Загрузка видео...

Не удалось загрузить видео

На главную

I gave Kimi K2.6 and K2.7 Code the exact same prompt to animate a letter burning to ash. Pure HTML canvas, zero libraries.

stevibe's profile picture

stevibe

27,021 subscribers

89,828 просмотров • 2 месяцев назад •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

GLM 5.1 vs GLM 5.2 6 advanced HTML canvas challenges: 💧 Ink diffusing in water ⚔️ Energy-blade duel 📱 Slide to unlock 🅿️ 360° parking assist 🔥 Burning letter to ash 🏠 Build-a-house sequence Pure canvas, zero libraries.
0:53

GLM 5.1 vs GLM 5.2 6 advanced HTML canvas challenges: 💧 Ink diffusing in water ⚔️ Energy-blade duel 📱 Slide to unlock 🅿️ 360° parking assist 🔥 Burning letter to ash 🏠 Build-a-house sequence Pure canvas, zero libraries.

stevibe

347,364 просмотров • 2 месяцев назад

New Kimi K2.7 Code performs at GPT-5.5 level 3x cheaper! We gave both models the same three prompts: build a self-contained HTML5 canvas sim with real physics, no libraries. A spring pendulum on a stretching coil, a 1 kg block trading collisions with a 100,000 kg block, and 22 balls churning in a spinning hexagon Outputs: Kimi K2.7 Code: $0.28 on 52.4k tokens GPT-5.5: $0.93 on 23.4k tokens Spring pendulums and blocks came out even. The balls Kimi did better: its pile spins with the drum when GPT's bounce around in pure chaos. On price to quality, K2.7 Code is the clear pick
0:25

New Kimi K2.7 Code performs at GPT-5.5 level 3x cheaper! We gave both models the same three prompts: build a self-contained HTML5 canvas sim with real physics, no libraries. A spring pendulum on a stretching coil, a 1 kg block trading collisions with a 100,000 kg block, and 22 balls churning in a spinning hexagon Outputs: Kimi K2.7 Code: $0.28 on 52.4k tokens GPT-5.5: $0.93 on 23.4k tokens Spring pendulums and blocks came out even. The balls Kimi did better: its pile spins with the drum when GPT's bounce around in pure chaos. On price to quality, K2.7 Code is the clear pick

atomic.chat

139,760 просмотров • 2 месяцев назад

holy shit Kimi K2.6 literally cooked claude design🤯 and it's 7x cheaper try the same prompt on both Kimi and Claude design, and there are the outputs see the exact same prompt 👇
0:21

holy shit Kimi K2.6 literally cooked claude design🤯 and it's 7x cheaper try the same prompt on both Kimi and Claude design, and there are the outputs see the exact same prompt 👇

Guri Singh

303,140 просмотров • 3 месяцев назад

Got a 24GB Graphics Card? These 6 coding models all fit on it (Q4): - qwen3.5:27b (17GB) - qwen3.5:35b (24GB) - glm-4.7-flash (19GB) - nemotron-3-nano:30b (24GB) - nemotron-cascade-2:30b (24GB) - gpt-oss:20b (14GB) I gave them the same challenge: draw a campfire with HTML Canvas. Why Canvas? HTML/CSS forgives bad syntax — things still render. JavaScript + Canvas doesn't — one mistake and the screen goes black.
0:24

Got a 24GB Graphics Card? These 6 coding models all fit on it (Q4): - qwen3.5:27b (17GB) - qwen3.5:35b (24GB) - glm-4.7-flash (19GB) - nemotron-3-nano:30b (24GB) - nemotron-cascade-2:30b (24GB) - gpt-oss:20b (14GB) I gave them the same challenge: draw a campfire with HTML Canvas. Why Canvas? HTML/CSS forgives bad syntax — things still render. JavaScript + Canvas doesn't — one mistake and the screen goes black.

stevibe

75,939 просмотров • 4 месяцев назад

lol...Kimi really cooked Claude Design tested the same prompt on both Kimi K2.6 and Claude Design, and these are the outputs not to mention Kimi is 7x cheaper and 100% open source... see prompt in the comments👇
0:21

lol...Kimi really cooked Claude Design tested the same prompt on both Kimi K2.6 and Claude Design, and these are the outputs not to mention Kimi is 7x cheaper and 100% open source... see prompt in the comments👇

