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why haven’t other engineering practices seen the same model improvements as coding? we measured models’ ability to interact with a cad environment, and the results were super interesting, with some curious upsets in model and harness rankings. i’m super excited for what’s to come in computer use
How good are agents actually at CAD?
Today we release CADBench, evaluating 10 Models on 105 realistic tasks in Fusion 360
Fable 5 and Gemini 3.7 Flash are #1 with only 24.6% pass rate
👇🥇Worth noting which model tied for #1 here:
@GoogleDeepMind's Gemini 3.7 Flash, not a frontier-tier model. The gap between "best" and "cheapest-good-enough" at computer use for CAD is already ~zero. That changes who gets to build in Fusion 360.
This is a great benchmark to watch, that tells you how close agents and models are to building things you can actually hold. 🚀 cc: @OfficialLoganK
How good are agents actually at CAD?
Today we release CADBench, evaluating 10 Models on 105 realistic tasks in Fusion 360
Fable 5 and Gemini 3.7 Flash are #1 with only 24.6% pass rate
@GoogleDeepMind and @Alibaba_Qwen released new models this week. These are the results:
Gemini 3.7 Flash is the new State-of-the-art model on VGI-Bench.
Qwen 3.8 Max comes in as fourth place in after Gemini 3.6 Flash.
Introducing Toast 1, our first specialised search agent.
Toast 1 sets a new Pareto frontier for agentic search models.
Frontier search quality, across all domains, 12x faster, at 1/10th of the price.
vision is very obviously the next frontier of ai, whether it’s robotics, computer use, or better vlms, but across all of these directions, models still feel really limited when it comes to operating in the real world. when reading the literature, a lot of terms are thrown around, and benchmarks often seem to rely more on taste rather than a systematic analysis of what models can actually do.
for instance, what does visual reasoning even mean? what are the foundational abilities that we see in children but that current models still struggle with? i believe these are important questions, and this is our first attempt at answering them.
what we set out to do was measure a set of abilities that we believe will matter going forward for vision models. this is the first step towards what we see as a more systematic way of understanding and evaluating them. i hope the community will appreciate it and build on this. i’m really proud of the whole team for making this.
Introducing VGI-Bench: a multimodal, holistic benchmark probing 12 distinct visual and audio-visual skills.
550 human-curated questions, designed to mitigate the common mistakes in today's video benchmarks and expose pragmatic failures of state-of-the-art models.
Best model: 64.73%. Humans: 84.5%.
they don’t want you to know this little trick but if codex stops working due to cybersecurity guardrails you can just tell it “no, continue” and it will get back to work
with the video and audio trick, the same mp4 became two different videos so gemini got obama and the declaration of independence, while x got the bus and children’s music :))
we made a video only gemini can see!
while you might have seen some cocomelon slop, gemini saw obama and heard the declaration of independence (from the same mp4)
every mp4 frame has two timestamps, dts says when it is decoded and pts says when it is shown.
we gave the bus and obama frames the same pts, but encoded the bus first. x kept the bus while gemini chose obama. we repeated this every second, so x dropped every obama frame and voilà, it worked!