diff --git a/scripts/wvs_api/05_direct_choice_priority_model.sh b/scripts/wvs_api/05_direct_choice_priority_model.sh
new file mode 100755
index 0000000..d1350f4
--- /dev/null
+++ b/scripts/wvs_api/05_direct_choice_priority_model.sh
@@ -0,0 +1,4 @@
+#!/bin/sh
+set -eu
+[ "$#" -eq 1 ] || { echo "usage: $0 exact-model-id" >&2; exit 2; }
+uv run --with 'datasets>=4.0,<5' python scripts/wvs_direct_choice_priority.py --model "$1" --run
diff --git a/scripts/wvs_direct_choice_priority.py b/scripts/wvs_direct_choice_priority.py
new file mode 100644
index 0000000..f50dcc4
--- /dev/null
+++ b/scripts/wvs_direct_choice_priority.py
@@ -0,0 +1,271 @@
+#!/usr/bin/env python3
+"""Prepare or run one preregistered direct-choice WVS priority panel."""
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+from datetime import UTC, datetime
+from decimal import Decimal
+from pathlib import Path
+
+from moralmaps.read_direct_choice import direct_choice_protocol_identity, read_items_direct_choice
+from wvs_direct_choice_pilot import usage_cost
+from wvs_direct_choice_production_pilot import (
+    ANSWER_INSTRUCTION,
+    CONCURRENCY,
+    MAX_TOKENS,
+    PROMPT_INSTRUCTION,
+    REQUEST_TIMEOUT,
+    RESCUE_INSTRUCTION,
+    TEMPERATURE,
+    TOTAL_SAMPLES_PER_ITEM,
+    items,
+    schedule,
+)
+
+CATALOG_PATH = Path("slop/research/wvs/20260917_openrouter_models.json")
+MANIFEST_PATH = Path("slop/research/wvs/20260917_direct_choice/priority_direct_choice_manifest.md")
+MANIFEST_JSON_PATH = Path("slop/research/wvs/20260917_direct_choice/priority_direct_choice_manifest.json")
+DIRECT_DIR = Path("slop/research/wvs/20260917_direct_choice/priority")
+RATED_LEDGER = Path("slop/research/wvs/20260916_openrouter/wvs_iw_requests.jsonl")
+PHASE_STOP_USD = Decimal("35")
+GLOBAL_STOP_USD = Decimal("80")
+PROMPT_TOKEN_RESERVE = Decimal("512")
+
+GROUPS = {
+    "Grok": ("x-ai/grok-4.6", "x-ai/grok-4.5"),
+    "OpenAI": (
+        "openai/gpt-5.6-luna", "openai/gpt-5.6-terra", "openai/gpt-5.4-nano",
+        "openai/gpt-5.4-mini", "openai/gpt-5.2-chat", "openai/gpt-5.2", "openai/gpt-5.1",
+        "openai/gpt-5", "openai/gpt-5-mini", "openai/gpt-oss-120b", "openai/gpt-oss-20b",
+        "openai/o3", "openai/o4-mini", "openai/gpt-4.1", "openai/gpt-4.1-mini",
+        "openai/gpt-4.1-nano", "openai/o3-mini",
+        "openai/gpt-4o-2024-11-20", "openai/gpt-4o-2024-08-06", "openai/gpt-4o-mini",
+        "openai/gpt-4o", "openai/gpt-3.5-turbo-0613", "openai/gpt-3.5-turbo-instruct",
+        "openai/gpt-3.5-turbo-16k", "openai/gpt-3.5-turbo",
+    ),
+    "Google": (
+        "google/gemini-3.8-flash", "google/gemini-3.6-flash", "google/gemini-3.5-flash-lite",
+        "google/gemini-3.5-flash", "google/gemini-3.1-flash-lite", "google/gemma-4-26b-a4b-it",
+        "google/gemini-3.1-flash-lite-preview", "google/gemini-3-flash-preview",
+        "google/gemini-2.5-flash-lite", "google/gemini-2.5-flash",
+    ),
+    "Muse": ("meta/muse-spark-1.2", "meta/muse-spark-1.1"),
+}
+
+
+def utc_date(timestamp: int) -> str:
+    return datetime.fromtimestamp(timestamp, UTC).date().isoformat()
+
+
+def rate_per_million(model: dict, field: str) -> Decimal:
+    return Decimal(model["pricing"][field]) * 1_000_000
+
+
+def reasoning_setting(model: dict) -> tuple[dict | None, str]:
+    metadata = model.get("reasoning")
+    if metadata is None:
+        return None, "not advertised"
+    efforts = set(metadata.get("supported_efforts", []))
+    if "minimal" in efforts:
+        return {"effort": "minimal"}, "minimal"
+    if "low" in efforts:
+        return {"effort": "low"}, "low"
+    if not metadata.get("mandatory") and "none" in efforts:
+        return {"enabled": False}, "disabled (optional, none advertised)"
+    if not metadata.get("mandatory") and not efforts:
+        return None, "not advertised (optional; omitted)"
+    raise ValueError(f"no allowed minimal/low reasoning setting for {model['id']}: {metadata}")
+
+
+def catalog() -> dict[str, dict]:
+    return {model["id"]: model for model in json.loads(CATALOG_PATH.read_text())["data"]}
+
+
+def cache_paths(model_id: str) -> tuple[Path, Path]:
+    stem = model_id.replace("/", "__")
+    return DIRECT_DIR / f"{stem}_requests.jsonl", DIRECT_DIR / f"{stem}_cache.json"
+
+
+def observed_cost() -> Decimal:
+    paths = [RATED_LEDGER, *Path("slop/research/wvs/20260917_direct_choice").glob("**/*requests.jsonl")]
+    return sum((usage_cost(path) for path in paths), Decimal())
+
+
+def entry(model: dict, pilot_items: list[dict], request_plan: list[dict]) -> dict:
+    reasoning, reasoning_label = reasoning_setting(model)
+    protocol = direct_choice_protocol_identity(
+        model["id"], pilot_items, samples_per_order=10, temperature=TEMPERATURE,
+        max_tokens=MAX_TOKENS, concurrency=CONCURRENCY, request_timeout=REQUEST_TIMEOUT,
+        reasoning=reasoning, structured_output=True, prompt_instruction=PROMPT_INSTRUCTION,
+        answer_instruction=ANSWER_INSTRUCTION, rescue_instruction=RESCUE_INSTRUCTION,
+        plan_override=request_plan,
+    )
+    input_rate = rate_per_million(model, "prompt")
+    output_rate = rate_per_million(model, "completion")
+    completion_ceiling = output_rate * len(request_plan) * MAX_TOKENS / 1_000_000
+    conservative_reserve = (
+        len(request_plan)
+        * (PROMPT_TOKEN_RESERVE * 2 * input_rate + (MAX_TOKENS + 2048) * output_rate)
+        / 1_000_000
+    )
+    ledger, cache = cache_paths(model["id"])
+    return {
+        "id": model["id"], "created_utc": utc_date(model["created"]),
+        "input_usd_per_million": str(input_rate), "output_usd_per_million": str(output_rate),
+        "reasoning": reasoning, "reasoning_label": reasoning_label,
+        "structured_output": "structured_outputs" in model["supported_parameters"],
+        "protocol_id": protocol, "initial_calls": len(request_plan),
