Add the Gemini 4 Argon model card and metadata

app/src/modelMeta.ts

@@ -142,6 +142,7 @@ export const MODEL_RELEASE_DATES: Record<string, string> = {
   "gemini-3.8-flash": "2026-09-02",
   "gemini-3.7-flash": "2026-08-13",
   "gemini-3.5-flash-lite": "2026-07-21",
+  "gemini-4-argon": "2026-10-13",
   "gemini-3.1-flash-lite-preview": "2026-03-03",
   "gpt-5.4-mini": "2026-03-17",
   "gpt-5.4-nano": "2026-03-17",

policybench/config.py

@@ -72,6 +72,13 @@
     "gemini-3.7-flash": "gemini/gemini-3.7-flash",
     "gemini-3.8-flash": "gemini/gemini-3.8-flash",
     "gemini-3.5-flash-lite": "gemini/gemini-3.5-flash-lite",
+    # Gemini 4 Argon. Google announced it on 2026-09-30 for Fairwind Program
+    # cyber partners only and opened it to the public on 2026-10-13, when the
+    # Gemini API changelog lists gemini-4-argon as generally available
+    # (ai.google.dev/gemini-api/docs/changelog, read 2026-10-13). It runs on
+    # the first-party Gemini API like the other Gemini rows. The id is the
+    # model page's one stable version; Google lists no dated snapshot.
+    "gemini-4-argon": "gemini/gemini-4-argon",
     # Cloaked OpenRouter preview listed 2026-08-20 (free window); Z.ai later
     # identified it as GLM-5.3-Flash. On the board as a labeled preview row.
     "ox-alpha": "openrouter/stealth/ox-alpha",
@@ -183,6 +190,17 @@
     "gemini-3.8-flash": {"input": 0.75, "output": 3.75},
     # gemini-3.5-flash-lite: $0.30 / $2.50 per 1M (Google list, 2026-09-03).
     "gemini-3.5-flash-lite": {"input": 0.30, "output": 2.50},
+    # gemini-4-argon: $2 / $10 per 1M input/output tokens (output includes
+    # thinking tokens) and $0.10 cached input, the standard-tier rates Google
+    # lists "through November 30, 2026"; from 2026-12-01 they double to
+    # $4 / $20 and $0.20 (ai.google.dev/gemini-api/docs/pricing, last updated
+    # and read 2026-10-13). The row is priced at the rate in force on its run
+    # day, as gemini-3.7-flash and gemini-3.8-flash are at their dated
+    # introductory rates. The page lists one rate at every prompt length and
+    # implicit caching only. litellm's remote price map carries the same
+    # three rates (read 2026-10-13); the override keeps the row priced if the
+    # map is unavailable or changes on 2026-12-01.
+    "gemini-4-argon": {"input": 2.0, "output": 10.0, "cache_read": 0.10},
     # glm-5.3: $1.40 / $4.40 per 1M (OpenRouter list, 2026-09-03).
     "glm-5.3": {"input": 1.40, "output": 4.40},
     # deepseek dated snapshots via OpenRouter (list prices 2026-09-03).

