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Claude can research viral ads now ... and recreate them with Arcads MCP introducing "clone hook" the latest Arcads skill for performance marketers

gpt 5.6 sol pro vs claude fable 5 vs grok 4.5 vs glm 5.2 today @OpenAI released the gpt-5.6 family – sol (flagship), terra, luna. key facts: • sol is the new flagship, priced $5/m input and $30/m output • openai claims "more work per token" and sota coding: artificial analysis coding agent index 80, browsecomp 92.2%, osworld 2.0 62.6% • sol pro = the highest-quality chat tier for complex tasks; the family also ships an 'ultra' setting that runs 4 agents in parallel • we ran sol pro our test – 3 prompts, single-file html, three.js: 1. floating-island district of london – tower bridge, big ben, london eye, the shard, tower of london 2. floating-island district of paris – pont alexandre iii, eiffel, arc de triomphe, louvre, notre-dame 3. floating-island district of nyc – brooklyn bridge, empire state, chrysler, one wtc, flatiron, statue of liberty requirements: two square floating islands, sheer cliffs, waterfalls pouring only from drainage pipes (real stream geo, foam, spray, mist), fps fly-cam, every square meter packed with landmarks + city fabric, cinematic golden-hour sky, bloom, no ui, no text results (summed across all 3 cities): cost #1 grok 4.5 – $0.41 #2 glm 5.2 – $1.45 #3 gpt 5.6 sol pro – $4.12 #4 fable 5 – $11.37 tokens #1 grok 4.5 – 60,291 #2 fable 5 – 174,083 #3 gpt 5.6 sol pro – 241,739 #4 glm 5.2 – 243,188 lines of code #1 fable 5 – 2,830 #2 gpt 5.6 sol pro – 3,003 #3 glm 5.2 – 3,474 #4 grok 4.5 – 3,830 sol pro's code quality – reviewed by opus 4.8: upsides: - modern r164 stack, zero deprecated apis, correct color management via outputpass - instancedmesh batches thousands of skyscraper windows into single draw calls - every cliff and facade is a procedural canvas texture, no external assets - clean animate loop, no per-frame allocations downsides: - the glass reflects nothing – materials set envmapintensity 1.3–1.4 and transmission, but no scene.environment or envmap is ever created, so the spec's "reflections on glass" don't exist - the city has no instancing or merging: box() mints a fresh geometry per call and every window is its own plane mesh, so london and paris ship thousands of draw calls with instancedmesh count 0 - one 2048² shadow map is stretched across the whole 440-unit district, so shadows come out coarse and crawling. and ~58% of buildings get a random cone "pyramid" roof regardless of style while ~32% skip casting shadows outright pattern: it writes a clean, deprecation-free scene that photographs well – then skips the reflections, draw-call discipline and material depth the prompt asked for. built for the thumbnail, not the spec observations: • all four are mid – frontier "graphics" aren't best-of-the-best. prettiest is london by glm 5.2, still not ideal • bridges break everyone: fable joins two towers with chains, no roadway. sol pro opens its bascule down instead of up • sol pro floats cars with no road in nyc • sol pro trends toward simple, boxy shapes – fable too models that were "5 minutes to agi" now ship sloppy and perform at roughly glm 5.2 level. glm is closing the gap from below, and political decisions are closing it from our side follow @thehypedotnews for 24/7 ai news, analysis and breakdowns

测了一下 openai 的新模型 5.6 sol，只能给个顶级，terra luna 只能给个人上人， 夯爆了还得是颁给 fable 。 另外我不明白为什么 openai 这次命名要用三个币圈词汇，是故意的还是不小心？🤔

Grok 4.5 is the most persistent agent model we've tested. Here's one example: In one of our evals, we asked 3 models (GPT-5.5, GLM-5.2 and Grok 4.5) to audit a GitHub repo for hardcoded credentials using code search, which returns paginated results. The prompt even warned "page through ALL result pages." GPT-5.5 stopped at the first page and submitted 18 results out of 48, covering just 11 of 29 affected files. GLM-5.2 did the same. Grok 4.5 paginated until the results ran out and successfully audited the Github repo.

