identities revealed · vote counted toward rankings
[3] judge by +
[4] filters: all +
identities sealed until vote
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best follows the task · strongest overall quality
advanced ranking modes Model × Effort
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rankings · quality
Quality ranks models by human preference, grouped by reasoning effort.
Efficiency
higher is betterWhy this is not pooled: each model, harness, effort, and profile combination stays separate. Repeated runs of the same configuration are averaged; settings are never averaged together, making tradeoffs between configurations visible.
| cost/run · tokens | confidence | |||||
|---|---|---|---|---|---|---|
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Ratings begin at 1500. Model × Effort pools across harnesses and profiles; Harness uses direct same-model, same-effort comparisons. Configurations with fewer than five votes are marked provisional.
[3] filters +
runs
search and filter imported runs, then open one to inspect its render, 3d model and recorded metrics.
| run · date | model · harness | effort | prompt | time · cost | agent activity | geometry | compare |
|---|---|---|---|---|---|---|---|
| Loading run history… | |||||||
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select a prompt to inspect each side’s render and 3d scene.
compare configurations
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[9] session timeline · loading trace…
how the benchmark works
the evaluation isolates the full tool-using stack while keeping the voting experience simple and blind.
- 01[controlled]standardized task
every run begins with the same natural-language blender task.
- 02[varies]agent builds
models work through their assigned harness and reasoning setting.
- 03[hidden until vote]blind comparison
identities and configuration details remain hidden during voting.
- 04[calculated]stack-level elo
pairwise outcomes update ratings for each configuration.
- controlled
- prompt, benchmark scene, comparison protocol, and vote outcomes.
- what varies
- model, reasoning effort, harness, and the resulting blender scene.
- after voting
- identities and technical run metrics are revealed without moving the renders.
- low samples
- new configurations remain marked provisional until enough votes accumulate.
- rating order
- elo is replayed vote by vote in the order votes were cast, so the same votes in another order can give slightly different ratings.
- shared outcomes
- a tie lifts and “both bad” lowers both results at once, so these outcomes shift the overall rating level instead of trading points between the two.
- blindness limits
- renders are public on run pages, so a determined voter could recognise a result. identities stay hidden in the vote itself.
- vote limits
- each browser votes once per pair, and requests are rate-limited per network address to bound automated voting.
pi · omp · omp-mcp
omp-mcp-skill · codex · opencode
images pulled on first use
run the benchmark locally
every run starts in a pinned docker image: one prompt per fresh container with blender and the selected agent, then the run is submitted separately.
$ chmod +x bench-run
$ ./bench-run pi --model PROVIDER/MODEL:high worldtree
$ ./bench-run submit results/pi/normal/MODEL/high/worldtree/0
# existing runs: submit-run.sh --dir <path>- images for pi, omp, omp-mcp, omp-mcp-skill, codex and opencode are pulled on first use.
- only the selected agent’s credentials are copied into the container.
- no cpu or gpu rendering is assumed; the actual blender environment is recorded with the session, preview, glb and blend file.
- session logs can contain prompts, tool output and local paths — inspect them before confirming upload.
- run locallybench-run executes the prompt in a fresh container
- submitbench-run submit uploads the run directory
- private staging queueuploads wait here, not yet public
- administrator review → publishedthe run appears in history and enters voting
preference data for a more open 3D future.
Render Arena is an open benchmark for comparing real Blender runs across the complete agent stack. Votes are stored against an anonymous browser token so one voter cannot submit the same comparison twice. Read more →