policybench/model_cards.py

@@ -448,6 +448,82 @@ class ModelCard:
             "whole-scenario (622 completion tokens)."
         ),
     ),
+    # Every field below comes from the 2026-10-13 onboarding record in
+    # ~/reviews/policybench-gemini-4-argon/{probes,onboard,smoke,board-run}
+    # (the reasoning probes, the output-limit probe, the gauntlet report, the
+    # two-scenario smoke, and the forced and tool_choice auto 100-household
+    # runs' comparison):
+    # - answer_contract "tool": the Gemini API accepts a forced function call
+    #   on this model (litellm sends mode ANY with allowed_function_names),
+    #   and the gauntlet passed on it, so the row takes the board's forced
+    #   answer tool, as Gemini 3.6, 3.7 and 3.8 Flash do.
+    # - explanation_chunk_size None: 16/16 on the canonical whole-scenario
+    #   request (chunking is closed to new models anyway).
+    # - thinking_budget True: the model reasons by default (Google's guide
+    #   gives a default thinking_level of medium), so it is in the
+    #   thinking-default class and takes that class's 300s timeout. The
+    #   16,384-token starting budget is the Gemini family's.
+    # - request_timeout_seconds None: the class default; the slowest of 87
+    #   harness-path probe calls took 41s, and the slowest board household
+    #   took 53s in the first board run and 47s in the second.
+    # - provider_max_completion_tokens 32_768. Google's pages disagree: the
+    #   Gemini 4 Argon guide for generateContent says "One non-streaming
+    #   request returns up to 32,768 tokens" (64K per stream), and the
+    #   Models API's outputTokenLimit is 32,768 (models-get-gemini-4-argon
+    #   .json in the onboarding record, read 2026-10-13); the model page
+    #   lists 65,536 as "default synchronous". litellm makes one
+    #   non-streaming REST request, so the card follows the guide; 32,768 is
+    #   the safe value either way.
+    # - expected_cost_per_scenario_usd 0.032: the gauntlet's full-scenario
+    #   probe at $2/$10 per 1M.
+    "gemini/gemini-4-argon": ModelCard(
+        litellm_id="gemini/gemini-4-argon",
+        answer_contract="tool",
+        thinking_budget=True,
+        provider_max_completion_tokens=32_768,
+        expected_cost_per_scenario_usd=0.032,
+        notes=(
+            "Onboarded 2026-10-13, the day Google opened the model to the "
+            "public (the Gemini API changelog lists gemini-4-argon as "
+            "generally available that day; the 2026-09-30 launch was limited "
+            "to Fairwind Program cyber partners). The Gemini API accepts a "
+            "forced function call on this model, so the row takes the "
+            "board's forced answer tool: the gauntlet, run with no card, "
+            "passed 3/3 (442 completion tokens, 5s) and 16/16 whole-scenario "
+            "(2,619 tokens, 24s) on the tool contract, about $0.032 per "
+            "scenario at $2/$10 per 1M. The harness sends no thinkingConfig, "
+            "and Google's guide gives a default thinking_level of medium. "
+            "The model decides call by call whether to think. A forced tool "
+            "does not switch its reasoning off, and no statistically "
+            "significant reduction in how often it reasons was detected. Over "
+            "the 100 board households a first forced-tool board run returned "
+            "reasoning tokens on 80 and a tool_choice auto run on 82 (Fisher "
+            "exact p = 0.86; 70 households reasoned in both runs, 10 only "
+            "when forced, 12 only under auto, 8 in neither), so the row has no "
+            "tool_choice auto sensitivity run; the second board run, below, "
+            "reasoned on 85. Earlier probes agree: on the "
+            "board's own request for 13 households (29 calls a shape) the "
+            "forced tool returned reasoning tokens on 20 calls, auto on 25 and "
+            "the JSON contract on 22 (forced against auto, p = 0.21), and all "
+            "87 calls parsed every output; on a one-question probe (10 calls a "
+            "shape) it returned thought tokens on 6 forced calls, 7 auto, 5 "
+            "with mode ANY and no named function, and none with no tools or "
+            "with a JSON response type. The first board run answered all 100 "
+            "households in one request each, 1,984/1,984 parsed, with no "
+            "budget escalation, for $2.77 (a median of 20s and about 2,030 "
+            "completion tokens per household, at most 6,374 against the "
+            "16,384-token starting budget). It ran with a 65,536-token "
+            "escalation ceiling, the model page's figure, but Google's "
+            "generateContent guide and the Models API give 32,768 for one "
+            "non-streaming request, which is what the harness sends, so the "
+            "board row is a second run on this card: all 100 households in one "
+            "request each, 1,984/1,984 parsed, no budget escalation, $2.79, "
+            "reasoning tokens on 85 households (a median of 22s and about "
+            "1,990 completion tokens per household, at most 47s and 4,872). "
+            "Requests asking for up to 131,072 tokens returned 200, which "
+            "shows only that the parameter is accepted."
+        ),
+    ),
     "openrouter/z-ai/glm-5.3": ModelCard(
         litellm_id="openrouter/z-ai/glm-5.3",
         answer_contract="tool",

