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Ran 10 more tests comparing GLM 5.2 & Opus. On average, GLM 5.2 produced 2x the tokens but was still faster + 3x cheaper with similar quality! I'm open sourcing all these tests tomorrow, including the code, my prompts, and the token/cost stats.

Hassan

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67,027 次观看 • 2 个月前 •via X (Twitter)

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Announcing GLM Arena! A series of tests (infographics, svgs, sites, ect..) ran on GLM 5.2 and Opus 4.8, with prompts included. On average, GLM 5.2 produced 2x the tokens but was still faster + 3x cheaper with similar quality.

Announcing GLM Arena! A series of tests (infographics, svgs, sites, ect..) ran on GLM 5.2 and Opus 4.8, with prompts included. On average, GLM 5.2 produced 2x the tokens but was still faster + 3x cheaper with similar quality.

Hassan

75,421 次观看 • 2 个月前

This model is insane at design. I asked GLM 5.2 (left) and Opus 4.8 (right) to build me a landing page and you can't even tell the difference. GLM cost $0.06 while opus cost $0.49. More than 6x cheaper while being faster + more token efficient. Another win for open source AI.

This model is insane at design. I asked GLM 5.2 (left) and Opus 4.8 (right) to build me a landing page and you can't even tell the difference. GLM cost $0.06 while opus cost $0.49. More than 6x cheaper while being faster + more token efficient. Another win for open source AI.

Hassan

1,580,732 次观看 • 2 个月前

We ran a test between the new DeepSeek V4 Flash, Kimi K3 and GLM 5.2. 3 open models. 1 prompt. /design command Reviewed gameplay features, UX/UI and cost. 🔹 Kimi K3 → 9.5/10 · $0.0740 🔹 GLM 5.2 → 9/10 · $0.0480 🔹 DeepSeek V4 Flash → 7/10 · $0.0005 DeepSeek V4 Flash is ~150x cheaper, but rough around edges. Kimi K3 and GLM 5.2 built the most polished gaming experience.

We ran a test between the new DeepSeek V4 Flash, Kimi K3 and GLM 5.2. 3 open models. 1 prompt. /design command Reviewed gameplay features, UX/UI and cost. 🔹 Kimi K3 → 9.5/10 · $0.0740 🔹 GLM 5.2 → 9/10 · $0.0480 🔹 DeepSeek V4 Flash → 7/10 · $0.0005 DeepSeek V4 Flash is ~150x cheaper, but rough around edges. Kimi K3 and GLM 5.2 built the most polished gaming experience.

Command Code

167,504 次观看 • 1 个月前

Sakana Fugu surprisingly performed near GLM 5.2 level but 17× more expensive! We gave the same prompt to 4 models: build a complete live Trader Desk with both frontend and backend components, real-time market data fetched from external APIs for 8 symbols, and a custom dark-theme UI. Outputs: Fugu Ultra — 22,225 t, $0.51 Opus 4.8 — 15,802 t, $0.31 GPT-5.5 — 11,474 t, $0.26 GLM 5.2 — 13,677 t, $0.03 Fugu created the most polished and feature-rich trading desk in the run. GLM 5.2 was very close behind, with a similarly complete multi-panel interface and live data, but at a much lower cost. Opus and GPT also performed well, delivering solid results with a better balance between quality and cost

Sakana Fugu surprisingly performed near GLM 5.2 level but 17× more expensive! We gave the same prompt to 4 models: build a complete live Trader Desk with both frontend and backend components, real-time market data fetched from external APIs for 8 symbols, and a custom dark-theme UI. Outputs: Fugu Ultra — 22,225 t, $0.51 Opus 4.8 — 15,802 t, $0.31 GPT-5.5 — 11,474 t, $0.26 GLM 5.2 — 13,677 t, $0.03 Fugu created the most polished and feature-rich trading desk in the run. GLM 5.2 was very close behind, with a similarly complete multi-panel interface and live data, but at a much lower cost. Opus and GPT also performed well, delivering solid results with a better balance between quality and cost

atomic.chat

744,642 次观看 • 2 个月前

New Hunyuan Hy3 hits Gemini 3.5 quality on physics for 35x cheaper! We gave 4 models the same prompt: build three self-contained HTML5 canvas scenes with real physics demos Prompts: - A bowling ball knocking down the pins - An air hockey rally that ends in a goal - A pool break scattering the rack Outputs: Hunyuan Hy3: 29,757 tokens, $0.006 Gemini 3.5: 23,300 tokens, $0.21 GLM-5.2: 25,454 tokens, $0.07 DeepSeek-V4: 50,600 tokens, $0.009 Tencent's Hy3 matched Gemini across all three: clean collisions, the puck bounced true, the pins scattered like a real strike, the rack broke with real momentum, nothing clipped or floated. GLM is genuinely strong on pure coding tasks, but the moment the job steps outside clean code it gives way. DeepSeek was the letdown, it burned the most tokens of anyone (50k, almost 2x Hy3) and still turned in the weakest scenes

