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GLM 5.3 Flash vs Kimi K3 tested both models on same prompt at highest reasoning available but results came out really different > 5.3 flash took 20 minutes to complete this and costed $0.7 via AIhubMix API > kimi k3 took 30 minutes to complete this and costed $4.32...

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J A Z I I

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23,105 просмотров • 3 дней назад •via X (Twitter)

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4 AI models trying to draw their self-portraits. GLM-5.3-Flash, GLM-5.3, Kimi K3, and Claude Opus 5. GLM-5.3-Flash came out looking the most handsome 🤓
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4 AI models trying to draw their self-portraits. GLM-5.3-Flash, GLM-5.3, Kimi K3, and Claude Opus 5. GLM-5.3-Flash came out looking the most handsome 🤓

TypingMind

53,843 просмотров • 1 день назад

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 you think won?
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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 you think won?

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63,741 просмотров • 2 месяцев назад

you can now use Ox Alpha, Kimi K3, gemini 3.7 flash and 47 other models for FREEE with no credit card 😳 aihubmix just opened a 50-model free catalog, every model $0/M in and out. the models: -Ox Aplha -gemini-3.7-flash -glm-5.2 -kimi-k3 -minimax-m3 how to set it up (2 min): step 1: sign up at email or oauth, no card step 2: create an api key one key, all models, free and paid alike step 3: call any -free model base url: model: coding-glm-5.2-free, gpt-5.5-free, whatever that's it the free catalog is live in 2 minutes works in any openai-compatible tool. save this if you want a simple multi model setup later
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you can now use Ox Alpha, Kimi K3, gemini 3.7 flash and 47 other models for FREEE with no credit card 😳 aihubmix just opened a 50-model free catalog, every model $0/M in and out. the models: -Ox Aplha -gemini-3.7-flash -glm-5.2 -kimi-k3 -minimax-m3 how to set it up (2 min): step 1: sign up at email or oauth, no card step 2: create an api key one key, all models, free and paid alike step 3: call any -free model base url: model: coding-glm-5.2-free, gpt-5.5-free, whatever that's it the free catalog is live in 2 minutes works in any openai-compatible tool. save this if you want a simple multi model setup later

painn

76,972 просмотров • 6 дней назад

One of the first Kimi K3 outputs for y'all, tried it on frontend 👀 First impressions is that it's VERY slow, even slower than Fable. This took 35 minutes to finish. However, this is one of the best outputs I've ever seen from this prompt, better than many frontier models.
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One of the first Kimi K3 outputs for y'all, tried it on frontend 👀 First impressions is that it's VERY slow, even slower than Fable. This took 35 minutes to finish. However, this is one of the best outputs I've ever seen from this prompt, better than many frontier models.

Lentils

104,085 просмотров • 1 месяц назад

Just tested Kimi.ai K3 against GPT-5.6 and Grok 4.5 on the same task: build a Subway Surfers game. Kimi K3 was my favorite. Better UI, smoother gameplay, and the overall experience felt more polished. GPT-5.6 was solid too, while Grok 4.5 fell behind on the UI. The interesting part is that all three needed at least one follow-up prompt. Kimi also took a bit longer to generate, but the extra time paid off. Ran Kimi K3 through Nebius Token Factory. Low inference cost + a really capable open source model is a great combo.
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Just tested Kimi.ai K3 against GPT-5.6 and Grok 4.5 on the same task: build a Subway Surfers game. Kimi K3 was my favorite. Better UI, smoother gameplay, and the overall experience felt more polished. GPT-5.6 was solid too, while Grok 4.5 fell behind on the UI. The interesting part is that all three needed at least one follow-up prompt. Kimi also took a bit longer to generate, but the extra time paid off. Ran Kimi K3 through Nebius Token Factory. Low inference cost + a really capable open source model is a great combo.

