DeepSeek V4.1 Flash in OpenCode 1.18.32
skills off
Web model exported by this site from the scene.blend the agent saved during the run. The model is not a deliverable produced by the agent. The render and original scene are separate downloads below.
Wall time 1h 14mCost $0.052 reported
Review
No verdict yet.
The full review
OK. Finished on its own after 74.6 min.
Scores
AI judge
Not scored
No rubric for this prompt
Visitor votes and reviewer grades are not available yet.
- AI judge
- AI judge: the test runner (model claude-opus-5-5), scoring by visual review of the 1600 px render against the pre-registered rubric. No AI-judge score exists for this result: there is no rubric for this prompt, so no score is shown and none is made up.
- Visitors
- Public voting with a blind reveal is coming: you score the output first, then see the other scores.
- Reviewer
- The reviewer's own grade appears here once it is added.
Scores compare results for the same prompt only. AI-judge boards rank runs within one prompt, equal totals sharing a rank; scores from different prompts are never averaged.
Downloads
Files actually published for this run. Original output, site-made previews and post-run conversions are labelled separately.
The exact prompt
as run, 621 characters. Every result for this prompt
Using Blender's Python API only (a script run headless with blender -b -P), model SpongeBob SquarePants as a stylised 3D character: porous yellow sponge body, white shirt, red tie, brown square pants, big eyes with blue irises, buck teeth, thin arms and legs with white socks and black shoes. Use real material colours (Principled BSDF base colours, not procedural-only) so the model keeps its colours when exported. Add a simple three-point light and camera, render a 1280x720 PNG to render.png, save scene.blend, and export model.glb (apply modifiers, no cameras or lights). Write everything into the working directory.
How it was made
Versions saved during the run
6 intermediate outputs, in the order the model wrote them.
Step by step
The prompt, every agent turn and tool call (tool name and a one-line summary; inputs and outputs are not shown), then the final message. The full transcript is 5.1 MB; it is published as a scrubbed, compressed download rather than shown on the page.
Show all 92 steps
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Download the full transcript OpenCode session export, 86 KB, gzip. Scrubbed: anything that looks like a key, token, e-mail address or phone number is replaced, and account details and the test machine's file paths are removed.
Cost breakdown
Headline cost $0.052, as reported by OpenCode (its own bill for the run). The list-price estimate from the token counts is $0.052.
| Tokens by type | Tokens | $ per 1M | Cost |
|---|---|---|---|
| Input (uncached) | 28,484 | $0.15 | $0.0043 |
| Cache write | 0 | $0.15 | $0 |
| Cache read | 2,680,704 | $0.00 | $0.0080 |
| Output | 65,388 | $0.60 | $0.0392 |
| of which reasoning | 50,296 | billed as output |
- Total computed from tokens
- $0.0515
- Total reported by the harness
- $0.0515
- Difference
- matches
OpenCode 1.18.32 reported its own total; token counts x list prices shown beside it. The harness reports reasoning tokens separately from output; here output includes them (15,092 visible + 50,296 reasoning), and both bill at the output rate.
Prices: https://api-docs.deepseek.com/quick_start/pricing, 2026-09-24.
Run notes
- frozen prompt (written), used verbatim
- plain OpenCode config copy (providers only; no ECC instructions/agents)
- W6: Blender 5.2.2 GUI on a private Xvfb display inside the sandbox (filesystem socket in the sandbox's own /tmp only, per-run cookie), software OpenGL (llvmpipe), render threads 2; working directory = the run's work dir
- opencode export written to a file (not piped)
- Mode: interactive TUI in tmux (160x48), inside bubblewrap sandbox
- Isolation audit: outside paths seen