Farhan

325,502 просмотров • 3 месяцев назад

Hello, Kimi K2.6.
0:21

Hello, Kimi K2.6.

stevibe

52,761 просмотров • 3 месяцев назад

Ran two prompts in Kimi K2.7 Code HighSpeed, Kimi K2.7 Code, and GLM 5.2 HighSpeed is 2.5 to 4.2 times faster than Kimi K2.7 Code while producing comparable quality 4.5 to 15 times faster than GLM 5.2, and half the price (or less) These are simple tests, so the pricing difference is small and speed difference could diverge a lot But definitely a hell of a lot faster with good output
0:53

Ran two prompts in Kimi K2.7 Code HighSpeed, Kimi K2.7 Code, and GLM 5.2 HighSpeed is 2.5 to 4.2 times faster than Kimi K2.7 Code while producing comparable quality 4.5 to 15 times faster than GLM 5.2, and half the price (or less) These are simple tests, so the pricing difference is small and speed difference could diverge a lot But definitely a hell of a lot faster with good output

GMI Cloud

38,174 просмотров • 1 месяц назад

Meituan's LongCat-2.0 reportedly lands near GPT-5.5 on SWE-bench. So I threw 5 HTML canvas animation prompts at both. 🥷 Paper sliced fruit-ninja style. 💧 An ink drop diffusing in water. 🔥 A letter burning. 🗑️ Paper crumpling into a ball. ✂️ A strip-cut shredder. Here's how they did 👇
0:37

Meituan's LongCat-2.0 reportedly lands near GPT-5.5 on SWE-bench. So I threw 5 HTML canvas animation prompts at both. 🥷 Paper sliced fruit-ninja style. 💧 An ink drop diffusing in water. 🔥 A letter burning. 🗑️ Paper crumpling into a ball. ✂️ A strip-cut shredder. Here's how they did 👇

stevibe

34,963 просмотров • 1 месяц назад

Which LLMs actually love to think? Tested 7 models on 5 math problems, measured reasoning length. The think winners: both Qwen3.5 models (27B and 35B A3B) — massive overthinkers, up to 10k+ tokens on a single question. Plot twists: > Kimi K2.6 feels verbose, actually one of the leanest > Gemma4 26B A4B solved 2 with ZERO thinking
0:42

Which LLMs actually love to think? Tested 7 models on 5 math problems, measured reasoning length. The think winners: both Qwen3.5 models (27B and 35B A3B) — massive overthinkers, up to 10k+ tokens on a single question. Plot twists: > Kimi K2.6 feels verbose, actually one of the leanest > Gemma4 26B A4B solved 2 with ZERO thinking

stevibe

97,403 просмотров • 3 месяцев назад

I designed a new test specifically for multimodal models: fill out a paper form. And it's much harder than it sounds. This isn't typing into an electronic field that captures your text. The form is just an image. The model has to place each form element: text, checkmarks — at the correct pixel position on the canvas itself. Results: 🟢 Kimi K2.6 → done in 3:45, 16.7k output tokens 🟡 Step 3.7 Flash → half the fields, 57k output tokens 🔴 Gemini 3.5 Flash → 489k output tokens, never finished. I had to kill it. Gemini burned ~29x more output tokens than Kimi on the exact same task, and Kimi's was the only form that actually looked filled out. The test, a mocked application form, contains some challenging parts, such as one-character-per-box fields. I provided every model the same set of tools: > get canvas size > drop probe markers to find coordinates > add text > add checkmarks > move elements > take a screenshot anytime to check their own work > ... etc So it's vision + spatial reasoning + tool use + long context, all at once. Small models (Qwen, Gemma) can't really complete this test, so I skipped them. What happened: > Kimi nailed name, DOB, ID, gender, marital status, nationality, email, phone, address, postal code — placement slightly loose, but content correct. 15 turns. Clean. > Step got maybe half right — fields dropped, "United States" landed in the email line, data floating outside boxes. Burned 1.24M input tokens doing it (81 turns of re-reading the canvas). > Gemini almost got there visually... then spiraled. By turn 40 it was issuing a delete_elements call wiping element IDs 365–425, basically erasing its own work. 31 minutes, 489k output tokens, still streaming. Terminated. The takeaway isn't "Gemini bad." This test is indeed difficult. But token efficiency is capability now. A model that needs 30x the tokens and still can't converge is going to be 30x the cost in production. Kimi K2.6 just quietly did the thing.
0:38

