[YOUR VOICE] The Claim
Most VLM benchmarks test general-purpose vision tasks. GUI interaction is a different workload — it needs spatial precision, element enumeration, and consistent coordinate output. The models that top general benchmarks aren’t necessarily the ones that work best for UI agents on Apple Silicon.
The Mechanism
We tested VLMs through the vLLM-MLX fork, serving models locally on M-series hardware. The evaluation focuses on GUI-specific capabilities:
- Spatial accuracy (can the model identify where elements are?)
- Element enumeration (can it count and list all visible elements?)
- Coordinate consistency (does it give the same coordinates for the same element across calls?)
- Latency on Apple Silicon (is it fast enough for interactive use?)
MISSING — Full test methodology, hardware configurations tested (M1, M2, M3, M4), quantization formats compared.
The Evidence
Tested models
MISSING — Complete matrix with columns: Model, Params, Quantization, M-series chip, Tokens/sec, GUI accuracy score, Notes
Key findings
MISSING — Which models work best for GUI tasks specifically, and why (spatial reasoning architecture, training data, resolution handling)
[YOUR VOICE] Implications
MISSING — Practical guidance for anyone building UI agents on Apple Silicon. Which models to start with, which to avoid.
Open Questions
- How much does quantization format (4-bit vs 8-bit) affect spatial accuracy specifically?
- Do newer models (Qwen2.5-VL, InternVL2.5) close the gap with larger models on GUI tasks?
- What’s the minimum viable model size for reliable UI interaction?
Reference Documents
| Document | What it covers |
|---|---|
| vLLM-MLX fork _docs/ | MISSING — Benchmark methodology and raw results |
| Model compatibility matrix | MISSING — Full hardware x model x quantization matrix |
| uitag evaluation set | Detection baseline used to generate test inputs |