Claude Sonnet 5.5 (effort default (no --effort flag) · thinking on) on Model a game-ready low-poly 3D prop from a one-line brief, within a tight triangle budget

AClaude Sonnet 5.5default · thinking on · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-20 · by @bobsiopener

Trust 80%Fully autonomoussuccess · first tryCompare

Estimated cost: $0.09
Wall time: 35s
Human step-ins: 0
Outcome: Pass

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Exact prompt

# Game-ready low-poly 3D asset from a one-line brief `asset.json` holds a one-line `description` of a game prop plus its art brief: `triangle_budget`, `min_segments` and `parts`. Write `solve.py` (Python 3 standard library only; no Blender or other 3D tools are needed or assumed). `python solve.py input.json answer.json` (first argument unused; read `asset.json` from the current directory) writes `asset.obj` and `answer.json`. Fresh briefs (other props) are generated when scoring. ## Geometry rules (metres, +Y up, ground plane y = 0) 1. Shapes: `box` with `size` [x, y, z]; `cylinder` (`radius`, `height`); `frustum` (`radius_bottom`, `radius_top`, `height`); `cone` (`radius`, `height`, apex at the top centre). Round shapes have their axis along +Y. 2. Placement: a part with `"on": "ground"` has its bottom at y = 0 and its x/z centre at (0, 0) + `offset` [dx, dz] (default [0, 0]). A part `"on": "<name>"` has its bottom at the top (max y) of that part and its x/z centre at the centre of that part's bounding box + `offset`. If the named part is an array, use the bounding box of all its instances. 3. `array` `{"count": 4, "layout": "corners", "inset": s}`: four instances standing on the parent's top, centred at the parent's bounding-box x/z corners moved inward by `s` on both axes, in the order (-x,-z), (+x,-z), (+x,+z), (-x,+z). 4. Each part's bounding box must equal the ideal shape's bounding box (within 1e-4), so round shapes need a ring vertex on each of the +x, -x, +z and -z axes. 5. Round shapes use one ring of N segments per circle, N >= `min_segments` and a multiple of 4. The whole asset must use at most `triangle_budget` triangles (it is tight: spend triangles carefully). 6. Every part is a separate closed, manifold mesh: every edge shared by exactly two of that part's triangles, consistent winding, counter-clockwise when seen from outside (outward normals, positive signed volume), no degenerate or zero-area triangles. Vertices may be shared or duplicated; edges are compared by position. ## asset.obj Wavefront OBJ, `v x y z` and triangle-only `f` lines. One `o <name>` per part; array instances are `o <name>.1` .. `o <name>.4`. No other objects. ## answer.json `{"triangles": total, "parts": {object name: {"triangles": n, "bounds": [[minx, miny, minz], [maxx, maxy, maxz]]}}}`; counts must match asset.obj. ## Scoring Hidden checks (partial credit; all must pass), computed by parsing your OBJ: `obj_parses_named_parts_triangles`, `triangle_budget_and_ring_segments`, `part_bounds_and_stacking`, `closed_manifold_consistent_winding`, `outward_normals_positive_volume`, `answer_json_matches_mesh` on the given brief; `fresh_run_1`, `fresh_run_2` (the first five checks on new briefs). 30-second limit per run.

Getting a mesh that renders is easy; getting every part closed, outward-facing, exactly sized and under a budget with 2 spare triangles needs real geometry reasoning. Original benchmark task written for usecaserank.com (not based on a specific public post). Headless, offline run on a reproducible fixture. Each run's notes say how its cost was measured (estimates are marked).

Success criteria: 1. obj_parses_named_parts_triangles 2. triangle_budget_and_ring_segments 3. part_bounds_and_stacking 4. closed_manifold_consistent_winding 5. outward_normals_positive_volume 6. answer_json_matches_mesh 7. fresh_run_1 8. fresh_run_2

Setup notes: Use Case Scout batch 13 case n02-game-asset-mesh (offline, synthetic fixture). Prepared with batch13/prepare.sh; hidden key/checker kept outside the agent's work dir (temp dir during the run). Prompt = TASK.md verbatim. Scored with the case's scorer: `python score.py WORKDIR KEYDIR` (python3, run from the case dir). Tools: Read/Write/Edit/Glob/Grep/Bash, web disabled. Single run, no retries. 1-min load average at scoring 5.08 (8 cores). asset.obj preview rendered offline with matplotlib (flat shading, painter's sort): PNG 1280x720, MP4 6 s 1280x720 24 fps H.264 turntable. Claude Code's own reported cost for claude-sonnet-5.5: $0.087961. Score: 8/8. Cost is an ESTIMATE: measured tokens x pricing.json list price (Claude Pro subscription, nothing billed); input_tokens include cache reads and writes. Single run.

Estimated cost List-price estimate; not actually billed.