New Hunyuan Hy3 hits Gemini 3.5 quality on physics for 35x cheaper! We gave 4 models the same prompt: build three self-contained HTML5 canvas scenes with real physics demos Prompts: - A bowling ball knocking down the pins - An air hockey rally that ends in a goal - A pool break scattering the rack Outputs: Hunyuan Hy3: 29,757 tokens, $0.006 Gemini 3.5: 23,300 tokens, $0.21 GLM-5.2: 25,454 tokens, $0.07 DeepSeek-V4: 50,600 tokens, $0.009 Tencent's Hy3 matched Gemini across all three: clean collisions, the puck bounced true, the pins scattered like a real strike, the rack broke with real momentum, nothing clipped or floated. GLM is genuinely strong on pure coding tasks, but the moment the job steps outside clean code it gives way. DeepSeek was the letdown, it burned the most tokens of anyone (50k, almost 2x Hy3) and still turned in the weakest scenes

atomic.chat

111,823 次观看 • 2 个月前

Laguna S 2.1 performs at GLM-5.2 level on building popular games with 6x fewer params! We gave three local models the same task: build three popular arcade games that play themselves. Each game is one self-contained HTML file with a bot that plays it. Prompts: – Geometry Dash – Doodle Jump – Air Hockey Outputs: Laguna S 2.1: 10.3K tokens GLM-5.2: 26.4K tokens Hy3: 10.4K tokens Laguna held its quality against a 753B model. We think Laguna's Geometry Dash looked the best of the three, the cube clears every spike and block. GLM won Air Hockey. Its table looked the most detailed of all. Hy3 was the only model that added shooting to its Doodle Jump. But Laguna is the only model in our benchmark that runs on a MacBook with 128GB!

Laguna S 2.1 performs at GLM-5.2 level on building popular games with 6x fewer params! We gave three local models the same task: build three popular arcade games that play themselves. Each game is one self-contained HTML file with a bot that plays it. Prompts: – Geometry Dash – Doodle Jump – Air Hockey Outputs: Laguna S 2.1: 10.3K tokens GLM-5.2: 26.4K tokens Hy3: 10.4K tokens Laguna held its quality against a 753B model. We think Laguna's Geometry Dash looked the best of the three, the cube clears every spike and block. GLM won Air Hockey. Its table looked the most detailed of all. Hy3 was the only model that added shooting to its Doodle Jump. But Laguna is the only model in our benchmark that runs on a MacBook with 128GB!

atomic.chat

66,917 次观看 • 1 个月前

Open weights just caught up to the frontier. GLM-5.2 from Z.ai tops the open-model rankings on Artificial Analysis and Arena.ai's Agent Arena. It's now live on CoreWeave Serverless Inference at $1.39 in and $4.40 out per 1M tokens. Ship more for less.

Open weights just caught up to the frontier. GLM-5.2 from Z.ai tops the open-model rankings on Artificial Analysis and Arena.ai's Agent Arena. It's now live on CoreWeave Serverless Inference at $1.39 in and $4.40 out per 1M tokens. Ship more for less.

CoreWeave

16,637 次观看 • 2 个月前

Grok 4.5 performed GPT Sol level for free! We gave 4 models the same prompt: build three self-contained HTML5 canvas scenes with real physics demos Prompts: -robot deathmatch, Tombstone vs Minotaur -a hydraulic press flattening stuff on a conveyor -a semi truck jumping a canyon Outputs: GPT-5.6 Sol: 12.9K tokens, $0.51 (~7 min) Grok 4.5: 10.8K tokens, $0 (~5 min) Muse Spark 1.1: 26.8K tokens, $0.12 (~7.5 min) GLM 5.2: 10.9K tokens, $0.02 (~12 min) Grok 4.5 handled all three scenes genuinely well and got surprisingly close to GPT-5.6 this round. On top of that, it ran on the free tier. GPT-5.6 Sol, the frontier model, put out solid but not standout work. GLM 5.2 rendered all three scenes for pennies, but it came out the roughest of the four. Meta's new Muse Spark burned the most tokens yet still stayed cheap, delivering an average result.

