v4 flash – 15m 20s #3 grok 4.6 – 28m 11s #4 ox alpha – 38m 54s - output tokens #1 gemini 3.7 flash – 77,396 #2 ox alpha – 87,613 #3 grok 4.6 – 105,687 #4 deepseek v4 flash – 127,884 - lines of code shipped #1 ox alpha – 2,090 #2 deepseek v4 flash – 2,291 #3 grok 4.6 – 3,529 #4 gemini 3.7 flash – 3,989 - total price #1 ox alpha – $0.000 #2 deepseek v4 flash – $0.091 #3 gemini 3.7 flash – $0.136 #4 grok 4.6 – $0.697 observations: • grok is 7.7x the price of deepseek. it is the only model that read the light – low sun, real shadows on the station, a cold night on the motel • gemini is the fastest and the least deliberate. 17,158 reasoning tokens against deepseek's 99,172, and it still shipped the most code – 3,989 lines • deepseek thought hardest and rendered plainest. 99,172 reasoning tokens, 5.8x gemini's, spent on layout rather than on light. its motel is the second best in the set for $0.030 • ox alpha is free and reads a photo as well as anything here – it lifted "family units / kitchenettes" off the pylon and redrew it in canvas conclusion: twelve scenes, four models, zero fixes, and the whole run cost $0.924! follow thehype. for 24/7 ai news, analysis and breakdowns
0:36

ox alpha vs deepseek v4 flash vision vs grok 4.6 vs gemini 3.7 flash vs – on photo-to-3d four vision models got one photograph each and had to rebuild the place inside it as a Three.js scene. twelve scenes, twelve first-try runs, zero console errors the setup: one reference photo per scene, sent as an image on OpenRouter. the prompt never says what is in the picture – no "motel", no "bar", no "gas station". the model has to read the photo and rebuild it: layout, materials, hour of the day, and whatever is around the corner that the frame does not show tasks – three photographs of early-2000s america: 1. a motel at night, neon pylon lit, snow on the ground 2. an old new york tavern interior, tin ceiling, tiled floor 3. an abandoned service station in the california desert, midday sun each scene ships as one self-contained html file, procedural geometry and canvas textures only, no downloads. three timed camera shots, and shot 1 has to reproduce the framing of the reference photo models: xAI grok 4.6, Google DeepMind gemini 3.7 flash, DeepSeek deepseek v4 flash vision exp, and ox alpha – a stealth model on openrouter, free, no lab attached to it yet results: - wall clock, three scenes #1 gemini 3.7 flash – 11m 12s #2 deepseek v4 flash – 15m 20s #3 grok 4.6 – 28m 11s #4 ox alpha – 38m 54s - output tokens #1 gemini 3.7 flash – 77,396 #2 ox alpha – 87,613 #3 grok 4.6 – 105,687 #4 deepseek v4 flash – 127,884 - lines of code shipped #1 ox alpha – 2,090 #2 deepseek v4 flash – 2,291 #3 grok 4.6 – 3,529 #4 gemini 3.7 flash – 3,989 - total price #1 ox alpha – $0.000 #2 deepseek v4 flash – $0.091 #3 gemini 3.7 flash – $0.136 #4 grok 4.6 – $0.697 observations: • grok is 7.7x the price of deepseek. it is the only model that read the light – low sun, real shadows on the station, a cold night on the motel • gemini is the fastest and the least deliberate. 17,158 reasoning tokens against deepseek's 99,172, and it still shipped the most code – 3,989 lines • deepseek thought hardest and rendered plainest. 99,172 reasoning tokens, 5.8x gemini's, spent on layout rather than on light. its motel is the second best in the set for $0.030 • ox alpha is free and reads a photo as well as anything here – it lifted "family units / kitchenettes" off the pylon and redrew it in canvas conclusion: twelve scenes, four models, zero fixes, and the whole run cost $0.924! follow thehype. for 24/7 ai news, analysis and breakdowns

thehype.

24,272 Aufrufe • vor 27 Tagen

Today, we're introducing not one but TWO new models, striking the balance between efficiency and quality to enable you to build production AI agents. — Gemini 3.6 Flash: Addresses efficiency feedback we received from Gemini 3.5 Flash with upgrades in coding, knowledge work, and multimodal tasks faster, more accurately, and with substantially fewer tokens per task — Gemini 3.5 Flash-Lite: Our fastest, most cost-effective 3.5-class model yet built for agentic workflows, hitting ~350 output tokens/sec with improved coding and overall quality Start building with these today via the Gemini API in Google AI Studio or try them out in the Google Gemini
0:42

Today, we're introducing not one but TWO new models, striking the balance between efficiency and quality to enable you to build production AI agents. — Gemini 3.6 Flash: Addresses efficiency feedback we received from Gemini 3.5 Flash with upgrades in coding, knowledge work, and multimodal tasks faster, more accurately, and with substantially fewer tokens per task — Gemini 3.5 Flash-Lite: Our fastest, most cost-effective 3.5-class model yet built for agentic workflows, hitting ~350 output tokens/sec with improved coding and overall quality Start building with these today via the Gemini API in Google AI Studio or try them out in the Google Gemini

Google AI

438,702 Aufrufe • vor 2 Monaten

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
1:09

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 23 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?
0:25

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 Aufrufe • vor 17 Tagen

Our most intelligent workhorse model yet for coding and agents has arrived ⚡ Meet Gemini 3.7 Flash. — Crush that seemingly endless to-do list. Gemini Spark in the Google Gemini now uses 3.7 Flash. The new model can equip your personal AI agent to work even smarter for you by seamlessly handling complex, multi-step tasks across your Google Workspace apps like Gmail, Google Calendar and Google Docs — Enjoy a smoother build experience. The model thinks more diligently, putting more effort into multi-step planning and tool calls. A more disciplined execution means less manual oversight and fewer retries across engineering workflows — Build more, spend less. 3.7 Flash is available through the end of the year with an introductory price of half the original 3.6 Flash cost per million tokens ($0.75/1M input tokens and $3.75/1M output tokens)
1:15

Our most intelligent workhorse model yet for coding and agents has arrived ⚡ Meet Gemini 3.7 Flash. — Crush that seemingly endless to-do list. Gemini Spark in the Google Gemini now uses 3.7 Flash. The new model can equip your personal AI agent to work even smarter for you by seamlessly handling complex, multi-step tasks across your Google Workspace apps like Gmail, Google Calendar and Google Docs — Enjoy a smoother build experience. The model thinks more diligently, putting more effort into multi-step planning and tool calls. A more disciplined execution means less manual oversight and fewer retries across engineering workflows — Build more, spend less. 3.7 Flash is available through the end of the year with an introductory price of half the original 3.6 Flash cost per million tokens ($0.75/1M input tokens and $3.75/1M output tokens)

Google AI

128,952 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
0:30

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.

15,997 Aufrufe • vor 25 Tagen