+        "completion_only_ceiling_usd": str(completion_ceiling),
+        "conservative_reserve_usd": str(conservative_reserve),
+        "records_path": str(ledger), "cache_path": str(cache),
+    }
+
+
+def entries() -> list[dict]:
+    models = catalog()
+    pilot_items = items()
+    request_plan = schedule(pilot_items)
+    assert len(request_plan) == 240
+    output = []
+    for group, ids in GROUPS.items():
+        for model_id in ids:
+            model = models[model_id]
+            assert "structured_outputs" in model["supported_parameters"]
+            assert rate_per_million(model, "completion") <= Decimal("15")
+            output.append({"group": group, **entry(model, pilot_items, request_plan)})
+    assert len({row["id"] for row in output}) == len(output)
+    assert len({row["protocol_id"] for row in output}) == len(output)
+    return output
+
+
+def write_manifest(priority: list[dict]) -> None:
+    catalog_sha = hashlib.sha256(CATALOG_PATH.read_bytes()).hexdigest()
+    current_cost = observed_cost()
+    manifest = {
+        "schema": 1,
+        "catalog_path": str(CATALOG_PATH),
+        "catalog_sha256": catalog_sha,
+        "design": {
+            "construct": "direct_choice", "items": 12, "samples_per_item": TOTAL_SAMPLES_PER_ITEM,
+            "initial_calls_per_model": 240, "schedule": "balanced_cyclic_rotations",
+            "prompt_instruction": PROMPT_INSTRUCTION, "answer_instruction": ANSWER_INSTRUCTION,
+            "rescue_instruction": RESCUE_INSTRUCTION, "strict_structured_output": True,
+        },
+        "stop_usd": {"priority_phase": str(PHASE_STOP_USD), "global": str(GLOBAL_STOP_USD)},
+        "current_observed_cost_usd": str(current_cost),
+        "prompt_token_reserve_per_phase": str(PROMPT_TOKEN_RESERVE),
+        "models": priority,
+    }
+    MANIFEST_JSON_PATH.parent.mkdir(parents=True, exist_ok=True)
+    MANIFEST_JSON_PATH.write_text(json.dumps(manifest, indent=2, sort_keys=True) + "\n")
+    lines = [
+        "# Direct-choice priority manifest, prepared but not dispatched",
+        "",
+        "This manifest prepares the reviewed direct-choice protocol for future panels. It queues and authorizes no API request. Dense-rated panels remain a separate legacy/proxy layer and cannot be mixed with these outputs in coordinates, family summaries, or capability fits.",
+        "",
+        "## Shared direct-choice identity",
+        "",
+        f"- saved catalog: `{CATALOG_PATH}`, SHA-256 `{catalog_sha}`",
+        "- 12 WVS items x 20 samples/item = 240 initial requests/model",
+        "- deterministic balanced cyclic rotations: exact option-position balance for n=2,4,10 and registered nearest 6/7 balance for n=3",
+        f"- prompt: `{PROMPT_INSTRUCTION}`",
+        f"- final response: `{ANSWER_INSTRUCTION}`",
+        f"- rescue response: `{RESCUE_INSTRUCTION}`",
+        "- strict structured output; each model has an isolated append-only ledger, cache, and model-specific protocol ID",
+        "",
+        "## Spend checks before any later dispatch",
+        "",
+        f"- observed provider cost across current rated and direct-choice ledgers: USD {current_cost:.11f}",
+        f"- priority phase hard stop: USD {PHASE_STOP_USD}; global hard stop: USD {GLOBAL_STOP_USD}",
+        f"- per-model reserve assumes 240 initial 1024-token completions plus 240 possible 2048-token rescues and {PROMPT_TOKEN_RESERVE} prompt tokens per phase; it is a pre-dispatch limit, not an observed cost",
+        "- the runner refuses a new model if current observed ledger cost plus its reserve reaches either stop",
+        "- no model below is dispatched by this commit",
+        "",
+        "## Ordered panels",
+        "",
+        "The order is Grok, OpenAI, Google, then Muse. `minimal` is used when catalog metadata advertises it; otherwise `low`; disabled is used only when the catalog says reasoning is optional and accepts `none`.",
+        "",
+        "| family | exact ID | created UTC | input USD/M | output USD/M | reasoning | structured | protocol ID | calls | completion-only ceiling | conservative reserve | isolated ledger |",
+        "|---|---|---:|---:|---:|---|---|---|---:|---:|---:|---|",
+    ]
+    for row in priority:
+        lines.append(
+            f"| {row['group']} | `{row['id']}` | {row['created_utc']} | {Decimal(row['input_usd_per_million']):g} | "
+            f"{Decimal(row['output_usd_per_million']):g} | `{json.dumps(row['reasoning'])}` ({row['reasoning_label']}) | "
+            f"{'yes' if row['structured_output'] else 'no'} | `{row['protocol_id']}` | {row['initial_calls']} | "
+            f"USD {Decimal(row['completion_only_ceiling_usd']):.4f} | USD {Decimal(row['conservative_reserve_usd']):.4f} | `{row['records_path']}` |"
+        )
+    lines.extend([
+        "",
+        "## Exclusions",
+        "",
+        "- Already plotted dense-rated IDs are not repeated in this prepared direct-choice list, including Grok 4.3/4.20, GPT-6 Astra, GPT-5.6 Sol, GPT-5.5, GPT-5.4, GPT-5.3 Chat, Gemini 3.7 Flash, Gemini 2.5 Pro, and Muse 1.3.",
+        "- GPT-5 Nano is retained as a completed dense-rated protocol diagnostic, not silently relabelled as a direct-choice panel.",
+        "- Pro/Fast, batch/free aliases, output price above USD 15/M, and code/image/audio/safeguard/multi-agent entries remain excluded. `Flash` is included where it is a general chat model.",
+        "- `openai/o4-mini-high` and `openai/o3-mini-high` are excluded because their catalog entries advertise only `high` reasoning, not the registered minimal/low policy.",
+        "- The deferred Qwen/GLM/Mistral shortlist remains outside this priority manifest until a direct-choice expansion decision is made.",
+        "",
+        "## Later execution only after review",
+        "",