Grok 4.5 performed GPT Sol level for free! We gave 4 models the same prompt: build three self-contained HTML5 canvas scenes with real physics demos Prompts: -robot deathmatch, Tombstone vs Minotaur -a hydraulic press flattening stuff on a conveyor -a semi truck jumping a canyon Outputs: GPT-5.6 Sol: 12.9K tokens, $0.51 (~7 min) Grok 4.5: 10.8K tokens, $0 (~5 min) Muse Spark 1.1: 26.8K tokens, $0.12 (~7.5 min) GLM 5.2: 10.9K tokens, $0.02 (~12 min) Grok 4.5 handled all three scenes genuinely well and got surprisingly close to GPT-5.6 this round. On top of that, it ran on the free tier. GPT-5.6 Sol, the frontier model, put out solid but not standout work. GLM 5.2 rendered all three scenes for pennies, but it came out the roughest of the four. Meta's new Muse Spark burned the most tokens yet still stayed cheap, delivering an average result.

atomic.chat

70,490 次观看 • 2 个月前

We benchmarked the top models on our own coding tasks. The results: - GPT 5.6 Sol (high) won on performance - Grok 4.6 (high) was the runner-up - GLM 5.3 Flash won on cost at comparable quality All 50%+ cheaper than our previous default (Opus 5)

We benchmarked the top models on our own coding tasks. The results: - GPT 5.6 Sol (high) won on performance - Grok 4.6 (high) was the runner-up - GLM 5.3 Flash won on cost at comparable quality All 50%+ cheaper than our previous default (Opus 5)

Zach Lloyd

20,904 次观看 • 4 天前

This free model just beat every closed-source AI on coding benchmarks. Open weights. 6x cheaper than Opus. The labs don't want you to know it exists. > GLM-5.2 from Zai just topped Code Arena - the first open-weights model to ever hold the #1 coding spot. Not a leaked weight, not a fine-tune. A fully open model beating GPT and Claude on their own turf. It's live on Hugging Face Inference API right now. 5 providers: Novita, Together AI, Fireworks, Deepinfra, Zai. OpenAI-compatible client. → Go to huggingface(.)co → grab HF_TOKEN from account settings → plug into any OpenAI-compatible client 6x cheaper than Opus. Companies bleeding on AI bills are already routing to this for orchestration, caching, and token optimization. The smart money moved before the headline dropped. > Now you know. huggingface(.)co/zai-org/GLM-5.2 Bookmark this before everyone else figures it out.

This free model just beat every closed-source AI on coding benchmarks. Open weights. 6x cheaper than Opus. The labs don't want you to know it exists. > GLM-5.2 from Zai just topped Code Arena - the first open-weights model to ever hold the #1 coding spot. Not a leaked weight, not a fine-tune. A fully open model beating GPT and Claude on their own turf. It's live on Hugging Face Inference API right now. 5 providers: Novita, Together AI, Fireworks, Deepinfra, Zai. OpenAI-compatible client. → Go to huggingface(.)co → grab HF_TOKEN from account settings → plug into any OpenAI-compatible client 6x cheaper than Opus. Companies bleeding on AI bills are already routing to this for orchestration, caching, and token optimization. The smart money moved before the headline dropped. > Now you know. huggingface(.)co/zai-org/GLM-5.2 Bookmark this before everyone else figures it out.

Atenov int.

12,395 次观看 • 2 个月前

Ox Alpha was GLM-5.3-Flash all along. Now it's FREE on three platforms. > B AI: 100% free API, no card. Opus 4.8-level coding at $0: chat.b{.}ai/chat > AIHubMix: free coding tier, same model: aihubmix{.}com/model/coding-glm-5.3-flash-free > Z ai: official GLM chat, free directly from the lab: chat.z{.}ai 320B total, 18B active. 1M context, multimodal. MIT open weights. Beats GLM-5.2 at one-tenth the price. Terminal Bench 2.1: 84.3. Approaches Opus 4.8 on coding. A stealth model that topped OpenRouter for a week. Now self-hostable for free.

