Add the Meta Muse Wonder model card and metadata

app/src/modelMeta.ts

@@ -169,6 +169,7 @@ export const MODEL_RELEASE_DATES: Record<string, string> = {
   "qwen-3.7-max": "2026-05-19",
   "qwen3.8-max": "2026-08-03",
   inkling: "2026-07-15",
+  "muse-wonder": "2026-10-12",
 };
 
 export const PROVIDER_LABELS: Record<ProviderKey, string> = {

policybench/config.py

@@ -107,6 +107,12 @@
     "qwen-3.7-max": "openrouter/qwen/qwen3.7-max",
     "qwen3.8-max": "openrouter/qwen/qwen3.8-max",
     "inkling": "openrouter/thinkingmachines/inkling",
+    # Meta Muse Wonder (2026-10-12). Meta's own Model API serves it as
+    # muse-wonder-1.0; PolicyBench holds no Meta key, and OpenRouter's one
+    # endpoint for the id is Meta itself (provider "Meta", serving
+    # meta/muse-wonder-20261012; openrouter.ai/api/v1/models/meta/
+    # muse-wonder/endpoints, read 2026-10-12).
+    "muse-wonder": "openrouter/meta/muse-wonder",
 }
 
 # Configured per-1M-token USD list prices, keyed by the predictions.csv.gz model
@@ -161,6 +167,12 @@
     # inkling: $1 / $4.05 per 1M input/output tokens (OpenRouter model
     # listing, 2026-08-03); litellm's price map does not yet cover the id.
     "inkling": {"input": 1.0, "output": 4.05},
+    # muse-wonder: $4 / $20 per 1M input/output tokens, $0.20 cached input,
+    # one rate at every prompt length (Meta Model API pricing in Meta's
+    # launch post, dev.meta.ai/resources/blog/build-with-muse-wonder, and
+    # the OpenRouter listing, both read 2026-10-12); litellm's price map
+    # does not yet cover the id.
+    "muse-wonder": {"input": 4.0, "output": 20.0, "cache_read": 0.20},
     # gemini-3.6-flash: $1.50 / $7.50 per 1M input/output tokens (Google
     # launch pricing, 2026-07-21); litellm's price map does not yet cover it.
     "gemini-3.6-flash": {"input": 1.5, "output": 7.5},

policybench/model_cards.py

@@ -574,6 +574,33 @@ class ModelCard:
             "$1/$4.05 per 1M on OpenRouter."
         ),
     ),
+    "openrouter/meta/muse-wonder": ModelCard(
+        litellm_id="openrouter/meta/muse-wonder",
+        answer_contract="json",
+        thinking_budget=True,
+        expected_cost_per_scenario_usd=0.059,
+        notes=(
+            "Onboarded 2026-10-13, the day after Meta released it. OpenRouter "
+            "lists one endpoint, Meta's own (meta/muse-wonder-20261012), so "
+            "OpenRouter passes requests to Meta's API. That API accepts "
+            "tool_choice 'auto' only: a forced (named-function) or 'required' "
+            'tool choice returns a 400 (\'only "auto" is supported for '
+            "tool_choice'), so the row runs the JSON contract whole-scenario, "
+            "as Opus 5.5 and Sonnet 5.5 do. A request with no reasoning "
+            "parameter reasons at the provider default: on a one-question "
+            "probe, 1,913 and 3,089 of 1,991 and 3,181 completion tokens with "
+            "no tools, and 2,189 and 2,635 of 2,352 and 2,815 with tool_choice "
+            "auto; it returns a short reasoning summary and an encrypted "
+            "trace. No reasoning-suppression sensitivity run is needed: the "
+            "row already reasons on the JSON contract, as Opus 5.5's and "
+            "Sonnet 5.5's do. Gauntlet: without the reasoning budget the "
+            "3-variable JSON probe stopped at the token limit (384 tokens); "
+            "with it, 3/3 and 16/16 parsed on the JSON contract (2,402 and "
+            "2,736 completion tokens, 15s and 18s). The gauntlet's cost "
+            "estimate is $0.059 per scenario at $4/$20 per 1M with $0.20 "
+            "cache reads."
+        ),
+    ),
     "openrouter/z-ai/glm-5.2": ModelCard(
         litellm_id="openrouter/z-ai/glm-5.2",
         answer_contract="json",