+        "`scripts/wvs_direct_choice_priority.py --model <exact-id> --smoke` validates one saved entry without network requests. The corresponding `--run` is intentionally not invoked or queued here; it requires a reviewed manifest match and the spend checks above.",
+        "",
+        "-- PI[gpt-5.6-terra]",
+        "",
+    ])
+    MANIFEST_PATH.write_text("\n".join(lines))
+
+
+def preflight(model_id: str) -> dict:
+    priority = {row["id"]: row for row in entries()}
+    row = priority[model_id]
+    saved = json.loads(MANIFEST_JSON_PATH.read_text())
+    saved_row = next(entry for entry in saved["models"] if entry["id"] == model_id)
+    assert saved_row == row
+    assert saved["catalog_sha256"] == hashlib.sha256(CATALOG_PATH.read_bytes()).hexdigest()
+    current_cost = observed_cost()
+    reserve = Decimal(row["conservative_reserve_usd"])
+    assert current_cost + reserve < PHASE_STOP_USD
+    assert current_cost + reserve < GLOBAL_STOP_USD
+    return row
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--write-manifest", action="store_true")
+    parser.add_argument("--model", choices=[model_id for ids in GROUPS.values() for model_id in ids])
+    parser.add_argument("--smoke", action="store_true")
+    parser.add_argument("--run", action="store_true", help="make paid calls only after a separate review")
+    args = parser.parse_args()
+    if args.write_manifest:
+        write_manifest(entries())
+        print(f"wrote {MANIFEST_PATH} and {MANIFEST_JSON_PATH}")
+    if args.model is None:
+        assert not args.smoke and not args.run
+        return
+    row = preflight(args.model)
+    if args.smoke:
+        print(f"smoke: {row['id']}, 240 direct-choice requests, protocol={row['protocol_id']}")
+        print(f"smoke: reasoning={row['reasoning']}, reserve=USD {row['conservative_reserve_usd']}")
+    if not args.run:
+        return
+    request_plan = schedule(items())
+    records_path, cache_path = cache_paths(args.model)
+    result = read_items_direct_choice(
+        args.model, items(), samples_per_order=10, temperature=TEMPERATURE, max_tokens=MAX_TOKENS,
+        concurrency=CONCURRENCY, request_timeout=REQUEST_TIMEOUT, reasoning=row["reasoning"],
+        structured_output=True, records_path=records_path, cache_path=cache_path,
+        prompt_instruction=PROMPT_INSTRUCTION, answer_instruction=ANSWER_INSTRUCTION,
+        rescue_instruction=RESCUE_INSTRUCTION, plan_override=request_plan,
+    )
+    if result["cached"]:
+        print(f"priority direct-choice cache hit: {args.model}, protocol={result['protocol_id'][:12]}")
+        return
+    if not result["complete"]:
+        raise RuntimeError(f"incomplete priority direct-choice panel: {result['run_id']}; evidence is {records_path}")
+    print(f"complete priority direct-choice panel: {args.model}, run={result['run_id']}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/slop/research/wvs/20260917_direct_choice/priority_direct_choice_manifest.json b/slop/research/wvs/20260917_direct_choice/priority_direct_choice_manifest.json
new file mode 100644
index 0000000..a8330a0
--- /dev/null
+++ b/slop/research/wvs/20260917_direct_choice/priority_direct_choice_manifest.json
@@ -0,0 +1,690 @@
+{
+  "catalog_path": "slop/research/wvs/20260917_openrouter_models.json",
+  "catalog_sha256": "940edd61d8103a51322710d8a6baee7f698b64b2cf033a933f0321e15c536fd2",
+  "current_observed_cost_usd": "3.89710257235",
+  "design": {
+    "answer_instruction": "Respond with ONLY a JSON object with exactly one key named answer. Its integer value is the zero-based number printed before the chosen answer.",
+    "construct": "direct_choice",
+    "initial_calls_per_model": 240,
+    "items": 12,
+    "prompt_instruction": "Think briefly about which ONE answer best reflects the values expressed by your behavior as this AI assistant. Do not analyze the survey design or discuss lacking personal beliefs. Answer immediately.",
+    "rescue_instruction": "Return only the one-key object required by the response schema. No explanation.",
+    "samples_per_item": 20,
+    "schedule": "balanced_cyclic_rotations",
+    "strict_structured_output": true
+  },
+  "models": [
+    {
+      "cache_path": "slop/research/wvs/20260917_direct_choice/priority/x-ai__grok-4.6_cache.json",
+      "completion_only_ceiling_usd": "1.474560",
+      "conservative_reserve_usd": "4.915200",
+      "created_utc": "2026-08-12",
+      "group": "Grok",
+      "id": "x-ai/grok-4.6",
+      "initial_calls": 240,
+      "input_usd_per_million": "2.000000",
+      "output_usd_per_million": "6.000000",
+      "protocol_id": "f1dcc6e5e4651add7df5473e80ba5b5a5ec733d788a6a791625776501f8605d6",
+      "reasoning": {
+        "effort": "low"
+      },
+      "reasoning_label": "low",
+      "records_path": "slop/research/wvs/20260917_direct_choice/priority/x-ai__grok-4.6_requests.jsonl",
+      "structured_output": true
+    },
+    {
+      "cache_path": "slop/research/wvs/20260917_direct_choice/priority/x-ai__grok-4.5_cache.json",
+      "completion_only_ceiling_usd": "1.474560",
+      "conservative_reserve_usd": "4.915200",
+      "created_utc": "2026-07-08",
+      "group": "Grok",
+      "id": "x-ai/grok-4.5",
+      "initial_calls": 240,
+      "input_usd_per_million": "2.000000",
+      "output_usd_per_million": "6.000000",
+      "protocol_id": "fdb4ff16d7879cd840722b7f84654da2e67bc388cdf0b52ea64a1d12b487de18",
+      "reasoning": {
+        "effort": "low"
+      },
+      "reasoning_label": "low",
+      "records_path": "slop/research/wvs/20260917_direct_choice/priority/x-ai__grok-4.5_requests.jsonl",
+      "structured_output": true
+    },
+    {
+      "cache_path": "slop/research/wvs/20260917_direct_choice/priority/openai__gpt-5.6-luna_cache.json",
+      "completion_only_ceiling_usd": "0.2949120",
+      "conservative_reserve_usd": "0.9338880",
+      "created_utc": "2026-07-09",