Ox Alpha was GLM-5.3-Flash all along. Now it's FREE on three platforms. > B AI: 100% free API, no card. Opus 4.8-level coding at $0: chat.b{.}ai/chat > AIHubMix: free coding tier, same model: aihubmix{.}com/model/coding-glm-5.3-flash-free > Z ai: official GLM chat, free directly from the lab: chat.z{.}ai 320B total, 18B active. 1M context, multimodal. MIT open weights. Beats GLM-5.2 at one-tenth the price. Terminal Bench 2.1: 84.3. Approaches Opus 4.8 on coding. A stealth model that topped OpenRouter for a week. Now self-hostable for free.

kaize

22,169 次观看 • 13 天前

🔥 GLM-5.3-Flash Hits #1 on 🐮 GLM-5.3-Flash (Ox Alpha) is now the most-used and most popular model on with cumulative token throughput surpassing 2.41 trillion tokens. As the first native omni-modal model in the GLM-5 series, it packs 320B total parameters, with 18B active, and features a hybrid sparse and linear attention architecture with a 1M-token context window. The result: lightning-fast responses, powerful reasoning, and outstanding cost efficiency. 🎁 Still 100% FREE on From high-frequency API calls and coding to complex Agents and long-document processing, jump in and experience the #1 model on for yourself! 👉 Try it free now:

🔥 GLM-5.3-Flash Hits #1 on 🐮 GLM-5.3-Flash (Ox Alpha) is now the most-used and most popular model on with cumulative token throughput surpassing 2.41 trillion tokens. As the first native omni-modal model in the GLM-5 series, it packs 320B total parameters, with 18B active, and features a hybrid sparse and linear attention architecture with a 1M-token context window. The result: lightning-fast responses, powerful reasoning, and outstanding cost efficiency. 🎁 Still 100% FREE on From high-frequency API calls and coding to complex Agents and long-document processing, jump in and experience the #1 model on for yourself! 👉 Try it free now:

B.AI

198,538 次观看 • 5 天前

I finally got GLM-5.2 to work an entire 3-year patient chart that only Bonsai 27B was allowed to read 🔥 292 encounters live inside Bonsai on my Mac Studio. llama.cpp, Metal, ternary, 7.2GB, Apache-2.0. The chart never leaves the machine. GLM-5.2 can only ask questions. It asked three. Bonsai answered each in ~2s with 19,398 tokens still cached. Then it caught the thing buried 17 months back: metformin + iodinated contrast at eGFR 39. Nephrology warned about it in 2025. The ED booked the CT anyway. A 27B-class model used to need a datacentre. PrismML say the 1-bit build is 3.9GB and fits an iPhone 17 Pro Max. The orchestrator never touched the data. That's the whole point. What should it read next?

I finally got GLM-5.2 to work an entire 3-year patient chart that only Bonsai 27B was allowed to read 🔥 292 encounters live inside Bonsai on my Mac Studio. llama.cpp, Metal, ternary, 7.2GB, Apache-2.0. The chart never leaves the machine. GLM-5.2 can only ask questions. It asked three. Bonsai answered each in ~2s with 19,398 tokens still cached. Then it caught the thing buried 17 months back: metformin + iodinated contrast at eGFR 39. Nephrology warned about it in 2025. The ED booked the CT anyway. A 27B-class model used to need a datacentre. PrismML say the 1-bit build is 3.9GB and fits an iPhone 17 Pro Max. The orchestrator never touched the data. That's the whole point. What should it read next?

Maziyar PANAHI

53,274 次观看 • 1 个月前

Opus 4.6 vs GPT 5.4 (High) (1/9) prompt: Build a single-file HTML/CSS/JS (no libs) demo that uses SVG to simulate a plant growing: stem extends, leaves sprout + unfurl with springy/windy “physics”, then seamlessly loops forever. For the initial impressions I'm really impressed by GPT, for speed they both felt about the same, but gpt was still half cheaper than opus. I also much prefer the design and animation that gpt produced, physics on the leaves are super cool and it also loops pretty nicely whilst opus just fades out the plant. Still got a bunch of tests to run but this is really exciting.

Opus 4.6 vs GPT 5.4 (High) (1/9) prompt: Build a single-file HTML/CSS/JS (no libs) demo that uses SVG to simulate a plant growing: stem extends, leaves sprout + unfurl with springy/windy “physics”, then seamlessly loops forever. For the initial impressions I'm really impressed by GPT, for speed they both felt about the same, but gpt was still half cheaper than opus. I also much prefer the design and animation that gpt produced, physics on the leaves are super cool and it also loops pretty nicely whilst opus just fades out the plant. Still got a bunch of tests to run but this is really exciting.