Arindam Majumder 𝕏

46,107 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?
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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

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?
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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

KIMI K3 is here, and it blew my mind 🤯 yes, it is expensive and very slow Took over 30 minutes, and blew an ENTIRE $20 kimi sub before it even finished the one prompt for this game. BUT OMG... the output geniuinly has fable 5 level magic built into it, this model falls between gpt 5.6 and fable 5 imo. you can play the game at kimisurfers (dot) com right now, and see what I mean. This model is too expensive to replace fable 5, BUTT it shows that the chinese open source labs are CATCHING anthropic and open ai FASTTT.
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KIMI K3 is here, and it blew my mind 🤯 yes, it is expensive and very slow Took over 30 minutes, and blew an ENTIRE $20 kimi sub before it even finished the one prompt for this game. BUT OMG... the output geniuinly has fable 5 level magic built into it, this model falls between gpt 5.6 and fable 5 imo. you can play the game at kimisurfers (dot) com right now, and see what I mean. This model is too expensive to replace fable 5, BUTT it shows that the chinese open source labs are CATCHING anthropic and open ai FASTTT.

Max Blade

128,924 просмотров • 1 месяц назад

DeepSeek v4 Flash (0731) vs Kimi K3 (10x more weights) i gave both the exact same prompt, in the exact same harness: one-shot a luxury product landing page ($10,000 "ARC-01" concept object). results in the video. my take: Kimi K3: wins on polish. Better typography hierarchy, more confident use of negative space, and the hero product shot integration is genuinely beautiful DeepSeek v4 Flash: very close. The structure is right, the copy is right, the layout logic is right. It loses on finish, not on fundamentals. a 10x smaller model getting ~90% of the way there. a year ago this gap would have been embarrassing. mind you it cost less than $0.10 for 6 build iterations and 8 QA passes. small models aren't "good for their size" anymore. they're just good.
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DeepSeek v4 Flash (0731) vs Kimi K3 (10x more weights) i gave both the exact same prompt, in the exact same harness: one-shot a luxury product landing page ($10,000 "ARC-01" concept object). results in the video. my take: Kimi K3: wins on polish. Better typography hierarchy, more confident use of negative space, and the hero product shot integration is genuinely beautiful DeepSeek v4 Flash: very close. The structure is right, the copy is right, the layout logic is right. It loses on finish, not on fundamentals. a 10x smaller model getting ~90% of the way there. a year ago this gap would have been embarrassing. mind you it cost less than $0.10 for 6 build iterations and 8 QA passes. small models aren't "good for their size" anymore. they're just good.

filipe

26,175 просмотров • 29 дней назад

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?
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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?

J A Z I I

48,124 просмотров • 2 месяцев назад

Kimi K3 just outperformed Claude Fable 5 at a quarter of the price. Kimi K3 $3.11 Claude Fable 5 $12.23 Same prompt: a one-shot MECCHA CHAMELEON, Steam hide-and-seek game. A white chameleon hides in a hand-drawn room, paints itself with the mouse to match the wall behind it, then survives three sweeps of a robot seeker. We asked for a live pixel-diff match %, five procedural zones, synthesized sound, three scored rounds — one HTML file, no libraries. Both models live on AI/ML API
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Kimi K3 just outperformed Claude Fable 5 at a quarter of the price. Kimi K3 $3.11 Claude Fable 5 $12.23 Same prompt: a one-shot MECCHA CHAMELEON, Steam hide-and-seek game. A white chameleon hides in a hand-drawn room, paints itself with the mouse to match the wall behind it, then survives three sweeps of a robot seeker. We asked for a live pixel-diff match %, five procedural zones, synthesized sound, three scored rounds — one HTML file, no libraries. Both models live on AI/ML API

AI/ML API

63,586 просмотров • 1 месяц 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 ↓
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$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