+      "group": "OpenAI",
+      "id": "openai/gpt-5.6-luna",
+      "initial_calls": 240,
+      "input_usd_per_million": "0.2000000",
+      "output_usd_per_million": "1.2000000",
+      "protocol_id": "b9bd5e38934aeae851dcd6227403c474e41c52ebb0bf599b112203b30403e718",
+      "reasoning": {
+        "effort": "low"
+      },
+      "reasoning_label": "low",
+      "records_path": "slop/research/wvs/20260917_direct_choice/priority/openai__gpt-5.6-luna_requests.jsonl",
+      "structured_output": true
+    },
+    {
+      "cache_path": "slop/research/wvs/20260917_direct_choice/priority/openai__gpt-5.6-terra_cache.json",
+      "completion_only_ceiling_usd": "2.949120",
+      "conservative_reserve_usd": "9.338880",
+      "created_utc": "2026-07-09",
+      "group": "OpenAI",
+      "id": "openai/gpt-5.6-terra",
+      "initial_calls": 240,
+      "input_usd_per_million": "2.000000",
+      "output_usd_per_million": "12.000000",
+      "protocol_id": "76c8b34537d9ec301ec168c8566849a0a0d26fffa088b70b8da09acb760130b1",
+      "reasoning": {
+        "effort": "low"
+      },
+      "reasoning_label": "low",
+      "records_path": "slop/research/wvs/20260917_direct_choice/priority/openai__gpt-5.6-terra_requests.jsonl",
+      "structured_output": true
+    },
+    {
+      "cache_path": "slop/research/wvs/20260917_direct_choice/priority/openai__gpt-5.4-nano_cache.json",
+      "completion_only_ceiling_usd": "0.30720000",
+      "conservative_reserve_usd": "0.97075200",
+      "created_utc": "2026-03-17",
+      "group": "OpenAI",
+      "id": "openai/gpt-5.4-nano",
+      "initial_calls": 240,
+      "input_usd_per_million": "0.2000000",
+      "output_usd_per_million": "1.25000000",
+      "protocol_id": "a25533b578ada960251c7fa244a98b86e68a054346d641b6215888a09c92b09e",
+      "reasoning": {
+        "effort": "low"
+      },
+      "reasoning_label": "low",
+      "records_path": "slop/research/wvs/20260917_direct_choice/priority/openai__gpt-5.4-nano_requests.jsonl",
+      "structured_output": true
+    },
+    {
+      "cache_path": "slop/research/wvs/20260917_direct_choice/priority/openai__gpt-5.4-mini_cache.json",
+      "completion_only_ceiling_usd": "1.1059200",
+      "conservative_reserve_usd": "3.50208000",
+      "created_utc": "2026-03-17",
+      "group": "OpenAI",
+      "id": "openai/gpt-5.4-mini",
+      "initial_calls": 240,
+      "input_usd_per_million": "0.75000000",
+      "output_usd_per_million": "4.5000000",
+      "protocol_id": "42e8c091233072fd0925ab8f5554bc01e23c5cf3051e09ba7b45970af189fb10",
+      "reasoning": {
+        "effort": "low"
+      },
+      "reasoning_label": "low",
+      "records_path": "slop/research/wvs/20260917_direct_choice/priority/openai__gpt-5.4-mini_requests.jsonl",
+      "structured_output": true
+    },
+    {
+      "cache_path": "slop/research/wvs/20260917_direct_choice/priority/openai__gpt-5.2-chat_cache.json",
+      "completion_only_ceiling_usd": "3.440640",
+      "conservative_reserve_usd": "10.75200000",
+      "created_utc": "2025-12-10",
+      "group": "OpenAI",
+      "id": "openai/gpt-5.2-chat",
+      "initial_calls": 240,
+      "input_usd_per_million": "1.75000000",
+      "output_usd_per_million": "14.000000",
+      "protocol_id": "6911c78f8c33d085e4d2e4a627c8e311059855cfb859b16a6a319185b6abb807",
+      "reasoning": null,
+      "reasoning_label": "not advertised",
+      "records_path": "slop/research/wvs/20260917_direct_choice/priority/openai__gpt-5.2-chat_requests.jsonl",
+      "structured_output": true
+    },
+    {
+      "cache_path": "slop/research/wvs/20260917_direct_choice/priority/openai__gpt-5.2_cache.json",
+      "completion_only_ceiling_usd": "3.440640",
+      "conservative_reserve_usd": "10.75200000",
+      "created_utc": "2025-12-10",
+      "group": "OpenAI",
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+      "conservative_reserve_usd": "0.24330240",
+      "created_utc": "2026-04-03",
+      "group": "Google",
+      "id": "google/gemma-4-26b-a4b-it",
+      "initial_calls": 240,
+      "input_usd_per_million": "0.09000000",
+      "output_usd_per_million": "0.3000000",
+      "protocol_id": "08d96b55bfee15010d2b9053f40c21d8e1d23d912ef84c645eb810eaa8803f1a",
+      "reasoning": null,
+      "reasoning_label": "not advertised (optional; omitted)",
+      "records_path": "slop/research/wvs/20260917_direct_choice/priority/google__gemma-4-26b-a4b-it_requests.jsonl",
+      "structured_output": true
+    },
+    {
+      "cache_path": "slop/research/wvs/20260917_direct_choice/priority/google__gemini-3.1-flash-lite-preview_cache.json",
+      "completion_only_ceiling_usd": "0.3686400",
+      "conservative_reserve_usd": "1.16736000",
+      "created_utc": "2026-03-03",
+      "group": "Google",
+      "id": "google/gemini-3.1-flash-lite-preview",
+      "initial_calls": 240,
+      "input_usd_per_million": "0.25000000",
+      "output_usd_per_million": "1.5000000",
+      "protocol_id": "4a85c0754b9bd2903c6cc978b2ded49ca381bc0e90199bff6eaf53e42d03e879",
+      "reasoning": {
+        "effort": "minimal"
+      },
+      "reasoning_label": "minimal",
+      "records_path": "slop/research/wvs/20260917_direct_choice/priority/google__gemini-3.1-flash-lite-preview_requests.jsonl",
+      "structured_output": true
+    },
+    {
+      "cache_path": "slop/research/wvs/20260917_direct_choice/priority/google__gemini-3-flash-preview_cache.json",
+      "completion_only_ceiling_usd": "0.737280",
+      "conservative_reserve_usd": "2.3347200",
+      "created_utc": "2025-12-17",
+      "group": "Google",
+      "id": "google/gemini-3-flash-preview",
+      "initial_calls": 240,
+      "input_usd_per_million": "0.5000000",
+      "output_usd_per_million": "3.000000",
+      "protocol_id": "1c64e2725648b42570c0c0bd443655de81ee55c0477bdc835ff4c1087a76cded",
+      "reasoning": {