policybench/paper_results.py

@@ -3093,6 +3093,13 @@ def top_failure_subtypes(self, n: int = 3) -> str:
     # techcrunch.com 2026-07-15; weights on Hugging Face the same day
     # under Apache 2.0 (Thinking Machines Lab's first from-scratch model)
     "inkling": "2026-07-15",
+    # techcrunch.com 2026-10-12 ("Meta releases its first frontier model,
+    # Muse Wonder") and venturebeat.com the same day; on Meta's Model API
+    # (muse-wonder-1.0) and OpenRouter (meta/muse-wonder, listed
+    # 2026-10-12) from launch. Proprietary: no weights. Muse Wonder Full,
+    # released alongside it, is limited to Muse Code subscriptions and has
+    # no API, so the board cannot run it.
+    "muse-wonder": "2026-10-12",
     # openrouter.ai/stealth/ox-alpha ("released on August 20, 2026" as a
     # stealth preview; the 2026-09-01 table carried 08-21 from the listing's
     # first observed day). After the row's run Z.ai identified the preview as

tests/test_eval_no_tools.py

@@ -3087,6 +3087,43 @@ def test_local_claude_registration_works_without_the_remote_cost_map():
     assert all(out["models"].values()), out["models"]
 
 
+def test_muse_wonder_routes_through_openrouter_at_metas_list_price():
+    """Muse Wonder runs through OpenRouter, whose one endpoint for the id is
+    Meta's own, at Meta's list price: $4 / $20 per 1M input/output tokens
+    and $0.20 cached input, at every prompt length."""
+    assert MODELS["muse-wonder"] == "openrouter/meta/muse-wonder"
+    assert PRICE_OVERRIDES_PER_1M["muse-wonder"] == {
+        "input": 4.0,
+        "output": 20.0,
+        "cache_read": 0.20,
+    }
+
+
+@pytest.mark.parametrize(
+    ("prompt_tokens", "completion_tokens", "cached_prompt_tokens", "expected"),
+    [
+        (1_000_000, 0, 0, 4.0),
+        (0, 1_000_000, 0, 20.0),
+        (1_000_000, 1_000_000, 0, 24.0),
+        (1_000_000, 0, 1_000_000, 0.20),
+        (1_000_000, 0, 250_000, 3.05),
+    ],
+)
+def test_muse_wonder_override_reconstructs_token_costs(
+    prompt_tokens, completion_tokens, cached_prompt_tokens, expected
+):
+    reconstructed = _reconstruct_token_cost(
+        model_name="muse-wonder",
+        model_id="openrouter/meta/muse-wonder",
+        prompt_tokens=prompt_tokens,
+        completion_tokens=completion_tokens,
+        cached_prompt_tokens=cached_prompt_tokens,
+    )
+
+    assert reconstructed.usd == pytest.approx(expected)
+    assert reconstructed.is_estimated is False
+
+
 def test_claude_haiku_5_5_reasons_by_default_but_not_under_the_forced_tool():
     """Haiku 5.5 thinks when the request omits the thinking parameter, so it
     is a thinking-default Claude: provider-default thinking, the thinking

tests/test_model_cards.py

@@ -68,6 +68,7 @@
     "openrouter/deepseek/deepseek-v4-pro-0813": ("tool", None, 2400, 98_304),
     "openrouter/deepseek/deepseek-v4-flash-0731": ("tool", None, 2400, 98_304),
     "openrouter/z-ai/glm-5.3": ("tool", None, 2400, 16_384),
+    "openrouter/meta/muse-wonder": ("json", None, 300, 16_384),
 }
 
 

tests/test_paper_results.py

@@ -73,6 +73,12 @@ def test_claude_haiku_5_5_release_date_is_recorded():
     assert MODEL_RELEASE_DATES["claude-haiku-5.5"] == "2026-10-07"
 
 
+def test_muse_wonder_release_date_is_recorded():
+    """Meta released Muse Wonder on 2026-10-12 (TechCrunch and VentureBeat,
+    both that day), on its Model API and OpenRouter from launch."""
+    assert MODEL_RELEASE_DATES["muse-wonder"] == "2026-10-12"
+
+
 def test_app_release_dates_mirror_the_paper_registry():
     """app/src/modelMeta.ts copies MODEL_RELEASE_DATES (its comment says to
     update both together); every runnable model has a date, and the two