27,159 просмотров • 20 дней назад

My honest take on Kimi.ai K3 👀 As soon as I got access to Kimi, I put it through the exact same 3D football stadium challenge I had previously given Claude Fable 5. Fable 5 finished in under an hour. Kimi took almost 3 hours 🥵 At first, that was a very bad impression but i decided to see what's taking it this much of time. It was running E2E tests, validating desktop, tablet, and mobile, taking screenshots, finding failures, fixing them, even adapting for low end hardware and low FPS before moving on. All these that Fable 5 never did. None of it because it wasn't able to run this WebGL app on a headless browser. Kimi also shipped a clean React + Three.js codebase with proper components while Fable, as you might have checked codebase, generated one big HTML file with everything stuffed in. With all of this happening, it felt like I was doing more "Vibe Coding" with Fable 5 and more "Vibe Engineering" with Kimi K3. It feels like it's spending more time making sure the code actually works and easily scalable. Really impressed so far. More Kimi K3 experiments coming soon. Code (Kimi): Code (Fable): Live (Kimi hosted):
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My honest take on Kimi.ai K3 👀 As soon as I got access to Kimi, I put it through the exact same 3D football stadium challenge I had previously given Claude Fable 5. Fable 5 finished in under an hour. Kimi took almost 3 hours 🥵 At first, that was a very bad impression but i decided to see what's taking it this much of time. It was running E2E tests, validating desktop, tablet, and mobile, taking screenshots, finding failures, fixing them, even adapting for low end hardware and low FPS before moving on. All these that Fable 5 never did. None of it because it wasn't able to run this WebGL app on a headless browser. Kimi also shipped a clean React + Three.js codebase with proper components while Fable, as you might have checked codebase, generated one big HTML file with everything stuffed in. With all of this happening, it felt like I was doing more "Vibe Coding" with Fable 5 and more "Vibe Engineering" with Kimi K3. It feels like it's spending more time making sure the code actually works and easily scalable. Really impressed so far. More Kimi K3 experiments coming soon. Code (Kimi): Code (Fable): Live (Kimi hosted):

The Bugged Dev

578,562 просмотров • 1 месяц назад

Kimi K3 vs GPT 5.6 Sol (extra high) Voxel Death Star trench run I do want to point out that this was supposed to be a static world, but when I gave this prompt to Kimi K3, it said: “Since I can't output raster images, I've done something better: a real-time, fully animated voxel rendering of the assault in a single HTML file. —” So this ended up being an animated competition The way I would subjectively score it is that GPT 5.6 had a better lighting/effects but K3s was much smoother. You can definitely tell open source is getting really good.
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Kimi K3 vs GPT 5.6 Sol (extra high) Voxel Death Star trench run I do want to point out that this was supposed to be a static world, but when I gave this prompt to Kimi K3, it said: “Since I can't output raster images, I've done something better: a real-time, fully animated voxel rendering of the assault in a single HTML file. —” So this ended up being an animated competition The way I would subjectively score it is that GPT 5.6 had a better lighting/effects but K3s was much smoother. You can definitely tell open source is getting really good.

Chris

50,121 просмотров • 1 месяц назад

🚨 GLM 5.3 just dropped, and here’s how to access the GLM family completely FREE. no subscription. no API credit cost & no rate limit. step 1️⃣: download the OpenCode app on your PC. it’s available on both Mac and Windows. 🔗: step 2️⃣: go to NVIDIA’s website, sign up with your email, verify your account, and generate a unified API key for free. NVIDIA offers free model endpoints for developers, and GLM 5.2 is one of the top models currently available there. 🔗: step 3️⃣: launch OpenCode, open the model selection menu, search for NVIDIA, and paste the API key you generated. also, there’s no rate limit, but traffic to the model through NVIDIA can get busy at times, so responses may occasionally be slower or temporarily unavailable. the only catch: NVIDIA hasn’t listed GLM 5.3 yet, so for now you’ll be using GLM 5.2. once GLM 5.3 gets added to NVIDIA’s model catalog, you should be able to switch to it the same way.
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🚨 GLM 5.3 just dropped, and here’s how to access the GLM family completely FREE. no subscription. no API credit cost & no rate limit. step 1️⃣: download the OpenCode app on your PC. it’s available on both Mac and Windows. 🔗: step 2️⃣: go to NVIDIA’s website, sign up with your email, verify your account, and generate a unified API key for free. NVIDIA offers free model endpoints for developers, and GLM 5.2 is one of the top models currently available there. 🔗: step 3️⃣: launch OpenCode, open the model selection menu, search for NVIDIA, and paste the API key you generated. also, there’s no rate limit, but traffic to the model through NVIDIA can get busy at times, so responses may occasionally be slower or temporarily unavailable. the only catch: NVIDIA hasn’t listed GLM 5.3 yet, so for now you’ll be using GLM 5.2. once GLM 5.3 gets added to NVIDIA’s model catalog, you should be able to switch to it the same way.

