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Evaluation

EEBench

2026ActivePublished: 24 September 2026Updated: 24 September 2026Published
Key innovation
Dispenses with judges — human and model alike. Electronic design correctness is checked by build checks, simulation measurements and BOM constraints, so the score is deterministic and reproducible.
Category
Evaluation
Abstraction level
System
Operation level
Agent runtimeSystem
Use cases
Evaluating models on electronics design tasksComparing engineering agents on cost and timeVerifying understanding of schematics and system diagramsSelecting a model to assist with circuit design

How it works

The model operates as an agent on an electrical engineering design task. The input material can be complex — it includes abstract circuit schematics and system diagrams, so it demands a rigorous integration of visual and textual content. The resulting design then passes three kinds of check: build checks verify that the design builds at all; simulation measurements verify electrical parameters; BOM constraints police whether the components used fit the stated requirements. Each check yields a deterministic verdict, so the same result can be reproduced independently. Cost and time per task and output token counts are measured alongside, allowing comparison not only of effectiveness but of a model's economics.

Problem solved

Evaluating models in engineering domains usually rests on closed questions or a model judge, neither of which says anything about whether the designed circuit actually works. EEBench closes that gap: it checks the engineering artefact with the same tooling used to check a real design.

Components

Build checksDesign buildability check

Verify that the produced design can be built at all — the first, hard correctness barrier.

Simulation measurementsElectrical parameter verification

Simulation measures whether the circuit meets the required parameters, instead of relying on the model's claim.

BOM constraintsComponent selection check

The bill of materials must fit the stated constraints — ruling out designs that are electrically sound but not realizable.

Cost and time metricsSecond axis of comparison

The leaderboard reports cost per task, time per task and output token counts alongside the score itself.

Implementation

Implementation pitfalls
Narrow domainMedium

The score speaks to electrical engineering, not general engineering ability; transferring it to other domains is unwarranted.

Fix:Treat EEBench as a domain signal and pair it with benchmarks from other areas.
Toolchain dependenceLow

Deterministic verification requires a specific build and simulation environment; a change of tool versions can shift results.

Fix:Compare results from the same leaderboard run and check the snapshot date.

Evolution

2026
Public EEBench leaderboard
Inflection point

atopile runs a public leaderboard with an option to submit your own model; in the published snapshot GPT-6 Astra leads with 69.3%.