Dev Ed

660,154 次观看 • 6 个月前

Okay this is kinda wild 👀 NVIDIA is basically handing out FREE API keys to 100+ top AI models - GLM 5.2, DeepSeek V4, Kimi K2.6, MiniMax M3, their own Nemotron and a ton more. it's called NVIDIA NIM and I had no idea it existed. it's rate-limited, so not something you'd run in production - but for personal use it's honestly great. you can poke a dozen frontier models, and some you can even grab and self-host. dropped a key into OpenCode and it just worked. the hyped ones like GLM 5.2 are jammed with queue right now, but the lighter models fly. all of it free.

Okay this is kinda wild 👀 NVIDIA is basically handing out FREE API keys to 100+ top AI models - GLM 5.2, DeepSeek V4, Kimi K2.6, MiniMax M3, their own Nemotron and a ton more. it's called NVIDIA NIM and I had no idea it existed. it's rate-limited, so not something you'd run in production - but for personal use it's honestly great. you can poke a dozen frontier models, and some you can even grab and self-host. dropped a key into OpenCode and it just worked. the hyped ones like GLM 5.2 are jammed with queue right now, but the lighter models fly. all of it free.

Stefan 3D AI

26,272 次观看 • 1 个月前

New Claude Sonnet 5 performs at GPT 5.5 level 6x cheaper! We gave 4 models the same prompt: build three self-contained HTML5 canvas scenes with real physics crash demos Prompts: - A car crashes into a brick wall - A wrecking ball destroys a house - A catapult throws a rock at a castle wall Outputs: Sonnet 5: 15,047 tokens, $0.15 Opus 4.8: 23,063 tokens, $0.58 Sonnet 4.6: 25,824 tokens, $0.39 GPT 5.5: 31,152 tokens, $0.94 Sonnet 5 did as well as Opus 4.8 and GPT 5.5 on all three tests. In the wrecking ball test, it beat Opus 4.8. The cable moves smoothly and every hit connects. In the catapult test, it beat GPT 5.5. The rock always lands inside the wall. Sonnet 5 still needs better detail and graphics. But it used fewer tokens than every other model

New Claude Sonnet 5 performs at GPT 5.5 level 6x cheaper! We gave 4 models the same prompt: build three self-contained HTML5 canvas scenes with real physics crash demos Prompts: - A car crashes into a brick wall - A wrecking ball destroys a house - A catapult throws a rock at a castle wall Outputs: Sonnet 5: 15,047 tokens, $0.15 Opus 4.8: 23,063 tokens, $0.58 Sonnet 4.6: 25,824 tokens, $0.39 GPT 5.5: 31,152 tokens, $0.94 Sonnet 5 did as well as Opus 4.8 and GPT 5.5 on all three tests. In the wrecking ball test, it beat Opus 4.8. The cable moves smoothly and every hit connects. In the catapult test, it beat GPT 5.5. The rock always lands inside the wall. Sonnet 5 still needs better detail and graphics. But it used fewer tokens than every other model

atomic.chat

728,688 次观看 • 2 个月前

1-bit Kimi K3 performs at Opus 5 level on 3D physics! We ran our Atomic Chat quant of Kimi K3 locally on 4x B200 against three cloud models and gave them all the same task, to build a giant anvil drop test as a single HTML file with real physics Outputs: K3 1bit (local): 15.8K tokens, $0 API cost Kimi K3 (API): 15.3K tokens, $0.30 API cost Opus 5: 22.8K tokens, $0.77 API cost GPT 5.6: 14.5K tokens, $0.72 API cost All four got the physics right. But only Kimi made a working winch. The drum turns and the chain drags the flat car off the pad. Opus 5 drew the most detail, road markings and sparks on the hit. And you can run a model at this level on your own box now. That still feels insane to us

1-bit Kimi K3 performs at Opus 5 level on 3D physics! We ran our Atomic Chat quant of Kimi K3 locally on 4x B200 against three cloud models and gave them all the same task, to build a giant anvil drop test as a single HTML file with real physics Outputs: K3 1bit (local): 15.8K tokens, $0 API cost Kimi K3 (API): 15.3K tokens, $0.30 API cost Opus 5: 22.8K tokens, $0.77 API cost GPT 5.6: 14.5K tokens, $0.72 API cost All four got the physics right. But only Kimi made a working winch. The drum turns and the chain drags the flat car off the pad. Opus 5 drew the most detail, road markings and sparks on the hit. And you can run a model at this level on your own box now. That still feels insane to us

atomic.chat

53,845 次观看 • 1 个月前

ANNOUNCING: Djinn has decided to open source all its code! Including code for miners and validators on Bittensor SN103, the website, app, smart contracts, tests, and more! As of five months ago. Yes, day one. We were the people’s subnet back then too, we just didn’t know it yet.

