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GLM 5.2 vs Kimi K2.7 both were tested on same settings with same prompt > GLM one shotted everything in 35 mins > Kimi needed extra prompts to fix movements and took 30 mins super surpised by how good GLM 5.2 is, while being so cheap which one do... show more

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63,741 просмотров • 2 месяцев назад •via X (Twitter)

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Opus 4.8 vs GLM 5.2 everyone is praising Glm on my timeline, so i had to test it myself > both models were given the same prompt > GLM was super fast and used like 2% of my weekly limit > Opus took over 20 mins and used like 3% of weekly limits which one do you think won?

Opus 4.8 vs GLM 5.2 everyone is praising Glm on my timeline, so i had to test it myself > both models were given the same prompt > GLM was super fast and used like 2% of my weekly limit > Opus took over 20 mins and used like 3% of weekly limits which one do you think won?

J A Z I I

48,108 просмотров • 1 месяц назад

Opus 4.8 vs MiniMax M3 tested both on default settings with the same prompt > Opus one shotted everything in 7 minutes > M3 needed an extra prompt to fix the "break block" feature and took 20+ minutes both got super close, judge both and lemme know which one looks better?

Opus 4.8 vs MiniMax M3 tested both on default settings with the same prompt > Opus one shotted everything in 7 minutes > M3 needed an extra prompt to fix the "break block" feature and took 20+ minutes both got super close, judge both and lemme know which one looks better?

J A Z I I

67,862 просмотров • 2 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

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

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:

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 • 1 месяц назад

We ran a test comparing GLM 5.2 vs Sonnet 5. We used the same prompt, with /design in the command code. The difference is big - effects, motion, and especially the footer. Sonnet 5 had nice hover effects, but didn't come close to GLM 5.2 GLM 5.2 wins on quality and pricing.

We ran a test comparing GLM 5.2 vs Sonnet 5. We used the same prompt, with /design in the command code. The difference is big - effects, motion, and especially the footer. Sonnet 5 had nice hover effects, but didn't come close to GLM 5.2 GLM 5.2 wins on quality and pricing.

Command Code

44,260 просмотров • 1 месяц назад

Fable 5 vs GPT 5.5 tested both on High settings with the same prompt > Fable one shotted everything in 15 minutes and used 3% of weekly limits > GPT 5.5 took 20 minutes and used 3% of weekly limits 5.5 is not looking good here at all which one do you think won?

Fable 5 vs GPT 5.5 tested both on High settings with the same prompt > Fable one shotted everything in 15 minutes and used 3% of weekly limits > GPT 5.5 took 20 minutes and used 3% of weekly limits 5.5 is not looking good here at all which one do you think won?

J A Z I I

107,777 просмотров • 2 месяцев назад

1-bit GLM-5.2 GGUF vs. Claude 4.8 Opus vs. GPT-5.5 We gave 3 models the same prompt and compared one-shot outputs. The 1-bit GLM-5.2 GGUF ran locally on a Mac Studio M3 Ultra with 256GB RAM at ~21.6 tok/s. Which output do you like best? GGUF:

1-bit GLM-5.2 GGUF vs. Claude 4.8 Opus vs. GPT-5.5 We gave 3 models the same prompt and compared one-shot outputs. The 1-bit GLM-5.2 GGUF ran locally on a Mac Studio M3 Ultra with 256GB RAM at ~21.6 tok/s. Which output do you like best? GGUF:

Unsloth AI

1,541,380 просмотров • 1 месяц 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

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 • 1 месяц назад

I re-ran my Fable 5 3D dashboard demo with GLM-5.2. Same prompt. Same reference image. The result is actually pretty solid: 3D globe, route arcs, glass panels, stats cards, and a usable demo page. It is not at Fable 5 level yet, especially in visual precision and one-shot polish. But GLM-5.2 is accessible to all GLM Coding Plan users now. If you’re curious, it’s worth trying. Demo webpage + prompt below 👇

I re-ran my Fable 5 3D dashboard demo with GLM-5.2. Same prompt. Same reference image. The result is actually pretty solid: 3D globe, route arcs, glass panels, stats cards, and a usable demo page. It is not at Fable 5 level yet, especially in visual precision and one-shot polish. But GLM-5.2 is accessible to all GLM Coding Plan users now. If you’re curious, it’s worth trying. Demo webpage + prompt below 👇