I designed a new test specifically for multimodal models: fill out a paper form. And it's much harder than it sounds. This isn't typing into an electronic field that captures your text. The form is just an image. The model has to place each form element: text, checkmarks — at the correct pixel position on the canvas itself. Results: 🟢 Kimi K2.6 → done in 3:45, 16.7k output tokens 🟡 Step 3.7 Flash → half the fields, 57k output tokens 🔴 Gemini 3.5 Flash → 489k output tokens, never finished. I had to kill it. Gemini burned ~29x more output tokens than Kimi on the exact same task, and Kimi's was the only form that actually looked filled out. The test, a mocked application form, contains some challenging parts, such as one-character-per-box fields. I provided every model the same set of tools: > get canvas size > drop probe markers to find coordinates > add text > add checkmarks > move elements > take a screenshot anytime to check their own work > ... etc So it's vision + spatial reasoning + tool use + long context, all at once. Small models (Qwen, Gemma) can't really complete this test, so I skipped them. What happened: > Kimi nailed name, DOB, ID, gender, marital status, nationality, email, phone, address, postal code — placement slightly loose, but content correct. 15 turns. Clean. > Step got maybe half right — fields dropped, "United States" landed in the email line, data floating outside boxes. Burned 1.24M input tokens doing it (81 turns of re-reading the canvas). > Gemini almost got there visually... then spiraled. By turn 40 it was issuing a delete_elements call wiping element IDs 365–425, basically erasing its own work. 31 minutes, 489k output tokens, still streaming. Terminated. The takeaway isn't "Gemini bad." This test is indeed difficult. But token efficiency is capability now. A model that needs 30x the tokens and still can't converge is going to be 30x the cost in production. Kimi K2.6 just quietly did the thing.

stevibe

25,455 просмотров • 2 месяцев назад

Round 2: Dense edition Qwen3.5 27B vs Gemma4 31B Same 4 HTML canvas challenges, this time no MoE. Setup: > Quant: Unsloth Q6_K_XL (both) > Each running on an RTX 5090 > Single HTML file, no libraries, real-time animation My read per prompt: 🌳 Tree — Gemma, branches are beautifully structured ✨ Night sky — close, but Qwen's stars blink better 🐟 Fish boids — Qwen, detailed fish and look at that motion, so realistic 🏠 House build — neither really nailed it Agree? Does dense actually beat MoE here?
0:43

Round 2: Dense edition Qwen3.5 27B vs Gemma4 31B Same 4 HTML canvas challenges, this time no MoE. Setup: > Quant: Unsloth Q6_K_XL (both) > Each running on an RTX 5090 > Single HTML file, no libraries, real-time animation My read per prompt: 🌳 Tree — Gemma, branches are beautifully structured ✨ Night sky — close, but Qwen's stars blink better 🐟 Fish boids — Qwen, detailed fish and look at that motion, so realistic 🏠 House build — neither really nailed it Agree? Does dense actually beat MoE here?

stevibe

33,304 просмотров • 3 месяцев назад

I gave the exact same prompt to Qwen 3.7 Max and GLM 5.2. Same workflow. Same platform. Nearly the same starting price. The results weren't even close. Here's what happened. 👇
0:43

I gave the exact same prompt to Qwen 3.7 Max and GLM 5.2. Same workflow. Same platform. Nearly the same starting price. The results weren't even close. Here's what happened. 👇

Md Riyazuddin

153,084 просмотров • 1 месяц назад

Design ∞ code loop, but 10x better. Introducing the browser agent in pen.dev. Import HTML/React to fully editable canvas, make edits/variations, export back to code. Build with the browser & the canvas at the same time, the ultimate combo. Download today.
1:07

Design ∞ code loop, but 10x better. Introducing the browser agent in pen.dev. Import HTML/React to fully editable canvas, make edits/variations, export back to code. Build with the browser & the canvas at the same time, the ultimate combo. Download today.

