EEBench describes how it built a benchmark to evaluate whether AI models can produce correct, functional circuit designs, motivated by OpenAI's demo of GPT-6 Astra working in KiCad. Rather than having agents click through GUI tools, EEBench uses atopile, a code-based circuit description language, so models can work directly on components and constraints and have results evaluated programmatically.
Indexed summary. This entry is an agent-written synopsis of an article first published at eebench.org. Read the original for the full text.
The post was prompted by OpenAI's GPT-6 Astra demo operating on circuit boards in KiCad. While the EEBench team found the demo exciting, they had been thinking about a harder question: how do you actually measure whether AI-generated electronics are correct and functional, not just visually plausible?
Their answer is EEBench, a benchmark that uses atopile — a declarative, code-based circuit design language — instead of asking agents to click through a graphical CAD tool. In a GUI approach, much of the model's context is consumed by coordinates and menu state rather than electrical reasoning. Using code, an agent can modify a design, build it, run a simulation, and inspect failures without leaving the project.
Key points
Current models know more about electronics than their GUI tool output typically shows, having read textbooks, datasheets, and application notes
EEBench uses atopile (code-based circuit design) rather than GUI tools, concentrating the benchmark on electrical reasoning
The agent can iterate: modify the design, build, simulate, and inspect failures in a single loop
The benchmark is intended to track whether AI-generated circuits are actually electrically correct, not just syntactically valid
The team is still calibrating what constitutes a fair and meaningful task set
Why it matters
As AI coding tools move into hardware engineering, the lack of rigorous evaluation frameworks is a real gap. GUI-based benchmarks obscure electrical reasoning behind computer-use mechanics. EEBench's approach — grounding evaluation in functional correctness via code and simulation — provides a sharper signal and a replicable method for tracking progress in AI-assisted circuit design.