AI Builder Club

49,848 просмотров • 2 месяцев назад

Grok should be praised a lot , this is one of the biggest launch > priced way less than even kimi k2.7 , glm 5.2 > on artificial code analysis only behind fable 5 and gpt 5.5 xhigh > this is well rounded launch i will showcase a 3d test that will blow everyone mind

Grok should be praised a lot , this is one of the biggest launch > priced way less than even kimi k2.7 , glm 5.2 > on artificial code analysis only behind fable 5 and gpt 5.5 xhigh > this is well rounded launch i will showcase a 3d test that will blow everyone mind

Chetaslua

25,644 просмотров • 1 месяц назад

I love using GLM 5.2 for web app iteration. My workflow: generate 6 variations, then pick the best one and continue iterating on it. I built Recast to make this even easier. Give it a prompt, get 6 variations, download the code for your favorite version, then continue iterating in your own coding agent. GLM 5.2 works really well for this use case since it's fast, cheap, and great at design. On average, you can make 3-6 landing pages with GLM 5.2 for every one you can generate with Opus 4.8. And the design variety of the GLM landing pages helps a lot with trying to think through what style you want to go with. Recast is launching soon (as a web app + skill).

I love using GLM 5.2 for web app iteration. My workflow: generate 6 variations, then pick the best one and continue iterating on it. I built Recast to make this even easier. Give it a prompt, get 6 variations, download the code for your favorite version, then continue iterating in your own coding agent. GLM 5.2 works really well for this use case since it's fast, cheap, and great at design. On average, you can make 3-6 landing pages with GLM 5.2 for every one you can generate with Opus 4.8. And the design variety of the GLM landing pages helps a lot with trying to think through what style you want to go with. Recast is launching soon (as a web app + skill).

Hassan

20,164 просмотров • 1 месяц назад

🚨 Kimi Cooked So Hard with this release This is one Shotted by Kimi k2.7 fast , math guys try kimi i think its better in math too now Fable was able to do this but kimi only took less than a min lol , i had early access to it

🚨 Kimi Cooked So Hard with this release This is one Shotted by Kimi k2.7 fast , math guys try kimi i think its better in math too now Fable was able to do this but kimi only took less than a min lol , i had early access to it

Chetaslua

55,498 просмотров • 1 месяц назад

I couldn't believe the results of this benchmarking test. I blind-tested 6 frontier models — Sonnet 5, Opus 4.8, Fable 5, GPT 5.5, GLM 5.2, and Kimi 2.5 — on knowledge work, not engineering. Writing. Analysis. Design. Vibe coding. Subagent simulations. The stuff we actually do all day. Same prompts, each model running its own subagent, every output graded blind before the labels were revealed. The results: → GLM 5.2 (the dirt-cheap open-source model from China) took first → Sonnet 5 took second, beating Fable 5 for knowledge work → Opus 4.8 got whooped Then I had Composer 2.5 judge every output independently — and it picked Fable 5 almost across the board. My blind picks and the AI judge completely disagreed. Full breakdown, method, and prompts in the video below. Which model are you running as your daily driver right now?

I couldn't believe the results of this benchmarking test. I blind-tested 6 frontier models — Sonnet 5, Opus 4.8, Fable 5, GPT 5.5, GLM 5.2, and Kimi 2.5 — on knowledge work, not engineering. Writing. Analysis. Design. Vibe coding. Subagent simulations. The stuff we actually do all day. Same prompts, each model running its own subagent, every output graded blind before the labels were revealed. The results: → GLM 5.2 (the dirt-cheap open-source model from China) took first → Sonnet 5 took second, beating Fable 5 for knowledge work → Opus 4.8 got whooped Then I had Composer 2.5 judge every output independently — and it picked Fable 5 almost across the board. My blind picks and the AI judge completely disagreed. Full breakdown, method, and prompts in the video below. Which model are you running as your daily driver right now?