Tom Krcha

100,938 просмотров • 19 дней назад

Kimi Code is good at video reasoning with Kimi K2.6.🎬 Drag in reference videos, ask about colors, shots, or visual style, and Kimi Code can generate a ready-to-use .cube LUT file.🎞️
0:37

Kimi Code is good at video reasoning with Kimi K2.6.🎬 Drag in reference videos, ask about colors, shots, or visual style, and Kimi Code can generate a ready-to-use .cube LUT file.🎞️

Kimi Developers

13,574 просмотров • 2 месяцев назад

🌘 Meet Kimi K2.7 Code HighSpeed! A high-speed mode of our latest open-source multimodal coding model, Kimi K2.7 Code. ⚡️ Up to 6× faster: Around 180 tok/s on coding tasks with median-length inputs, and up to 260 tok/s on shorter-context tasks. 🔷 Rolling out to Kimi Code Beta Program members, Kimi API developers, and Kimi Business users. (Access will remain limited for now due to capacity constraints.) 🔷 No invite needed. Anyone who joins the Beta Program has a chance to get access 👉 Open intelligence should be instant, affordable, and borderless. We'll continue improving the model and expanding access as more capacity becomes available! 🔗 Kimi Code: 🔗 API:
22:42

🌘 Meet Kimi K2.7 Code HighSpeed! A high-speed mode of our latest open-source multimodal coding model, Kimi K2.7 Code. ⚡️ Up to 6× faster: Around 180 tok/s on coding tasks with median-length inputs, and up to 260 tok/s on shorter-context tasks. 🔷 Rolling out to Kimi Code Beta Program members, Kimi API developers, and Kimi Business users. (Access will remain limited for now due to capacity constraints.) 🔷 No invite needed. Anyone who joins the Beta Program has a chance to get access 👉 Open intelligence should be instant, affordable, and borderless. We'll continue improving the model and expanding access as more capacity becomes available! 🔗 Kimi Code: 🔗 API:

Kimi.ai

569,736 просмотров • 2 месяцев назад

A 3B model just cleared a puzzle that a 1.6 TRILLION param model couldn't. You've seen this benchmark before: my sliding-puzzle test. Same Kimi & DeepSeek runs as last time. The only new thing: I dropped VibeThinker-3B in for a side-by-side. > VibeThinker → 3B > DeepSeek V4 Flash → 284B > Kimi K2.6 → 1T > DeepSeek V4 Pro → 1.6T Shuffle depths 5, 10, 12, 15, 18, 22. One wrong move scrambles the whole board, so it's pure long-chain reasoning. ✅ VibeThinker-3B: solved all six. Never lost the thread. ⚠️ The giants started cracking at depth 15: Flash, Pro, and even Kimi each blew a run, scrambling the board past the move cap. As VibeThinker was not trained for tool calling, I had it emit X and ran the move. Bigger generalist ≠ smarter.
0:48

A 3B model just cleared a puzzle that a 1.6 TRILLION param model couldn't. You've seen this benchmark before: my sliding-puzzle test. Same Kimi & DeepSeek runs as last time. The only new thing: I dropped VibeThinker-3B in for a side-by-side. > VibeThinker → 3B > DeepSeek V4 Flash → 284B > Kimi K2.6 → 1T > DeepSeek V4 Pro → 1.6T Shuffle depths 5, 10, 12, 15, 18, 22. One wrong move scrambles the whole board, so it's pure long-chain reasoning. ✅ VibeThinker-3B: solved all six. Never lost the thread. ⚠️ The giants started cracking at depth 15: Flash, Pro, and even Kimi each blew a run, scrambling the board past the move cap. As VibeThinker was not trained for tool calling, I had it emit X and ran the move. Bigger generalist ≠ smarter.