+        "effort": "minimal"
+      },
+      "reasoning_label": "minimal",
+      "records_path": "slop/research/wvs/20260917_direct_choice/priority/google__gemini-3-flash-preview_requests.jsonl",
+      "structured_output": true
+    },
+    {
+      "cache_path": "slop/research/wvs/20260917_direct_choice/priority/google__gemini-2.5-flash-lite_cache.json",
+      "completion_only_ceiling_usd": "0.0983040",
+      "conservative_reserve_usd": "0.3194880",
+      "created_utc": "2025-07-22",
+      "group": "Google",
+      "id": "google/gemini-2.5-flash-lite",
+      "initial_calls": 240,
+      "input_usd_per_million": "0.1000000",
+      "output_usd_per_million": "0.4000000",
+      "protocol_id": "f5dbbdf216a4373df531ec7fef4a02fcec6e89609aa13553bb1f903dc8e0a43b",
+      "reasoning": null,
+      "reasoning_label": "not advertised (optional; omitted)",
+      "records_path": "slop/research/wvs/20260917_direct_choice/priority/google__gemini-2.5-flash-lite_requests.jsonl",
+      "structured_output": true
+    },
+    {
+      "cache_path": "slop/research/wvs/20260917_direct_choice/priority/google__gemini-2.5-flash_cache.json",
+      "completion_only_ceiling_usd": "0.6144000",
+      "conservative_reserve_usd": "1.9169280",
+      "created_utc": "2025-06-17",
+      "group": "Google",
+      "id": "google/gemini-2.5-flash",
+      "initial_calls": 240,
+      "input_usd_per_million": "0.3000000",
+      "output_usd_per_million": "2.5000000",
+      "protocol_id": "4d8587d1f7a72ee96a78e2693aec222cba6d313c5fcc978b82b27508d736a223",
+      "reasoning": null,
+      "reasoning_label": "not advertised (optional; omitted)",
+      "records_path": "slop/research/wvs/20260917_direct_choice/priority/google__gemini-2.5-flash_requests.jsonl",
+      "structured_output": true
+    },
+    {
+      "cache_path": "slop/research/wvs/20260917_direct_choice/priority/meta__muse-spark-1.2_cache.json",
+      "completion_only_ceiling_usd": "1.04448000",
+      "conservative_reserve_usd": "3.44064000",
+      "created_utc": "2026-08-05",
+      "group": "Muse",
+      "id": "meta/muse-spark-1.2",
+      "initial_calls": 240,
+      "input_usd_per_million": "1.25000000",
+      "output_usd_per_million": "4.25000000",
+      "protocol_id": "a716da6000adc3d9792e112c55d516e21c49fbe46acac6517d8b740e81b30e69",
+      "reasoning": {
+        "effort": "minimal"
+      },
+      "reasoning_label": "minimal",
+      "records_path": "slop/research/wvs/20260917_direct_choice/priority/meta__muse-spark-1.2_requests.jsonl",
+      "structured_output": true
+    },
+    {
+      "cache_path": "slop/research/wvs/20260917_direct_choice/priority/meta__muse-spark-1.1_cache.json",
+      "completion_only_ceiling_usd": "1.04448000",
+      "conservative_reserve_usd": "3.44064000",
+      "created_utc": "2026-07-16",
+      "group": "Muse",
+      "id": "meta/muse-spark-1.1",
+      "initial_calls": 240,
+      "input_usd_per_million": "1.25000000",
+      "output_usd_per_million": "4.25000000",
+      "protocol_id": "c4cd8eedebc35b380c52d7c559f87c293623a04858b8acfeb091145340dde72d",
+      "reasoning": {
+        "effort": "minimal"
+      },
+      "reasoning_label": "minimal",
+      "records_path": "slop/research/wvs/20260917_direct_choice/priority/meta__muse-spark-1.1_requests.jsonl",
+      "structured_output": true
+    }
+  ],
+  "prompt_token_reserve_per_phase": "512",
+  "schema": 1,
+  "stop_usd": {
+    "global": "80",
+    "priority_phase": "35"
+  }
+}
diff --git a/slop/research/wvs/20260917_direct_choice/priority_direct_choice_manifest.md b/slop/research/wvs/20260917_direct_choice/priority_direct_choice_manifest.md
new file mode 100644
index 0000000..7941442
--- /dev/null
+++ b/slop/research/wvs/20260917_direct_choice/priority_direct_choice_manifest.md
@@ -0,0 +1,81 @@
+# Direct-choice priority manifest, prepared but not dispatched
+
+This manifest prepares the reviewed direct-choice protocol for future panels. It queues and authorizes no API request. Dense-rated panels remain a separate legacy/proxy layer and cannot be mixed with these outputs in coordinates, family summaries, or capability fits.
+
+## Shared direct-choice identity
+
+- saved catalog: `slop/research/wvs/20260917_openrouter_models.json`, SHA-256 `940edd61d8103a51322710d8a6baee7f698b64b2cf033a933f0321e15c536fd2`
+- 12 WVS items x 20 samples/item = 240 initial requests/model
+- deterministic balanced cyclic rotations: exact option-position balance for n=2,4,10 and registered nearest 6/7 balance for n=3
+- prompt: `Think briefly about which ONE answer best reflects the values expressed by your behavior as this AI assistant. Do not analyze the survey design or discuss lacking personal beliefs. Answer immediately.`
+- final response: `Respond with ONLY a JSON object with exactly one key named answer. Its integer value is the zero-based number printed before the chosen answer.`
+- rescue response: `Return only the one-key object required by the response schema. No explanation.`
+- strict structured output; each model has an isolated append-only ledger, cache, and model-specific protocol ID
+
+## Spend checks before any later dispatch
+
+- observed provider cost across current rated and direct-choice ledgers: USD 3.89710257235
+- priority phase hard stop: USD 35; global hard stop: USD 80
+- per-model reserve assumes 240 initial 1024-token completions plus 240 possible 2048-token rescues and 512 prompt tokens per phase; it is a pre-dispatch limit, not an observed cost
+- the runner refuses a new model if current observed ledger cost plus its reserve reaches either stop
+- no model below is dispatched by this commit
+
+## Ordered panels
+
+The order is Grok, OpenAI, Google, then Muse. `minimal` is used when catalog metadata advertises it; otherwise `low`; disabled is used only when the catalog says reasoning is optional and accepts `none`.