JJ Englert

10,737 просмотров • 1 месяц назад

NVIDIA is giving away free access to 140+ AI models for a full year > most people building AI agents are paying $50-200/month for API access NVIDIA just made that argument irrelevant models you get: GLM 5.2, MiniMax M3, Nemotron-3-Ultra-550B-A55B, Kimi K2.7, and 130+ more setup: > step 1 - get your free key > go to > register -> bind phone -> copy API key > step 2 - add to Hermes agent > open Settings -> Model Provider -> Custom base_url = " api_key = "nvapi-xxxxxxxxxxxxxxxxxxxx" > step 3 - pick a model model = "z-ai/glm-5.2" model = "minimaxai/minimax-m3" model = "nvidia/nemotron-3-ultra-550b-a55b" model = "moonshot-ai/kimi-k2.7" > Hermes already has NVIDIA set as default base_url > paste the key and you're running instantly > works the same in Cursor and OpenCode > cost: $0 > limit: 40 req/min > expires: 1 year while everyone is paying for API access, this is sitting there for free with this many models in one place, it is also the easiest way to test which one actually fits your agent before you commit to a paid provider

NVIDIA is giving away free access to 140+ AI models for a full year > most people building AI agents are paying $50-200/month for API access NVIDIA just made that argument irrelevant models you get: GLM 5.2, MiniMax M3, Nemotron-3-Ultra-550B-A55B, Kimi K2.7, and 130+ more setup: > step 1 - get your free key > go to > register -> bind phone -> copy API key > step 2 - add to Hermes agent > open Settings -> Model Provider -> Custom base_url = " api_key = "nvapi-xxxxxxxxxxxxxxxxxxxx" > step 3 - pick a model model = "z-ai/glm-5.2" model = "minimaxai/minimax-m3" model = "nvidia/nemotron-3-ultra-550b-a55b" model = "moonshot-ai/kimi-k2.7" > Hermes already has NVIDIA set as default base_url > paste the key and you're running instantly > works the same in Cursor and OpenCode > cost: $0 > limit: 40 req/min > expires: 1 year while everyone is paying for API access, this is sitting there for free with this many models in one place, it is also the easiest way to test which one actually fits your agent before you commit to a paid provider

Mr. Buzzoni

120,852 просмотров • 29 дней назад

$50 IN FREE CREDITS FOR GLM 5.2, KIMI K3 AND DEEPSEEK V4 PRO, ON A CODE THE COMPANY PUBLISHED ITSELF • ai&, the inference layer > Frontier models at up to 80% lower cost: > GLM 5.2, Kimi K3, Kimi K2.7 Code, DeepSeek V4 Pro and V4 Flash, plus Qwen3.6 27B, Gemma 4 31B and gpt-oss-120b. > OpenAI-compatible endpoint, so it drops into any agent or IDE you already run. • where the discount comes from > They route across AMD, NVIDIA and Tenstorrent, sending each workload to the silicon that suits it. > Everyone else tunes on one kind of chip, so the saving is architectural rather than promotional. Claiming it -> Credits in the top right, pick $50, apply "UNITABETAI" at checkout. The code has been live since their June launch, so run it on your own account before you plan around it ↓

$50 IN FREE CREDITS FOR GLM 5.2, KIMI K3 AND DEEPSEEK V4 PRO, ON A CODE THE COMPANY PUBLISHED ITSELF • ai&, the inference layer > Frontier models at up to 80% lower cost: > GLM 5.2, Kimi K3, Kimi K2.7 Code, DeepSeek V4 Pro and V4 Flash, plus Qwen3.6 27B, Gemma 4 31B and gpt-oss-120b. > OpenAI-compatible endpoint, so it drops into any agent or IDE you already run. • where the discount comes from > They route across AMD, NVIDIA and Tenstorrent, sending each workload to the silicon that suits it. > Everyone else tunes on one kind of chip, so the saving is architectural rather than promotional. Claiming it -> Credits in the top right, pick $50, apply "UNITABETAI" at checkout. The code has been live since their June launch, so run it on your own account before you plan around it ↓