policybench/paper_results.py

@@ -3265,6 +3265,15 @@ def last_engine_upgrade(self) -> EngineUpgrade:
     "gemini-3.8-flash": "2026-09-02",
     # blog.google/products/gemini/gemini-3-7-flash (2026-08-13)
     "gemini-3.7-flash": "2026-08-13",
+    # ai.google.dev/gemini-api/docs/changelog ("October 13, 2026": Gemini 4
+    # Argon, gemini-4-argon, generally available) and blog.google/
+    # innovation-and-ai/models-and-research/gemini-models/
+    # introducing-gemini-4-argon (2026-10-13: the paid tier of the Gemini
+    # API, and Google AI Pro and Ultra subscribers in the Gemini app), both
+    # read 2026-10-13. Google announced the model on 2026-09-30 for Fairwind
+    # Program cyber partners only (techcrunch.com 2026-09-30); that day is
+    # excluded under the public-availability rule.
+    "gemini-4-argon": "2026-10-13",
     # blog.google gemini-3-1-flash-lite; siliconangle.com 2026-03-03
     "gemini-3.1-flash-lite-preview": "2026-03-03",
     # openai.com/index/introducing-gpt-5-4-mini-and-nano; 9to5mac 2026-03-17

tests/test_eval_no_tools.py

@@ -7,6 +7,8 @@
 
 import litellm
 import pytest
+from hypothesis import given
+from hypothesis import strategies as st
 
 from policybench.config import (
     COUNTRY_PROGRAMS,
@@ -3124,6 +3126,115 @@ def test_muse_wonder_override_reconstructs_token_costs(
     assert reconstructed.is_estimated is False
 