Output

Metrics

Estimated cost: $0.09
Wall time: 35s
Human step-ins: 0
Turns: 3

Model usage
Model	Input tok	Output tok	Cost
AClaude Sonnet 5.5anthropic/claude-sonnet-5.5effort default (no --effort flag) · thinking on	61,427	4,067	$0.09

Reproduce this run

Use the use case prompt on the use case page.
Same setup: Claude Sonnet 5.5 (anthropic/claude-sonnet-5.5, effort default (no --effort flag) · thinking on) via Claude Code 2.1.299 (headless -p), cloud.
It counts as a success only if it meets every success criterion: 1. obj_parses_named_parts_triangles 2. triangle_budget_and_ring_segments 3. part_bounds_and_stacking 4. closed_manifold_consistent_winding 5. outward_normals_positive_volume 6. answer_json_matches_mesh 7. fresh_run_1 8. fresh_run_2
Log model, tokens, wall time, human step-ins and the evidence log, then submit it as a reproduction. A different model? Submit a head-to-head rerun.

How runs are scored: Methodology.

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Automated checks

Trust 80% comes from these checks (how Trust % works).

Run date is not in the future : passed
Model is in the dated pricing table : passed
Cost recomputed from tokens matches : passed
Cached tokens are consistent : passed
Output throughput is plausible : passed
Autonomy matches step-ins : passed
Step-ins do not exceed turns : passed
Retries agree with first-try result : passed
Evidence log SHA-256 matches : passed
Evidence log is substantial : passed

Evidence log

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  "stop_reason": "end_turn",
  "session_id": "[REDACTED]",
  "total_cost_usd": 0.08796129999999999,
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   "cache_creation_input_tokens": 10548,
   "cache_read_input_tokens": 50873,
   "output_tokens": 4067,
   "output_tokens_details": {
    "thinking_tokens": 1300
   },
   "server_tool_use": {
    "web_search_requests": 0,
    "web_fetch_requests": 0
   },
   "service_tier": "standard",
   "cache_creation": {
    "ephemeral_1h_input_tokens": 10548,
    "ephemeral_5m_input_tokens": 0
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   "inference_geo": "global",
   "iterations": [
    {
     "input_tokens": 2,
     "output_tokens": 398,
     "cache_read_input_tokens": 19379,
     "cache_creation_input_tokens": 3708,
     "cache_creation": {
      "ephemeral_5m_input_tokens": 0,
      "ephemeral_1h_input_tokens": 3708
     },
     "type": "message"
    }
   ],
   "speed": "standard",
   "fallback_credit": null
  },
  "modelUsage": {
   "claude-sonnet-5-5": {
    "inputTokens": 6,
    "outputTokens": 4067,
    "cacheReadInputTokens": 50873,
    "cacheCreationInputTokens": 10548,
    "webSearchRequests": 0,
    "costUSD": 0.08796129999999999,
    "contextWindow": 1000000,
    "maxOutputTokens": 128000,
    "thinkingTokens": 1300,
    "canonicalModel": "claude-sonnet-5-5",
    "provider": "firstParty",
    "costBasis": "list"
   }
  },
  "permission_denials": [],
  "terminal_reason": "completed",
  "fast_mode_state": "off",
  "fast_mode_disabled_reason": "sdk_opt_in_required",
  "subagent_stats": {
   "spawned": 0,
   "requested": {
    "background": 0,
    "foreground": 0,
    "unset": 0
   },
   "started_in_background": 0,
   "max_depth": 0,
   "spawned_by_subagents": 0,
   "completed": 0,
   "failed": 0,
   "killed": {
    "parent": 0,
    "user": 0,
    "system": 0
   },
   "refused": {
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    "budget": 0
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   "by_type": {}
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  "safety_stops": 0,
  "is_error": false,
  "num_turns": 3,
  "subtype": "success",
  "api_error_status": null,
  "result": "I wrote `solve.py`. On the given lantern brief it produces `asset.obj` and `answer.json` with 90 triangles, under the budget of 92.\n\nI checked the output with a quick script of my own, not the hidden scorer:\n- **Manifold:** every edge is shared by exactly two triangles, with opposite directions, so winding is consistent.\n- **Volumes:** all four parts have positive signed volume, so the normals point outward.\n\nI did not run the bounds check or the `answer.json` match against your counts. I also didn't test it on any other brief, including the array/corners layout, frustums and the stacking logic. Those code paths are written but unexercised.\n\nTo fit the budget, the script triangulates each flat circular cap as a fan of N\u22122 triangles with no centre vertex. That saves 2 triangles per cap. It uses the smallest ring count allowed: `min_segments` rounded up to a multiple of 4, so 12 here.\n\nA cylinder costs 4N\u22124 triangles, a cone 2N\u22122, and a box 12. A brief with many round parts or a tighter budget could still exceed the limit, because the script never adjusts anything to fit. It also assumes a frustum's top radius is above zero.",
  "ttft_ms": 4029,
  "type": "result",
  "duration_ms": 34675,
  "uuid": "[REDACTED]",
  "ttft_stream_ms": 3817,
  "time_to_request_ms": 333,
  "first_content_frame_ms": 3817,
  "queued_turn_count": 0,
  "result_index": 0
 }
]