Results

Paired experiments hold prompts, Blender builds, model settings and evaluators fixed. Each pair is one generation per condition, so read these as directional evidence rather than a success rate.

#No plugin vs with plugin: October 2, 2026

The interactive comparisons cover eight matched pairs: two tasks across GPT 6 Astra, GPT 6.1 Sol, Opus 5.5 and Sonnet 5.5. Eight fresh no-plugin baselines are paired with eight existing frozen plugin-skill runs, using the same task briefs, models, effort, budget, Blender build and evaluator.

Both reviews preferred the plugin in all eight pairs. Technical gates passed in 4/8 no-plugin submissions and 7/8 plugin submissions after the naming correction. These are observed pilot outcomes, not general success rates. Sonnet's lamp is a technical regression: its no-plugin run passed, while its plugin run failed geometry checks.

The no-plugin runs were collected later, so generation order is not counterbalanced. Oversized staging obscures some baseline assets; preference includes presentation and delivery quality as well as modeling. This tests the plugin's skills and scripts, not MCP's additional effect. See the full comparison report for per-pair outcomes and failed checks.

#Added-guidance pilot: October 2, 2026

The separate added-guidance experiment covers 16 generations across GPT 6 Astra, GPT 6.1 Sol, Opus 5.5 and Sonnet 5.5: a joinery stool and an articulated lamp, each with the current skills and with added spatial checks. Both conditions have the same tools and 12-minute generation budget.

Two counterbalanced Astra reviews preferred the current workflow unanimously in six pairs, the added checks in Sonnet's lamp pair, and split on Sonnet's stool. The extra guidance remains experimental. Fourteen submissions pass the technical gates after correcting a hardware-name false negative; the remaining failures are a timeout and invalid geometry. Structural validity does not settle visual quality or ambiguous contacts.

This comparison measures added guidance on top of existing skills. It is separate from the older without-plugin / with-plugin samples below. The full pilot report preserves the correction, limitations and per-pair outcomes.

#Earlier paired samples

In five fresh pairs, the plugin's workflow passed the technical gate every time and the agent without it failed every time. Three blinded judges preferred the plugin's asset in four of the five pairs.

Scores and build times read baseline / plugin. The lanterns were generated with GPT-6 Sol; the press and drawbridge with GPT-6 Astra.

Every baseline failed the geometry check, with invalid elements such as degenerate faces or zero-length edges. Three also exported a 200-meter studio floor with an asset limited to a few meters. The plugin used more tokens in every pair. Lantern build times were about even; the press and drawbridge took about twice as long.

#Claude Code samples

Those pairs ran in Codex. We also ran the lantern task once each with Claude Sonnet 5.5 and Claude Opus 5.5 in Claude Code, using the plugin's skills and no baseline.

Both lanterns showed every required part. Sonnet failed only the automated part-naming check, because no object was named body, housing, or frame. These are single generations with no judges and no baseline, so they don't show a benefit over working without the plugin or rank the models. The full evidence has the controls.

#What this doesn't show

• A general success rate. Five pairs can't predict results for arbitrary prompts, models, or art styles.
• Independent votes. Three judges on one pair are three opinions about the same two assets.
• What the MCP tools add. These runs used the skills without the MCP tools.
• A Claude Code baseline for the earlier study. Its paired samples ran in Codex; its two Claude runs were single generations. The separate spatial pilot above includes paired Claude workflows.
• Consistent visual gains. In lantern repeat 2, every judge preferred the baseline's appearance, even though its geometry failed the technical gate.

Plugin revisions have also shown both gains and regressions. Review results yourself, particularly shape, materials, animation, and exports.

#Details

The full evidence lists every defect, the controls, and how the oversized floors were handled for visual judging. The benchmark methodology defines the conditions and the visual rubric.

#Whole-scene benchmark

Two additional opt-in suites expand coverage: scenes contains a decorated reading room, an exportable low-poly night market and a coastal-cafe holdout; game_characters contains a static ranger, a rigged scout with deformation poses and a nonhuman badger-merchant holdout. Scene evidence keeps authored cameras and lighting, and render-only interiors do not require a GLB. These are available fixtures. The gallery now adds ten completed scene pairs: reading rooms and night markets across all four current models, and coastal cafés from Astra and Sol. Eight pairs have valid counterbalanced reviews; two market pairs are unreviewed. The Claude cafés and game-character runs did not produce complete paired evaluator results and are excluded from the completed comparison count. The October 4 publication report records every added row, resource limits, scoring corrections and missing evidence. See the scene and character protocol.

The courier depot benchmark asks for 21 asset families and 45 instances. It checks each asset's construction and placement, then the assembled scene. The suite is implemented and calibrated; model runs are still pending. Read the whole-scene protocol.

#Gallery previews

The gallery covers five tasks with GPT 6 Astra, GPT 6.1 Sol, Opus 5.5, and Sonnet 5.5. Each entry compares no plugin with the pinned plugin workflow. Original dates, scores, and study conditions remain attached to each pair.

The older GPT 6 Sol lantern is a separate historical example. The gallery owner selected run 3 after viewing all three attempts; all three reviewers preferred its no-plugin result. The other attempts remain in the archived gallery. This selection is not an average or a random sample.

HD previews are new 1536-pixel Cycles renders with 64 samples and OpenImageDenoise. Saved studio floors and backdrops are hidden where needed to frame the asset; their names appear under Scores & review. The saved models and technical results are unchanged. Choose Review images for the images used by the judges. Their votes describe those images, not the new previews.

For the matrix expansion, review views exclude the listed studio meshes too. This prevents oversized floors from hiding the modeled asset. Original submission views are linked inside Scores & review, and their technical failures remain recorded.

The matrix protocol lists the generation controls and selection policy.