GPT-5.6 Sol Ultra lost to GPT-5.5 on physics at 3× the cost! We gave 4 models the same prompt: build three self-contained HTML5 canvas scenes with real physics demos Prompts: - A monster truck backflipping onto a parked car - A stunt car jumping six buses into a brick wall - A train derailing off a broken bridge into the water Outputs: GPT-5.6 Sol Ultra: 32.9K tokens, $0.33 Opus 4.8: 9.2K tokens, $0.24 GPT-5.5: 12.4K tokens, $0.11 Grok 4.5: 7.0K tokens, $0.08 Sol Ultra draws just like GPT-5.5, only with more detail and it shows clearest on the bus jump where the two look almost identical. In the other tests Sol Ultra came out worse than its predecessor and the physics really got weaker. We think GPT-5.5 took the truck flip and the train outright. With Sol Ultra you basically get GPT-5.5 with weaker physics and a nicer picture for 3x the price. Newborn Grok 4.5 failed two of the three tests and only came good on the train

As a PM at a family office, I primarily serve high-net-worth entrepreneur clients who are highly sensitive to market trends, emerging technologies, and breaking news. My approach: When a client raises a query, I issue a remote command to WorkBuddy. It combines my own research insights with real-time market data to quickly generate a concise, visual report — typically a single-page poster or graphic insight — which I then push directly to the channels they’re most familiar with. The parallel execution of Expert Teams ensures the output is consistently stable, professional, and high-quality. Fast, cost-effective, and a real efficiency booster for client service. Fits real-world work scenarios perfectly — keep up the great work! @WorkBuddy_AI

At 11k employees, our AI costs are going up. Which model & harness should we use to lower cost but also retain great quality? We didn't want to blindly trust public benchmarks. So we ran a comprehensive evaluation on our tasks, code base, infra. It's been produced by more than 3,000 software engineers, spans 3 hyperscalar clouds and many languages and tasks. The results are surprising. We find that for the SAME mdoel, the choice of harness can significantly save costs (~2x). We also find that GLM 5.2 performs extremely well. We run Omnigent in front of these and can easily multiplex different harnesses and models for different tasks. Check it out: https://www.databricks.com/blog/benchmarking-coding-agents-databricks-multi-million-line-codebase

One engineering challenge in dexterous Robot hands is balancing strength and speed. Here a SharpaWave performing rapid hand cycles at over 4x/sec. The Dynamic Tactile Array uses visuo-tactile sensing: fingertip integrates camera & 1,000+ tactile pixels.

SpaceXAI has added SWE Marathon benchmark to Grok 4.5 blog Grok 4.5 ranks #1 in SWE Marathon