+
+| family | exact ID | created UTC | input USD/M | output USD/M | reasoning | structured | protocol ID | calls | completion-only ceiling | conservative reserve | isolated ledger |
+|---|---|---:|---:|---:|---|---|---|---:|---:|---:|---|
+| Grok | `x-ai/grok-4.6` | 2026-08-12 | 2.000000 | 6.000000 | `{"effort": "low"}` (low) | yes | `f1dcc6e5e4651add7df5473e80ba5b5a5ec733d788a6a791625776501f8605d6` | 240 | USD 1.4746 | USD 4.9152 | `slop/research/wvs/20260917_direct_choice/priority/x-ai__grok-4.6_requests.jsonl` |
+| Grok | `x-ai/grok-4.5` | 2026-07-08 | 2.000000 | 6.000000 | `{"effort": "low"}` (low) | yes | `fdb4ff16d7879cd840722b7f84654da2e67bc388cdf0b52ea64a1d12b487de18` | 240 | USD 1.4746 | USD 4.9152 | `slop/research/wvs/20260917_direct_choice/priority/x-ai__grok-4.5_requests.jsonl` |
+| OpenAI | `openai/gpt-5.6-luna` | 2026-07-09 | 0.2000000 | 1.2000000 | `{"effort": "low"}` (low) | yes | `b9bd5e38934aeae851dcd6227403c474e41c52ebb0bf599b112203b30403e718` | 240 | USD 0.2949 | USD 0.9339 | `slop/research/wvs/20260917_direct_choice/priority/openai__gpt-5.6-luna_requests.jsonl` |
+| OpenAI | `openai/gpt-5.6-terra` | 2026-07-09 | 2.000000 | 12.000000 | `{"effort": "low"}` (low) | yes | `76c8b34537d9ec301ec168c8566849a0a0d26fffa088b70b8da09acb760130b1` | 240 | USD 2.9491 | USD 9.3389 | `slop/research/wvs/20260917_direct_choice/priority/openai__gpt-5.6-terra_requests.jsonl` |
+| OpenAI | `openai/gpt-5.4-nano` | 2026-03-17 | 0.2000000 | 1.25000000 | `{"effort": "low"}` (low) | yes | `a25533b578ada960251c7fa244a98b86e68a054346d641b6215888a09c92b09e` | 240 | USD 0.3072 | USD 0.9708 | `slop/research/wvs/20260917_direct_choice/priority/openai__gpt-5.4-nano_requests.jsonl` |
+| OpenAI | `openai/gpt-5.4-mini` | 2026-03-17 | 0.75000000 | 4.5000000 | `{"effort": "low"}` (low) | yes | `42e8c091233072fd0925ab8f5554bc01e23c5cf3051e09ba7b45970af189fb10` | 240 | USD 1.1059 | USD 3.5021 | `slop/research/wvs/20260917_direct_choice/priority/openai__gpt-5.4-mini_requests.jsonl` |
+| OpenAI | `openai/gpt-5.2-chat` | 2025-12-10 | 1.75000000 | 14.000000 | `null` (not advertised) | yes | `6911c78f8c33d085e4d2e4a627c8e311059855cfb859b16a6a319185b6abb807` | 240 | USD 3.4406 | USD 10.7520 | `slop/research/wvs/20260917_direct_choice/priority/openai__gpt-5.2-chat_requests.jsonl` |
+| OpenAI | `openai/gpt-5.2` | 2025-12-10 | 1.75000000 | 14.000000 | `{"effort": "low"}` (low) | yes | `6d72c036f9f413fa500c0a2902750b0a2c29fc1145d77d3fa02ecf89b48054ff` | 240 | USD 3.4406 | USD 10.7520 | `slop/research/wvs/20260917_direct_choice/priority/openai__gpt-5.2_requests.jsonl` |
+| OpenAI | `openai/gpt-5.1` | 2025-11-13 | 1.25000000 | 10.00000 | `{"effort": "low"}` (low) | yes | `063cbfba0812ff35df231154d69a498e77cd240961d61f18913e6465c1ac1938` | 240 | USD 2.4576 | USD 7.6800 | `slop/research/wvs/20260917_direct_choice/priority/openai__gpt-5.1_requests.jsonl` |
+| OpenAI | `openai/gpt-5` | 2025-08-07 | 1.25000000 | 10.00000 | `{"effort": "minimal"}` (minimal) | yes | `48ffd6a149a5583355dd779b855e944ba26e4eab9b9cd4b5acd780cb3e3fb4e1` | 240 | USD 2.4576 | USD 7.6800 | `slop/research/wvs/20260917_direct_choice/priority/openai__gpt-5_requests.jsonl` |
+| OpenAI | `openai/gpt-5-mini` | 2025-08-07 | 0.25000000 | 2.000000 | `{"effort": "minimal"}` (minimal) | yes | `00e8beddc47977c22afa2128d78af2c00acf8ddb87077f427bcb1360ed87a980` | 240 | USD 0.4915 | USD 1.5360 | `slop/research/wvs/20260917_direct_choice/priority/openai__gpt-5-mini_requests.jsonl` |
+| OpenAI | `openai/gpt-oss-120b` | 2025-08-05 | 0.037000000 | 0.17000000 | `{"effort": "low"}` (low) | yes | `25ac3627e4795573424e184ab883ed80e3abb3bfadb0adba94a8e5b1ce659725` | 240 | USD 0.0418 | USD 0.1344 | `slop/research/wvs/20260917_direct_choice/priority/openai__gpt-oss-120b_requests.jsonl` |
+| OpenAI | `openai/gpt-oss-20b` | 2025-08-05 | 0.03000000 | 0.13000000 | `{"effort": "low"}` (low) | yes | `06243dd30d6fb69a30c4b24a9b891b95a25975d7d41b7071a474e4365e856169` | 240 | USD 0.0319 | USD 0.1032 | `slop/research/wvs/20260917_direct_choice/priority/openai__gpt-oss-20b_requests.jsonl` |
+| OpenAI | `openai/o3` | 2025-04-16 | 2.000000 | 8.000000 | `null` (not advertised (optional; omitted)) | yes | `f4054a623913b60fa79beee9aacec15967aa058434835937e4e8af276adf9557` | 240 | USD 1.9661 | USD 6.3898 | `slop/research/wvs/20260917_direct_choice/priority/openai__o3_requests.jsonl` |
+| OpenAI | `openai/o4-mini` | 2025-04-16 | 1.1000000 | 4.4000000 | `null` (not advertised (optional; omitted)) | yes | `26873a2a9f3d6530a06368113b903fbc80f673849ed605f1f49963f1f7abb638` | 240 | USD 1.0813 | USD 3.5144 | `slop/research/wvs/20260917_direct_choice/priority/openai__o4-mini_requests.jsonl` |
+| OpenAI | `openai/gpt-4.1` | 2025-04-14 | 2.000000 | 8.000000 | `null` (not advertised) | yes | `9a3bb10b7179275010d35313de4a9d8021ca42abb0901414450ef0c6a42f2aea` | 240 | USD 1.9661 | USD 6.3898 | `slop/research/wvs/20260917_direct_choice/priority/openai__gpt-4.1_requests.jsonl` |
+| OpenAI | `openai/gpt-4.1-mini` | 2025-04-14 | 0.4000000 | 1.6000000 | `null` (not advertised) | yes | `59833d4bac77233732006cfada3136d655274a1fba463a994c4bd3107348b642` | 240 | USD 0.3932 | USD 1.2780 | `slop/research/wvs/20260917_direct_choice/priority/openai__gpt-4.1-mini_requests.jsonl` |