ANNOUNCING: Djinn has decided to open source all its code! Including code for miners and validators on Bittensor SN103, the website, app, smart contracts, tests, and more! As of five months ago. Yes, day one. We were the people’s subnet back then too, we just didn’t know it yet.

Djinn

10,961 次观看 • 2 个月前

GPT-6 Astra vs Fable 5.1 vs Kimi K3 vs Sol Astra: Took 11 minutes. This is from a follow-up after the first attempt had rendering and UI bugs. The smoke feels a little off. I expected more detail, but it did a good job understanding the intent. Cost: $12.85. Fable 5.1: Took 18 minutes to complete the same test with high detailing. Cost: $12.60. Kimi K3: Took 10 minutes with a similar level of output and cost only $6.95. SOL: Took 7 minutes, but the output seemed messy. Cost: $5.78. Personally, I like Kimi K3 for delivering similar quality with better token efficiency, followed by fable 5.1 for realism. What about you? I’ll do a couple more tests before coming to a conclusion.

GPT-6 Astra vs Fable 5.1 vs Kimi K3 vs Sol Astra: Took 11 minutes. This is from a follow-up after the first attempt had rendering and UI bugs. The smoke feels a little off. I expected more detail, but it did a good job understanding the intent. Cost: $12.85. Fable 5.1: Took 18 minutes to complete the same test with high detailing. Cost: $12.60. Kimi K3: Took 10 minutes with a similar level of output and cost only $6.95. SOL: Took 7 minutes, but the output seemed messy. Cost: $5.78. Personally, I like Kimi K3 for delivering similar quality with better token efficiency, followed by fable 5.1 for realism. What about you? I’ll do a couple more tests before coming to a conclusion.

Bhavy☄️

76,377 次观看 • 4 天前

Flappy Bird w/ DeepSeek V4 Pro vs GLM 5.2 vs Fable 5 I ran a small experiment: same game, same prompt, three different models. Test setup was simple: /𝚍𝚎𝚜𝚒𝚐𝚗 in Command Code. The thing I cared about was not UI. UI is now relatively easy for models to imitate. I cared more about UX: whether the model understands the interaction, the flow, the hierarchy, the little product decisions that make something feel usable. 𝙲𝚘𝚜𝚝𝚜: 𝙳𝚎𝚎𝚙𝚂𝚎𝚎𝚔 𝚟𝟺 𝙿𝚛𝚘: $𝟶.𝟶𝟶𝟶𝟾 𝙶𝙻𝙼 𝟻.𝟸: $𝟶.𝟶𝟺𝟾 𝙵𝚊𝚋𝚕𝚎 𝟻: $𝟶.𝟺𝟸 The surprising result was that Fable 5 did not produce a meaningfully better UX. It was good, but not good enough to justify the pricing delta for this kind of design task. Open-source models are getting very interesting here. They are much cheaper, competitive on UI, and increasingly good enough on UX. The gap is closing fast.

Flappy Bird w/ DeepSeek V4 Pro vs GLM 5.2 vs Fable 5 I ran a small experiment: same game, same prompt, three different models. Test setup was simple: /𝚍𝚎𝚜𝚒𝚐𝚗 in Command Code. The thing I cared about was not UI. UI is now relatively easy for models to imitate. I cared more about UX: whether the model understands the interaction, the flow, the hierarchy, the little product decisions that make something feel usable. 𝙲𝚘𝚜𝚝𝚜: 𝙳𝚎𝚎𝚙𝚂𝚎𝚎𝚔 𝚟𝟺 𝙿𝚛𝚘: $𝟶.𝟶𝟶𝟶𝟾 𝙶𝙻𝙼 𝟻.𝟸: $𝟶.𝟶𝟺𝟾 𝙵𝚊𝚋𝚕𝚎 𝟻: $𝟶.𝟺𝟸 The surprising result was that Fable 5 did not produce a meaningfully better UX. It was good, but not good enough to justify the pricing delta for this kind of design task. Open-source models are getting very interesting here. They are much cheaper, competitive on UI, and increasingly good enough on UX. The gap is closing fast.

Ahmad Awais

26,918 次观看 • 2 个月前