 
+def test_gemini_4_argon_runs_first_party_at_googles_dated_rate():
+    """Gemini 4 Argon runs on the first-party Gemini API, like the other
+    Gemini rows, at the rate Google's pricing page lists through 2026-11-30:
+    $2 / $10 per 1M input/output tokens and $0.10 cached input, at every
+    prompt length (the page doubles all three from 2026-12-01)."""
+    assert MODELS["gemini-4-argon"] == "gemini/gemini-4-argon"
+    assert PRICE_OVERRIDES_PER_1M["gemini-4-argon"] == {
+        "input": 2.0,
+        "output": 10.0,
+        "cache_read": 0.10,
+    }
+
+
+@pytest.mark.parametrize(
+    ("prompt_tokens", "completion_tokens", "cached_prompt_tokens", "expected"),
+    [
+        (1_000_000, 0, 0, 2.0),
+        (0, 1_000_000, 0, 10.0),
+        (1_000_000, 1_000_000, 0, 12.0),
+        (1_000_000, 0, 1_000_000, 0.10),
+        (1_000_000, 0, 250_000, 1.525),
+        # The gauntlet's whole-scenario probe used 2,619 completion tokens
+        # (reasoning included); the same request's prompt is 2,762 tokens in
+        # the harness probe. That is the card's $0.032.
+        (2_762, 2_619, 0, 0.031714),
+    ],
+)
+def test_gemini_4_argon_override_reconstructs_token_costs(
+    prompt_tokens, completion_tokens, cached_prompt_tokens, expected
+):
+    reconstructed = _reconstruct_token_cost(
+        model_name="gemini-4-argon",
+        model_id="gemini/gemini-4-argon",
+        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
+
+
+@given(
+    prompt_tokens=st.integers(min_value=0, max_value=1_048_576),
+    completion_tokens=st.integers(min_value=0, max_value=131_072),
+    cached_share=st.floats(min_value=0.0, max_value=1.0),
+)
+def test_gemini_4_argon_cost_is_linear_in_tokens_and_bounded_by_the_cache_rates(
+    prompt_tokens, completion_tokens, cached_share
+):
+    """Invariants of the override's reconstruction: the cost is the sum of
+    uncached input at $2, cached input at $0.10 and output at $10 per 1M; it
+    is never negative; and it lies between the all-cached and the uncached
+    cost of the same call."""
+    cached = int(prompt_tokens * cached_share)
+
+    def cost(cached_prompt_tokens):
+        return _reconstruct_token_cost(
+            model_name="gemini-4-argon",
+            model_id="gemini/gemini-4-argon",
+            prompt_tokens=prompt_tokens,
+            completion_tokens=completion_tokens,
+            cached_prompt_tokens=cached_prompt_tokens,
+        ).usd
+
+    expected = (
+        (prompt_tokens - cached) * 2.0 + cached * 0.10 + completion_tokens * 10.0
+    ) / 1_000_000
+    assert cost(cached) == pytest.approx(expected, abs=1e-12)
+    assert cost(cached) >= 0
+    assert cost(prompt_tokens) - 1e-12 <= cost(cached) <= cost(0) + 1e-12
+
+
+def test_gemini_4_argon_request_forces_the_answer_tool_and_sets_no_reasoning(
+    mini_scenario,
+):
+    """The Gemini API accepts a forced function call on Gemini 4 Argon, so
+    its card selects the board's forced answer tool (the older Gemini rows
+    answer as JSON by the family default). The request carries no reasoning
+    parameter: the model reasons at Google's default. It takes the
+    thinking-default class's 300s timeout and the Gemini family's
+    16,384-token starting budget."""
+    from policybench.eval_no_tools import (
+        ANSWER_FUNCTION_NAME,
+        _answer_contract_for_model,
+        _chat_completion_request_kwargs,
+    )
+
+    model_id = "gemini/gemini-4-argon"
+    assert _answer_contract_for_model(model_id, env={}) == "tool"
+    assert _thinking_configuration(model_id) == {"mode": "provider_default"}
+    assert _request_timeout_seconds(model_id) == 300
+
+    _, kwargs = _chat_completion_request_kwargs(mini_scenario, ["income_tax"], model_id)
+
+    assert kwargs["tool_choice"] == {
+        "type": "function",
+        "function": {"name": ANSWER_FUNCTION_NAME},
+    }
+    assert [tool["function"]["name"] for tool in kwargs["tools"]] == [
+        ANSWER_FUNCTION_NAME
+    ]
+    assert kwargs["max_completion_tokens"] == 16_384
+    assert kwargs["timeout"] == 300
+    assert "response_format" not in kwargs
+    for reasoning_key in ("reasoning", "reasoning_effort", "thinking"):
+        assert reasoning_key not in kwargs
+
+
 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

@@ -69,6 +69,7 @@
     "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),
+    "gemini/gemini-4-argon": ("tool", None, 300, 16_384),
 }
 
 
@@ -151,6 +152,7 @@ def test_provider_max_clamps_initial_completion_budget(monkeypatch):
         ("gemini/gemini-3.5-flash", 65_535),
         ("gemini/gemini-3-flash-preview", 65_535),
         ("gemini/gemini-3.6-flash", 65_536),
+        ("gemini/gemini-4-argon", 32_768),
     ],
 )
 def test_roster_model_provider_maximums_limit_escalation(model_id, expected):

tests/test_paper_results.py

@@ -81,6 +81,15 @@ def test_muse_wonder_release_date_is_recorded():
     assert MODEL_RELEASE_DATES["muse-wonder"] == "2026-10-12"
 
 
+def test_gemini_4_argon_release_date_is_its_public_day():
+    """Google announced Gemini 4 Argon on 2026-09-30 for Fairwind Program
+    cyber partners only (TechCrunch, that day) and opened it to the public on
+    2026-10-13 (the Gemini API changelog's general-availability entry and
+    Google's launch post, both that day). The partner-only day is excluded
+    under the public-availability rule, as GPT-6 Astra's is."""
+    assert MODEL_RELEASE_DATES["gemini-4-argon"] == "2026-10-13"
+
+
 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