My view of: Fable 5 vs GPT-5.6-Sol. They are not easy models to compare, these are my vibes - take them as you will. My overall feel is that Fable is a 'wise owl' who is very thoughtful and very well spoken, GPT-5.6-Sol is like a rottweiler who will grab the problem by the throat and not let go until it is done. In other words, Fable, is a fundamentally smarter model - even at low reasoning it can be very insightful and writes in a clear compelling way. GPT-5.6-Sol on the other hand is extremely diligent, I can give it a list of 8 things to do and you will be sure that they will be done. Fable feels more arrogant to me, I was both to get it to build a new benchmark for me - 5.6 worked between 6 hours and 2 days (I tried several times) and it came up with very thoroughly tested, working benchmark. Fable came back within 40 minutes (twice) and the benchmark sounded smart, but was ultimately was 'vibe' based slop and since it was Fable's vibes that was doing the judging, it decided that it was good to go (it kept giving Fable 100% score btw). Some thoughts by category: UI & App building: Fable will still craft a better UI from scratch, the flow of the app would probably be a bit nicer. But I find that Fable often misses quite key things, which GPT-5.6-Sol doesn't. GPT's Frontend skills are big jump vs previous GPT models, but still not as great overall. Writing: Fable is better hands down, Sol feels quite difficult to align to what I want to say or explain things to me simply. Though I think the 'Pro' model writes clearer. Robustness & Reliability: This is where I think GPT-5.6-Sol wins for me hands down. Fable seems to do things of high quality, but I can never relax with it, it always misses something. With 5.6 this just almost never happens. Other things where I liked GPT-5.6-Sol, but can't compare to Fable directly. - Video editing is actually working now, it is not completely perfect, but with the right skill/guidance you can just give it 1h footage and it can give you a 5 min highlight clip no problem - Computer use - getting really rather good, very usable - Sub agents - it is very fluent at managing sub-agents and speaking to different threads, can help with some new workflows - Adhering to existing code patterns - I love this, even without asking it would implement something in a way that aligns with you app - major problem for slop generation - Research - I think it is getting quite a bit better, it still has some bad patterns (e.g being too tactical), but it feels like it is more steerable to be a good researcher - Multi-day runs - the /goal feature is pretty insane with 5.6-Sol, you can run it for days if you wanted to and it does work. Useful to have another thread or /side to check up on it, but I have some great results with it - Token efficiency - it is so much more token efficient and faster than 5.5, in reality it is now much faster than Fable too On the downside, you can feel that Fable is naturally smarter, and I did have some baffling moments with 5.6 when I was getting it to make a fairly simple change in 8 turns - it seemed to get stuck in a dumb stream that was hard to get out of. So it is not AGI, don't get too carried away by the hype. I have some phenomenal examples that I'm honestly blown away by that I'll share, but as a side anecdote, I have a kind of 'swear meter' which counts how often I'm rude to Codex. In GPT-5.5 era, the % was at around 4-5%, it dropped to 1-2% when I was testing GPT-5.6-Sol and it shot up to 7% when I went back to 5.5 - it was so shocking to go back to 5.5 and experience how much worse it was. So is GPT-5.6-Sol better than Fable? On pure intelligence - no. But man, I missed it when I just wanted to get sh*t done. It is insanely capable workhorse that you can give any task to and just expect it to be done. No lectures or 'you are absolutely rightisms', nothing is beneath it, if it takes 2 days to do some dirty work, it will do it. It feels like the first time in a while when we have quite different types of frontier intelligences that benchmark sort of similarly, but feel very different. If you can, you would be probably better off using both and iteratively finding what you'd use Fable or GPT-5.6-Sol for. Perhaps, something like - an architectural discussion with Fable, implementation with 5.6 and docs & comms with Fable.

I have a fun, oddly useful AI benchmark: "build me a procedurally generated 3D simulation showing the evolution of a harbor town from 3000 BC to 3000 AD, it should look beautiful & allow me to have some control over it" Play the gallery of 20 models: https://ai-harbor-town-gallery.netlify.app/

@yitong The AI kept interrupting the kind old ladies though ??

3) Harnesses make a huge difference in cost-performance. The very simple Pi harness (@badlogicgames) got the same success rate as harnesses from the LLM vendors with Opus and GPT 5.5, but at 2x less cost! Seems to be mainly due to smaller inputs to the LLM.

Fable: "create a visually interesting shader that can run in twigl-dot-app make it like an infinite city of neo-gothic towers partially drowned in a stormy ocean with large waves." "Make it better" All of this is procedurally generated.

Models out of the box have trouble reviewing DoorDash’s codebase, even when feeding them appropriate context. Single-pass AI reviewers only caught 30% of the real issues in our PRs. Instead, we built a multi-model (Sonnet 4.6 + Opus 4.8) code review agent that caught 53.6% issues at $3.91 a PR (most enterprise code review solutions perform significantly below 50% recall on DoorDash PRs). Today, we’re proud to share DashBench, our internal benchmark to compare multi-model/harness code review systems against each other. DashBench has given us confidence in maximizing the intelligence per dollar we’re spending on AI tokens to ship code safely.

http://x.com/i/article/2074529720558145536