m0h

78,449 просмотров • 15 дней назад

Tested the exact same prompt on Seedance 2.0 vs. Omni Flash, and the results are wild. 🤯 Both have their weird little AI mutations (we all know the struggle lol), but the biggest shocker? Omni Flash generated this instantly with zero censorship, while Seedance 2.0 threw a fit and tried to ban the prompt. 💀 Dropping the exact prompt in the thread below 👇 Next up, I’m gonna push them harder with some crazy dynamic animation. Which model do you think won this round? tbh I’m torn.
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Tested the exact same prompt on Seedance 2.0 vs. Omni Flash, and the results are wild. 🤯 Both have their weird little AI mutations (we all know the struggle lol), but the biggest shocker? Omni Flash generated this instantly with zero censorship, while Seedance 2.0 threw a fit and tried to ban the prompt. 💀 Dropping the exact prompt in the thread below 👇 Next up, I’m gonna push them harder with some crazy dynamic animation. Which model do you think won this round? tbh I’m torn.

Ivanna | AI Art & Prompts

38,463 просмотров • 2 месяцев назад

So what's your feeling on DeepSeek V4 Flash 0731? Surely great model for Local AI and coding but clearly not at GLM 5.2 or Kimi K3 level. Obviously, because it's a different category. For Frogger I tried 3 times using omp, but I always get a game too complex to play or with small issues here and there. I've used the mxfp4 version. But it worked great with web search, html and CSS issues with custom plugins on TMRNL X with Hermes Agent.
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So what's your feeling on DeepSeek V4 Flash 0731? Surely great model for Local AI and coding but clearly not at GLM 5.2 or Kimi K3 level. Obviously, because it's a different category. For Frogger I tried 3 times using omp, but I always get a game too complex to play or with small issues here and there. I've used the mxfp4 version. But it worked great with web search, html and CSS issues with custom plugins on TMRNL X with Hermes Agent.

Ivan Fioravanti ᯅ

46,353 просмотров • 28 дней назад

Blender tutorials are about to age very badly. In this demo, Kimi K3 gets two character references and a plain-English request. Minutes later, it builds the first version inside Blender. The user asks for a better horn, Kimi edits the same scene, and the character starts moving. This is not another AI tool generating one lucky image. It is working with an actual editable 3D project, then changing the geometry after feedback instead of restarting from zero. It still needs human direction, because the first result is rarely the good one. But blockouts, repetitive edits, Blender Python, and basic iteration are becoming a conversation rather than hours of clicking through menus. Kimi K3 + Blender MCP is basically turning 3D modeling into: describe it, inspect it, complain about it, and let the AI fix it.
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Blender tutorials are about to age very badly. In this demo, Kimi K3 gets two character references and a plain-English request. Minutes later, it builds the first version inside Blender. The user asks for a better horn, Kimi edits the same scene, and the character starts moving. This is not another AI tool generating one lucky image. It is working with an actual editable 3D project, then changing the geometry after feedback instead of restarting from zero. It still needs human direction, because the first result is rarely the good one. But blockouts, repetitive edits, Blender Python, and basic iteration are becoming a conversation rather than hours of clicking through menus. Kimi K3 + Blender MCP is basically turning 3D modeling into: describe it, inspect it, complain about it, and let the AI fix it.

Rina

19,092 просмотров • 1 месяц назад

one model built ten webpages in 160 seconds. the next one took 623. same prompt to three models at once: ten standalone HTML pages, one per footballer, shared template and CSS, each self-contained. Ling-3.0-flash: 160s Step 3.7 Flash: 319s MiniMax M2.7: 623s first reasoning token came back in 84ms. the other two took 378ms and 8.7s. Ant Ling shipped it at 124B total, 5.1B active per token. one eighth the parameters of their own 1T flagship, and it matches or beats it on most benchmarks they published. the whole race takes 20 seconds to watch.
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one model built ten webpages in 160 seconds. the next one took 623. same prompt to three models at once: ten standalone HTML pages, one per footballer, shared template and CSS, each self-contained. Ling-3.0-flash: 160s Step 3.7 Flash: 319s MiniMax M2.7: 623s first reasoning token came back in 84ms. the other two took 378ms and 8.7s. Ant Ling shipped it at 124B total, 5.1B active per token. one eighth the parameters of their own 1T flagship, and it matches or beats it on most benchmarks they published. the whole race takes 20 seconds to watch.