stevibe

32,494 просмотров • 1 месяц назад

MiniMax M3 vs GLM-5.2 vs Kimi K2.7 in Lunar Lander contest! 🔥 Last test of the day before going back to learning & build phase with Local Models, Swift and Apple Core AI 💪 I took the opportunity to test the MiniMax Code app for macOS. Video below speeded up. Followed by Lunar Lander implementation from the various models. My ranking: 🥇 GLM 5.2 🥈 MiniMax M3 🥉 Kimi K2.7 Coding Prompt and final results in this gist:
0:42

MiniMax M3 vs GLM-5.2 vs Kimi K2.7 in Lunar Lander contest! 🔥 Last test of the day before going back to learning & build phase with Local Models, Swift and Apple Core AI 💪 I took the opportunity to test the MiniMax Code app for macOS. Video below speeded up. Followed by Lunar Lander implementation from the various models. My ranking: 🥇 GLM 5.2 🥈 MiniMax M3 🥉 Kimi K2.7 Coding Prompt and final results in this gist:

Ivan Fioravanti ᯅ

21,604 просмотров • 2 месяцев назад

New Z.ai GLM-5.2 beats Kimi K2.7 Code on physics contest! We gave both models the same three prompts and asked them to build self contained HTML5 sims with real physics and no libraries: 1. Pool break 2. Block on a bed of springs 3. Galton board Outputs: GLM-5.2: 12,640 tokens Kimi K2.7 Code: 7,420 tokens GLM 5.2 nailed all three, and it did it with way more detail and polish. The break conserved momentum, the block bounced off the springs and the Galton beads spread into a clean bell curve. Kimi struggled on every scene: its block fell straight through the springs, its break didn't look realistic with the balls colliding all wrong, and on the Galton board its balls overlapped and piled into each other instead of spreading out
0:33

New Z.ai GLM-5.2 beats Kimi K2.7 Code on physics contest! We gave both models the same three prompts and asked them to build self contained HTML5 sims with real physics and no libraries: 1. Pool break 2. Block on a bed of springs 3. Galton board Outputs: GLM-5.2: 12,640 tokens Kimi K2.7 Code: 7,420 tokens GLM 5.2 nailed all three, and it did it with way more detail and polish. The break conserved momentum, the block bounced off the springs and the Galton beads spread into a clean bell curve. Kimi struggled on every scene: its block fell straight through the springs, its break didn't look realistic with the balls colliding all wrong, and on the Galton board its balls overlapped and piled into each other instead of spreading out

atomic.chat

60,899 просмотров • 2 месяцев назад

Can an open-weight coding agent + harness match Claude Code at training a domain-specific model? Same one-line prompt. ~13 min e2e. Pushed to Hugging Face. Pi + @moonshotai Kimi K2.6 vs Claude Code + Opus 4.7. Task: classify NC session laws (1866-1967) as Jim Crow or not
4:48

Can an open-weight coding agent + harness match Claude Code at training a domain-specific model? Same one-line prompt. ~13 min e2e. Pushed to Hugging Face. Pi + @moonshotai Kimi K2.6 vs Claude Code + Opus 4.7. Task: classify NC session laws (1866-1967) as Jim Crow or not

Daniel van Strien

75,883 просмотров • 3 месяцев назад

"Its (Sonnet 5) performance is close to Opus 4.8, at lower prices." So I ran 4 canvas test through both. > Opus 4.8, 4/4 actually animating. > Sonnet 5, 2/4 came back as static images. And "lower price"? On the paper shredder task, Sonnet 5 spent $0.36 for a static image. Opus 4.8 spent $0.18 and it actually animated. The 4 tests: > Win 98 drag-to-BSOD > Self-typing keyboard + CRT > Letter burning > Paper shredder
0:34

"Its (Sonnet 5) performance is close to Opus 4.8, at lower prices." So I ran 4 canvas test through both. > Opus 4.8, 4/4 actually animating. > Sonnet 5, 2/4 came back as static images. And "lower price"? On the paper shredder task, Sonnet 5 spent $0.36 for a static image. Opus 4.8 spent $0.18 and it actually animated. The 4 tests: > Win 98 drag-to-BSOD > Self-typing keyboard + CRT > Letter burning > Paper shredder

stevibe

60,788 просмотров • 1 месяц назад