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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteIn one study of LEED building-document checks, Gemma 3 4B was more accurate than Llama 3.1 8B: 67.3% versus 60.5%. That 6.8-percentage-point gap is a result for one specific task and dataset—not proof that smaller language models are generally better.
What the study compared
Aritro De and Juliana Felkner’s July 17, 2026 paper tested local language models as verifiers of documentation for LEED v4.1 Building Design and Construction (BD+C) credits. Across four University of Texas at Austin building projects, the evaluation covered 153 credit-level decisions supported by 484 PDFs. The authors describe the results as specific to this task, not as a general benchmark of model intelligence. Read the paper on arXiv.
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The models were asked whether the submitted documentation supported credit compliance, with outputs including pass, fail, or insufficient data. The documents ranged across project narratives, schedules, energy reports, commissioning plans, material submittals, construction-waste reports, and drawings. Each ground-truth decision was checked by a LEED Accredited Professional using project documentation and official GBCI review information.
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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →How the two models performed
| Measure | Gemma 3 4B | Llama 3.1 8B |
|---|---|---|
| Text-only overall accuracy | 67.3% | 60.5% |
| Reported runtime per credit in the tested setup | About 35 seconds | About 45 seconds |
Gemma’s lead was 6.8 percentage points overall. It scored higher on three of the four buildings, while Llama 3.1 8B did better on the second. The aggregate result therefore hides meaningful variation among projects and their documentation.
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Why parameter count did not settle the result
Parameter count alone does not determine accuracy on a particular workflow. Here, the models had to find relevant evidence in heterogeneous project files and interpret whether that evidence supported a credit decision. Their performance depended on the fit between the model, task, prompts, and available documentation—not simply on whether one model had more parameters.
The paper does not establish a single cause for Gemma’s lead, nor does it show that a 4B model will outperform an 8B model on other tasks. Its evidence supports the narrower conclusion that the larger model did not win this evaluation.
Why adding a rule-based checker did not raise overall accuracy
The authors also tested a neuro-symbolic configuration: a language model handled contextual interpretation and evidence or value extraction, while a deterministic checker applied encoded arithmetic and thresholds. That complete configuration scored 61.6% overall, below text-only Gemma 3 4B’s 67.3%.
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The checker did help on a targeted quantitative prerequisite. For EA-p2, accuracy rose from 50% with text-only Gemma 3 4B to 100% in the neuro-symbolic configuration. This illustrates the distinction between a focused win on a rule-based calculation and performance across all credit decisions. A rule checker can help when the relevant values are extracted correctly and the requirement is explicit; adding pipeline stages does not guarantee better overall results. Evidence retrieval and extraction remained important, and some qualitative categories lost ground.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the document and prompt tests suggest
Drawing images
In the tested ablations, including low-resolution drawing images rendered at 150–300 dpi consistently reduced accuracy. This is a result for these inputs and this pipeline; it should not be generalized into a claim that vision models or drawing analysis always reduce accuracy.
Prompt choice
Prompt performance varied with each building’s ground-truth PASS rate. Rubric prompts performed better on documentation-rich projects, while chain-of-thought prompts did better on documentation-lean projects. Those patterns describe the study’s dataset, not a universal rule for choosing prompts.
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How much confidence to place in the result
The evaluation was limited to four UT Austin buildings and 153 credit-level decisions. Its ground truth included PASS and INSUFFICIENT_DATA cases but no verified FAIL cases, so the study does not establish how well the models identify actual noncompliance. The authors treat the statistics as descriptive rather than making broad claims about population-level performance.
The reported runtimes—about 35 seconds per credit for text-only Gemma 3 4B and about 45 seconds for Llama 3.1 8B—are observations from the paper’s local Ollama setup, not universal speed guarantees or hardware requirements. The authors say project documents were not transmitted to an external model provider in their local setup. Results on more buildings, certification systems, and model versions would be needed to determine whether the observed model ranking holds elsewhere.
What the result means for choosing a local model
For a document-verification workflow, this paper is a reminder to evaluate models on representative project files and decisions rather than choosing by parameter count alone. An aggregate accuracy score can conceal differences by building, credit type, and error category, while a component that improves a quantitative prerequisite may still lower overall accuracy.
Quick Recap
- Use the result as evidence about this LEED v4.1 BD+C evaluation, not as a universal ranking of 4B and 8B models.
- Check performance across documentation profiles and decision categories, including insufficient-data judgments.
- Evaluate retrieval and extraction alongside the model’s final decision; downstream rules cannot correct values that were never found or were extracted incorrectly.
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