Hanako

34,332 просмотров • 29 дней назад

qwen 3.8 max vs deepseek v4 flash 0731 vs kimi k3 vs gpt 5.6 sol – on rubik's cube and chess four frontier models built a rubik's cube stand and solved it, then built a chess board and played claude opus 5 on it the setup: Nous Research's hermes agent cli on OpenRouter tasks: 1. cube – build a 3d rubik's cube with a cli and a Three.js viewer, then solve an identical scrambled position on your own stand 2. chess – build a 3d chess stand, then play white against claude opus 5 as black, live, one move at a time. no engine, no solver, no opening book on either side. stockfish depth 14 grades every chess ply afterwards; neither player sees the score models: DeepSeek v4 flash 0731, OpenAI gpt-5.6 sol, Kimi.ai kimi k3, Qwen qwen 3.8 max gpt-5.6 sol and deepseek v4 flash solved their cubes – sol in 24 moves and seventeen seconds, deepseek in 32. qwen and kimi never got there, giving up at 96 and 207 moves then all four built chess stands and played white against claude opus 5 on them, and all four resigned: deepseek on move 13, sol on 19, kimi on 21, qwen holding out longest at 29 - build time, both stands #1 gpt-5.6 sol – 16m 43s #2 deepseek v4 flash – 97m 39s #3 kimi k3 – 166m 09s #4 qwen 3.8 max – 215m 08s - build attempts before a working stand #1 gpt-5.6 sol – 3 #2 qwen 3.8 max – 4 #3 kimi k3 – 4 #4 deepseek v4 flash – 5 - total tokens #1 gpt-5.6 sol – 6,713,754 #2 qwen 3.8 max – 17,272,507 #3 kimi k3 – 22,427,504 #4 deepseek v4 flash – 27,417,442 - total price #1 deepseek v4 flash – $0.557 #2 gpt-5.6 sol – $6.319 #3 qwen 3.8 max – $10.270 #4 kimi k3 – $16.667 observations: • deepseek v4 flash is the cheapest model here by a margin nobody else is near, and it got there while being the least efficient of the four. it burned 27.4m tokens – more than anyone, 5m more than kimi – and still finished both benchmarks for $0.557. that is $0.02 per million tokens against kimi's $0.74. it also needed the most passes to produce working stands, five, and that did not matter: all five deepseek passes together cost a thirtieth of kimi's two • so what deepseek cannot do is get it right the first time. what it can do is get it right the fifth time, for half a dollar. that is a different thing to be buying – not a good first draft, but the option to keep asking • gpt-5.6 sol is the opposite profile and the strongest of the four on pure efficiency. 16m 43s to build both stands, 6.7m tokens, three passes – under 40% of the next lowest token count and a quarter of deepseek's, on an eighth of qwen's clock. it also solved the cube fastest of anyone, 24 moves in seventeen seconds. sol is what you reach for when you want the answer now and can absorb $0.94 per million • sol's weakness is in what it does not check. its chess viewer deleted the capturing piece instead of the captured one, so pieces disappeared off the board mid-game – a defect the fifty-cent deepseek stand did not have. fast and terse turns out to be the same dial as fast and unverified • qwen 3.8 max is not the cheap open-weights option it gets treated as. $10.270 across the two benchmarks, second most expensive of the four, 18x deepseek, and by a distance the slowest – 215 minutes of build time, nearly thirteen times sol's. what the money buys is judgment: it played eighteen moves without a single error worth a hundredth of a pawn, then made exactly one bad move in the whole game, and averaged 44.6 centipawns lost across the longest game any of the four managed. it also could not solve a rubik's cube in 96 tries • kimi k3 is the one line with no reading that flatters it. most expensive at $16.667, last on the cube at 207 moves, last at chess at 478 centipawns lost per move. it is also the model that verified hardest – on the cube it wrote its own integrity check instead of trusting its output. that makes the result worse rather than better: the checking was real, and the reasoning underneath it still was not follow thehype. for 24/7 ai news, analysis and breakdowns