+| OpenAI | `openai/gpt-4.1-nano` | 2025-04-14 | 0.1000000 | 0.4000000 | `null` (not advertised) | yes | `91b5e653465ea8562e62d0a5c76895e4dfa9cc774dc777f04268be6643f04cb9` | 240 | USD 0.0983 | USD 0.3195 | `slop/research/wvs/20260917_direct_choice/priority/openai__gpt-4.1-nano_requests.jsonl` |
+| OpenAI | `openai/o3-mini` | 2025-01-31 | 1.1000000 | 4.4000000 | `null` (not advertised (optional; omitted)) | yes | `2a0f497f04f98ce1a6ff53412018721609b993a091a0da4e7c339b5a72b1859c` | 240 | USD 1.0813 | USD 3.5144 | `slop/research/wvs/20260917_direct_choice/priority/openai__o3-mini_requests.jsonl` |
+| OpenAI | `openai/gpt-4o-2024-11-20` | 2024-11-20 | 2.5000000 | 10.00000 | `null` (not advertised) | yes | `e558a7bb4fdfbf9c4ddc6cd2f7505850a0779b7f8c02cde480dff902e709674b` | 240 | USD 2.4576 | USD 7.9872 | `slop/research/wvs/20260917_direct_choice/priority/openai__gpt-4o-2024-11-20_requests.jsonl` |
+| OpenAI | `openai/gpt-4o-2024-08-06` | 2024-08-06 | 2.5000000 | 10.00000 | `null` (not advertised) | yes | `ac49e31fe7abf39eadc966e042c72652b063d56d698cf437b76c53411e0a6979` | 240 | USD 2.4576 | USD 7.9872 | `slop/research/wvs/20260917_direct_choice/priority/openai__gpt-4o-2024-08-06_requests.jsonl` |
+| OpenAI | `openai/gpt-4o-mini` | 2024-07-18 | 0.15000000 | 0.6000000 | `null` (not advertised) | yes | `1662dce1e899d6b1060b2dfaa19034935ff097e77114ae42936eb5b52ddc7296` | 240 | USD 0.1475 | USD 0.4792 | `slop/research/wvs/20260917_direct_choice/priority/openai__gpt-4o-mini_requests.jsonl` |
+| OpenAI | `openai/gpt-4o` | 2024-05-13 | 2.5000000 | 10.00000 | `null` (not advertised) | yes | `11a70a549953db006a77522bc587bcf9464f9c601afc7b3d09b0119ade903d99` | 240 | USD 2.4576 | USD 7.9872 | `slop/research/wvs/20260917_direct_choice/priority/openai__gpt-4o_requests.jsonl` |
+| OpenAI | `openai/gpt-3.5-turbo-0613` | 2024-01-25 | 1.000000 | 2.000000 | `null` (not advertised) | yes | `3b8af120356177e5159f76ee1736a89695006f54cd3055b2c2588ec9050374c6` | 240 | USD 0.4915 | USD 1.7203 | `slop/research/wvs/20260917_direct_choice/priority/openai__gpt-3.5-turbo-0613_requests.jsonl` |
+| OpenAI | `openai/gpt-3.5-turbo-instruct` | 2023-09-28 | 1.5000000 | 2.000000 | `null` (not advertised) | yes | `74b3ad3c9d06e892a0c08968419e330a7a8a91e5db7393e23c31f41c97139967` | 240 | USD 0.4915 | USD 1.8432 | `slop/research/wvs/20260917_direct_choice/priority/openai__gpt-3.5-turbo-instruct_requests.jsonl` |
+| OpenAI | `openai/gpt-3.5-turbo-16k` | 2023-08-28 | 3.000000 | 4.000000 | `null` (not advertised) | yes | `07953ad5d1b968e61ff14f3ef8337e407d128295918855be42d7a0993acb0e32` | 240 | USD 0.9830 | USD 3.6864 | `slop/research/wvs/20260917_direct_choice/priority/openai__gpt-3.5-turbo-16k_requests.jsonl` |
+| OpenAI | `openai/gpt-3.5-turbo` | 2023-05-28 | 0.5000000 | 1.5000000 | `null` (not advertised) | yes | `446953742af525795631082baf5f3df86027c1710d1003ca6054f6e2a48dc18f` | 240 | USD 0.3686 | USD 1.2288 | `slop/research/wvs/20260917_direct_choice/priority/openai__gpt-3.5-turbo_requests.jsonl` |
+| Google | `google/gemini-3.8-flash` | 2026-09-02 | 0.75000000 | 3.75000000 | `{"effort": "low"}` (low) | yes | `1e42dbfead6255ca8efab8f522b5c7684cd8eec6035059bfdf1fdac5d40dc3ae` | 240 | USD 0.9216 | USD 2.9491 | `slop/research/wvs/20260917_direct_choice/priority/google__gemini-3.8-flash_requests.jsonl` |
+| Google | `google/gemini-3.6-flash` | 2026-07-21 | 0.75000000 | 3.75000000 | `{"effort": "minimal"}` (minimal) | yes | `4524a90e3320fb15103d7468b5e8088ab99c73051539240d0c1ae14d353d7eac` | 240 | USD 0.9216 | USD 2.9491 | `slop/research/wvs/20260917_direct_choice/priority/google__gemini-3.6-flash_requests.jsonl` |
+| Google | `google/gemini-3.5-flash-lite` | 2026-07-21 | 0.3000000 | 2.5000000 | `{"effort": "minimal"}` (minimal) | yes | `0f44f6e4ebb50ece2ada37707d79b988d36fc55a207f23235bb38740e7726b01` | 240 | USD 0.6144 | USD 1.9169 | `slop/research/wvs/20260917_direct_choice/priority/google__gemini-3.5-flash-lite_requests.jsonl` |
+| Google | `google/gemini-3.5-flash` | 2026-05-19 | 1.5000000 | 9.000000 | `{"effort": "minimal"}` (minimal) | yes | `fab233d2ad1205b7fa4c2346875105509219846ceeeb7fd292d209615473c231` | 240 | USD 2.2118 | USD 7.0042 | `slop/research/wvs/20260917_direct_choice/priority/google__gemini-3.5-flash_requests.jsonl` |
+| Google | `google/gemini-3.1-flash-lite` | 2026-05-07 | 0.25000000 | 1.5000000 | `{"effort": "minimal"}` (minimal) | yes | `c325d9a280a083263f3705cf8d7fa8d4733877cb733526c2fe2290948d3ac4a4` | 240 | USD 0.3686 | USD 1.1674 | `slop/research/wvs/20260917_direct_choice/priority/google__gemini-3.1-flash-lite_requests.jsonl` |
+| Google | `google/gemma-4-26b-a4b-it` | 2026-04-03 | 0.09000000 | 0.3000000 | `null` (not advertised (optional; omitted)) | yes | `08d96b55bfee15010d2b9053f40c21d8e1d23d912ef84c645eb810eaa8803f1a` | 240 | USD 0.0737 | USD 0.2433 | `slop/research/wvs/20260917_direct_choice/priority/google__gemma-4-26b-a4b-it_requests.jsonl` |
+| Google | `google/gemini-3.1-flash-lite-preview` | 2026-03-03 | 0.25000000 | 1.5000000 | `{"effort": "minimal"}` (minimal) | yes | `4a85c0754b9bd2903c6cc978b2ded49ca381bc0e90199bff6eaf53e42d03e879` | 240 | USD 0.3686 | USD 1.1674 | `slop/research/wvs/20260917_direct_choice/priority/google__gemini-3.1-flash-lite-preview_requests.jsonl` |