slash1s

15,613 просмотров • 3 дней назад

Timelapse #156 (36 hrs) - Worked with the tiny corp on getting GLM 5.2 running on 8xMI300X (sglang won here) - Launched KernelBench-Mega and updated Kernelbench-Hard with h100 and b200 sweeps - Took care of boring business stuff - Did some training sweeps for specialized technical vocab audio model - Some bugs with putting kernelbench-mega and hard on cloud instances so had to do some reruns. Learned a lot though - Setting up my own local rl infra and profiling concurrency 128 rollouts with vllm. Became clear to me that I need to serve in nvfp4, use MoE only for throughput, reap so training doesn’t OOM, dig into the vllm kernel graph itself to not underutilize my hardware from poor flashinfer/cutlass selections for my rtx pro 6000 sm120 architecture - Might do online distillation from glm 5.2 but for now taking it one step at a time - Slept for a bit then woke up and showered - Fixed an issue with SGLang tensor parallel deadlock on GLM 5.2 architecture with MTP enabled - GLM 5.2 inference is 2-3x faster than coding plans and running on amd boxes - Spent time with family - Hung out with some friends - Recorded some yoctogpt lectures with the revamped notebook (high taste btw) - Setting up dflash training for GLM 5.2

Timelapse #156 (36 hrs) - Worked with the tiny corp on getting GLM 5.2 running on 8xMI300X (sglang won here) - Launched KernelBench-Mega and updated Kernelbench-Hard with h100 and b200 sweeps - Took care of boring business stuff - Did some training sweeps for specialized technical vocab audio model - Some bugs with putting kernelbench-mega and hard on cloud instances so had to do some reruns. Learned a lot though - Setting up my own local rl infra and profiling concurrency 128 rollouts with vllm. Became clear to me that I need to serve in nvfp4, use MoE only for throughput, reap so training doesn’t OOM, dig into the vllm kernel graph itself to not underutilize my hardware from poor flashinfer/cutlass selections for my rtx pro 6000 sm120 architecture - Might do online distillation from glm 5.2 but for now taking it one step at a time - Slept for a bit then woke up and showered - Fixed an issue with SGLang tensor parallel deadlock on GLM 5.2 architecture with MTP enabled - GLM 5.2 inference is 2-3x faster than coding plans and running on amd boxes - Spent time with family - Hung out with some friends - Recorded some yoctogpt lectures with the revamped notebook (high taste btw) - Setting up dflash training for GLM 5.2

Elliot Arledge

45,447 просмотров • 1 месяц назад

GLM 5.2 just killed Claude Fable 5. Watch 6:36 first if you only have a minute. That’s where it stops being a tech demo and starts being a game. One small sentence. Two prompts. The AI built Minecraft from scratch. Not a clone. Not a demo. A working game: - inventory + crafting table - ore mining: coal, iron, gold - underground caves - mobs: zombies, sheep, chickens, cows, pigs - day/night cycle - fall damage - locked 60 FPS The numbers are insane: Notch built the original Minecraft solo over months. Microsoft bought it for $2.5B in 2014. GLM 5.2 shipped a working copy in the time it took the creator to grab a coffee. The science is wild too: The model iteratively builds the systems itself. Inventory first. Then crafting. Then mining. Then mobs. No extra instructions in between. The grift opportunity is even wilder: Indie studios charge $50k+ for a survival game prototype. Unity freelancers sit at $80/hr. Now one kid + one prompt + one evening = a shippable MVP. Full timestamps: 0:25 - Minecraft in ONE prompt 6:36 - Minecraft in TWO prompts (caves, mobs, crafting) 9:40 - the verdict: GLM 5.2 or Claude Fable 5? Claude Fable 5 still holds up. But GLM added caves and hostile mobs nobody asked for. Save this before your knowledge cutoff stops mattering.

GLM 5.2 just killed Claude Fable 5. Watch 6:36 first if you only have a minute. That’s where it stops being a tech demo and starts being a game. One small sentence. Two prompts. The AI built Minecraft from scratch. Not a clone. Not a demo. A working game: - inventory + crafting table - ore mining: coal, iron, gold - underground caves - mobs: zombies, sheep, chickens, cows, pigs - day/night cycle - fall damage - locked 60 FPS The numbers are insane: Notch built the original Minecraft solo over months. Microsoft bought it for $2.5B in 2014. GLM 5.2 shipped a working copy in the time it took the creator to grab a coffee. The science is wild too: The model iteratively builds the systems itself. Inventory first. Then crafting. Then mining. Then mobs. No extra instructions in between. The grift opportunity is even wilder: Indie studios charge $50k+ for a survival game prototype. Unity freelancers sit at $80/hr. Now one kid + one prompt + one evening = a shippable MVP. Full timestamps: 0:25 - Minecraft in ONE prompt 6:36 - Minecraft in TWO prompts (caves, mobs, crafting) 9:40 - the verdict: GLM 5.2 or Claude Fable 5? Claude Fable 5 still holds up. But GLM added caves and hostile mobs nobody asked for. Save this before your knowledge cutoff stops mattering.