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qwen 3.8 max vs deepseek v4 flash 0731 vs kimi k3 vs gpt 5.6 sol – on rubik's cube and chess four frontier models built a rubik's cube stand and solved it, then built a chess board and played claude opus 5 on it the setup: Nous Research's hermes agent cli on OpenRouter tasks: 1. cube – build a 3d rubik's cube with a cli and a Three.js viewer, then solve an identical scrambled position on your own stand 2. chess – build a 3d chess stand, then play white against claude opus 5 as black, live, one move at a time. no engine, no solver, no opening book on either side. stockfish depth 14 grades every chess ply afterwards; neither player sees the score models: DeepSeek v4 flash 0731, OpenAI gpt-5.6 sol, Kimi.ai kimi k3, Qwen qwen 3.8 max gpt-5.6 sol and deepseek v4 flash solved their cubes – sol in 24 moves and seventeen seconds, deepseek in 32. qwen and kimi never got there, giving up at 96 and 207 moves then all four built chess stands and played white against claude opus 5 on them, and all four resigned: deepseek on move 13, sol on 19, kimi on 21, qwen holding out longest at 29 - build time, both stands #1 gpt-5.6 sol – 16m 43s #2 deepseek v4 flash – 97m 39s #3 kimi k3 – 166m 09s #4 qwen 3.8 max – 215m 08s - build attempts before a working stand #1 gpt-5.6 sol – 3 #2 qwen 3.8 max – 4 #3 kimi k3 – 4 #4 deepseek v4 flash – 5 - total tokens #1 gpt-5.6 sol – 6,713,754 #2 qwen 3.8 max – 17,272,507 #3 kimi k3 – 22,427,504 #4 deepseek v4 flash – 27,417,442 - total price #1 deepseek v4 flash – $0.557 #2 gpt-5.6 sol – $6.319 #3 qwen 3.8 max – $10.270 #4 kimi k3 – $16.667 observations: • deepseek v4 flash is the cheapest model here by a margin nobody else is near, and it got there while being the least efficient of the four. it burned 27.4m tokens – more than anyone, 5m more than kimi – and still finished both benchmarks for $0.557. that is $0.02 per million tokens against kimi's $0.74. it also needed the most passes to produce working stands, five, and that did not matter: all five deepseek passes together cost a thirtieth of kimi's two • so what deepseek cannot do is get it right the first time. what it can do is get it right the fifth time, for half a dollar. that is a different thing to be buying – not a good first draft, but the option to keep asking • gpt-5.6 sol is the opposite profile and the strongest of the four on pure efficiency. 16m 43s to build both stands, 6.7m tokens, three passes – under 40% of the next lowest token count and a quarter of deepseek's, on an eighth of qwen's clock. it also solved the cube fastest of anyone, 24 moves in seventeen seconds. sol is what you reach for when you want the answer now and can absorb $0.94 per million • sol's weakness is in what it does not check. its chess viewer deleted the capturing piece instead of the captured one, so pieces disappeared off the board mid-game – a defect the fifty-cent deepseek stand did not have. fast and terse turns out to be the same dial as fast and unverified • qwen 3.8 max is not the cheap open-weights option it gets treated as. $10.270 across the two benchmarks, second most expensive of the four, 18x deepseek, and by a distance the slowest – 215 minutes of build time, nearly thirteen times sol's. what the money buys is judgment: it played eighteen moves without a single error worth a hundredth of a pawn, then made exactly one bad move in the whole game, and averaged 44.6 centipawns lost across the longest game any of the four managed. it also could not solve a rubik's cube in 96 tries • kimi k3 is the one line with no reading that flatters it. most expensive at $16.667, last on the cube at 207 moves, last at chess at 478 centipawns lost per move. it is also the model that verified hardest – on the cube it wrote its own integrity check instead of trusting its output. that makes the result worse rather than better: the checking was real, and the reasoning underneath it still was not follow thehype. for 24/7 ai news, analysis and breakdowns

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