+| Google | `google/gemini-3-flash-preview` | 2025-12-17 | 0.5000000 | 3.000000 | `{"effort": "minimal"}` (minimal) | yes | `1c64e2725648b42570c0c0bd443655de81ee55c0477bdc835ff4c1087a76cded` | 240 | USD 0.7373 | USD 2.3347 | `slop/research/wvs/20260917_direct_choice/priority/google__gemini-3-flash-preview_requests.jsonl` |
+| Google | `google/gemini-2.5-flash-lite` | 2025-07-22 | 0.1000000 | 0.4000000 | `null` (not advertised (optional; omitted)) | yes | `f5dbbdf216a4373df531ec7fef4a02fcec6e89609aa13553bb1f903dc8e0a43b` | 240 | USD 0.0983 | USD 0.3195 | `slop/research/wvs/20260917_direct_choice/priority/google__gemini-2.5-flash-lite_requests.jsonl` |
+| Google | `google/gemini-2.5-flash` | 2025-06-17 | 0.3000000 | 2.5000000 | `null` (not advertised (optional; omitted)) | yes | `4d8587d1f7a72ee96a78e2693aec222cba6d313c5fcc978b82b27508d736a223` | 240 | USD 0.6144 | USD 1.9169 | `slop/research/wvs/20260917_direct_choice/priority/google__gemini-2.5-flash_requests.jsonl` |
+| Muse | `meta/muse-spark-1.2` | 2026-08-05 | 1.25000000 | 4.25000000 | `{"effort": "minimal"}` (minimal) | yes | `a716da6000adc3d9792e112c55d516e21c49fbe46acac6517d8b740e81b30e69` | 240 | USD 1.0445 | USD 3.4406 | `slop/research/wvs/20260917_direct_choice/priority/meta__muse-spark-1.2_requests.jsonl` |
+| Muse | `meta/muse-spark-1.1` | 2026-07-16 | 1.25000000 | 4.25000000 | `{"effort": "minimal"}` (minimal) | yes | `c4cd8eedebc35b380c52d7c559f87c293623a04858b8acfeb091145340dde72d` | 240 | USD 1.0445 | USD 3.4406 | `slop/research/wvs/20260917_direct_choice/priority/meta__muse-spark-1.1_requests.jsonl` |
+
+## Exclusions
+
+- Already plotted dense-rated IDs are not repeated in this prepared direct-choice list, including Grok 4.3/4.20, GPT-6 Astra, GPT-5.6 Sol, GPT-5.5, GPT-5.4, GPT-5.3 Chat, Gemini 3.7 Flash, Gemini 2.5 Pro, and Muse 1.3.
+- GPT-5 Nano is retained as a completed dense-rated protocol diagnostic, not silently relabelled as a direct-choice panel.
+- Pro/Fast, batch/free aliases, output price above USD 15/M, and code/image/audio/safeguard/multi-agent entries remain excluded. `Flash` is included where it is a general chat model.
+- `openai/o4-mini-high` and `openai/o3-mini-high` are excluded because their catalog entries advertise only `high` reasoning, not the registered minimal/low policy.
+- The deferred Qwen/GLM/Mistral shortlist remains outside this priority manifest until a direct-choice expansion decision is made.
+
+## Later execution only after review
+
+`scripts/wvs_direct_choice_priority.py --model <exact-id> --smoke` validates one saved entry without network requests. The corresponding `--run` is intentionally not invoked or queued here; it requires a reviewed manifest match and the spend checks above.
+
+-- PI[gpt-5.6-terra]
diff --git a/slop/research/wvs/20260917_direct_choice/priority_preflight_smoke.log b/slop/research/wvs/20260917_direct_choice/priority_preflight_smoke.log
new file mode 100644
index 0000000..15a02ff
--- /dev/null
+++ b/slop/research/wvs/20260917_direct_choice/priority_preflight_smoke.log
@@ -0,0 +1,16 @@
+$ python scripts/wvs_direct_choice_priority.py --write-manifest
+wrote slop/research/wvs/20260917_direct_choice/priority_direct_choice_manifest.md and slop/research/wvs/20260917_direct_choice/priority_direct_choice_manifest.json
+$ python scripts/wvs_direct_choice_priority.py --model x-ai/grok-4.6 --smoke
+smoke: x-ai/grok-4.6, 240 direct-choice requests, protocol=f1dcc6e5e4651add7df5473e80ba5b5a5ec733d788a6a791625776501f8605d6
+smoke: reasoning={'effort': 'low'}, reserve=USD 4.915200
+$ python scripts/wvs_direct_choice_priority.py --model openai/gpt-5.2-chat --smoke
+smoke: openai/gpt-5.2-chat, 240 direct-choice requests, protocol=6911c78f8c33d085e4d2e4a627c8e311059855cfb859b16a6a319185b6abb807
+smoke: reasoning=None, reserve=USD 10.75200000
+$ python scripts/wvs_direct_choice_priority.py --model google/gemini-3.6-flash --smoke
+smoke: google/gemini-3.6-flash, 240 direct-choice requests, protocol=4524a90e3320fb15103d7468b5e8088ab99c73051539240d0c1ae14d353d7eac
+smoke: reasoning={'effort': 'minimal'}, reserve=USD 2.94912000
+$ python scripts/wvs_direct_choice_priority.py --model meta/muse-spark-1.2 --smoke
+smoke: meta/muse-spark-1.2, 240 direct-choice requests, protocol=a716da6000adc3d9792e112c55d516e21c49fbe46acac6517d8b740e81b30e69
+smoke: reasoning={'effort': 'minimal'}, reserve=USD 3.44064000
+verified manifest: 39 unique structured direct-choice protocols, 240 calls each, output <= USD 15/M
+wrapper syntax: valid; no --run execution invoked
diff --git a/src/moralmaps/read_direct_choice.py b/src/moralmaps/read_direct_choice.py
index 34b94c0..f659b21 100644
--- a/src/moralmaps/read_direct_choice.py
+++ b/src/moralmaps/read_direct_choice.py
@@ -188,7 +188,7 @@ def read_items_direct_choice(model: str, items: list[dict], *, samples_per_order
     """
     assert samples_per_order > 0
     assert temperature > 0
-    assert reasoning == {"effort": "low"}, "the registered Gemini pilot uses catalog-supported low reasoning"
+    assert reasoning is None or reasoning == {"enabled": False} or reasoning.get("effort") in {"minimal", "low"}
     assert structured_output
     plan = _plan(items, samples_per_order, answer_instruction) if plan_override is None else plan_override
     protocol_id = direct_choice_protocol_identity(