AdiiX

144,802 просмотров • 1 месяц назад

Open weight Kimi K3 crushed cloud frontier GPT 5.6 at 3D destruction physics! Moonshot AI became the first lab to open-source frontier model weights. We tested them on 8x B300 We gave four models the same task: build three self-contained HTML scenes with real physics Prompts: – A monster truck crushing a row of cars – Two cars jumping a canyon and colliding head-on mid-air – A giant anvil drop test flattening cars one by one Outputs: – Kimi K3 (local): 32.7K tokens, $0 – GPT 5.6: 19.2K tokens, $0.30 – Grok 4.5: 55.2K tokens, $0.45 – GLM 5.2: 66.8K tokens, $0.15 Kimi K3 made the most realistic scenes and won all three against every other model. Its truck rides over the cars and crushes them one by one, and the wrecks stay crushed. The canyon scene shows the gap best: Kimi's cars smash into each other and fall into the gorge, GPT's cars fly past the crash and land with no damage. Grok 4.5 and GLM 5.2 surprised us with the collision animation in the second scene: the impact moment is the most epic of the whole run, bumpers fold, wheels fly off, debris flies everywhere. Their other scenes are nowhere near that level

Open weight Kimi K3 crushed cloud frontier GPT 5.6 at 3D destruction physics! Moonshot AI became the first lab to open-source frontier model weights. We tested them on 8x B300 We gave four models the same task: build three self-contained HTML scenes with real physics Prompts: – A monster truck crushing a row of cars – Two cars jumping a canyon and colliding head-on mid-air – A giant anvil drop test flattening cars one by one Outputs: – Kimi K3 (local): 32.7K tokens, $0 – GPT 5.6: 19.2K tokens, $0.30 – Grok 4.5: 55.2K tokens, $0.45 – GLM 5.2: 66.8K tokens, $0.15 Kimi K3 made the most realistic scenes and won all three against every other model. Its truck rides over the cars and crushes them one by one, and the wrecks stay crushed. The canyon scene shows the gap best: Kimi's cars smash into each other and fall into the gorge, GPT's cars fly past the crash and land with no damage. Grok 4.5 and GLM 5.2 surprised us with the collision animation in the second scene: the impact moment is the most epic of the whole run, bumpers fold, wheels fly off, debris flies everywhere. Their other scenes are nowhere near that level

atomic.chat

146,160 просмотров • 16 дней назад

🚨 Deepseek V4 pro GA is being tested to few users > this is the output on the same prompt that was used for kimi k3 and GPT sol 5.6 in the quoted video > The details are solid , addition of tents , campfire and everything this model looks promising

🚨 Deepseek V4 pro GA is being tested to few users > this is the output on the same prompt that was used for kimi k3 and GPT sol 5.6 in the quoted video > The details are solid , addition of tents , campfire and everything this model looks promising

Chetaslua

172,573 просмотров • 25 дней назад

So which model is the best at frontend design? You can now eval pretty much all of them in pen(dot)dev in realtime in parallel. It's the most versatile design agent, where you can Sign in with ChatGPT, use your Claude subscription, OpenCode Go, Github CoPilot, connect Cursor Composer 2.5/Grok 4.5, Minimax M3, Kimi K2.7/K3, GLM 5.2, Gemini 3.5 Flash, Qwen 3.7 Plus etc, mix and match these models on design tasks directly in your codebase locally on your desktop.

So which model is the best at frontend design? You can now eval pretty much all of them in pen(dot)dev in realtime in parallel. It's the most versatile design agent, where you can Sign in with ChatGPT, use your Claude subscription, OpenCode Go, Github CoPilot, connect Cursor Composer 2.5/Grok 4.5, Minimax M3, Kimi K2.7/K3, GLM 5.2, Gemini 3.5 Flash, Qwen 3.7 Plus etc, mix and match these models on design tasks directly in your codebase locally on your desktop.

Tom Krcha

2,454,764 просмотров • 23 дней назад