fix(vertex_ai): resolve correct default max_tokens for versioned claude ids

two defects stacked on the vertex claude path so a request without
max_tokens was silently capped at 4096 output tokens

the fill in transform_request called AnthropicConfig.get_config directly
so get_max_tokens_for_model on VertexAIAnthropicConfig never ran, and the
bare versioned id like claude-haiku-4-5@20251001 is not a cost map key,
so the fill fell back to the 4096 default

now transform_request calls self.get_config and VertexAIAnthropicConfig
resolves the vertex_ai prefixed key first, then falls back to the base
lookup

also the two vertex_ai claude haiku 4 5 map entries said 8192 max output
while every sibling claude-haiku-4-5 key says 64000 and google documents
64000, the reporter verified 64000 live on vertex with a 200 and 15290
completion tokens, both entries and the runtime backup map now say 64000

Fixes #40363

litellm/llms/anthropic/chat/transformation.py

@@ -2,7 +2,7 @@ import json
 import re
 import time
 from collections.abc import Callable, Mapping, Sequence
-from types import MappingProxyType
+from types import BuiltinFunctionType, FunctionType, MappingProxyType
 from typing import TYPE_CHECKING, Any, Final, NoReturn, cast
 
 import httpx
@@ -322,7 +322,26 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
 
     @classmethod
     def get_config(cls, *, model: str | None = None):
-        config: Final = super().get_config()
+        # defaults configured on the base class (litellm.AnthropicConfig(max_tokens=...)) live on
+        # AnthropicConfig itself and must keep reaching subclass requests (vertex/azure claude)
+        base: Final = {  # mutable-ok: get_config returns a plain dict, same contract as the base impl
+            k: v
+            for k, v in AnthropicConfig.__dict__.items()
+            if not k.startswith("_")
+            and not isinstance(
+                v,
+                (
+                    FunctionType,
+                    BuiltinFunctionType,
+                    classmethod,
+                    staticmethod,
+                    property,
+                ),
+            )
+            and v is not None
+            and not callable(v)
+        }
+        config: Final = {**base, **super().get_config()}  # mutable-ok: one-shot merge, same dict contract
 
         # anthropic requires a default value for max_tokens
         if config.get("max_tokens") is None:
@@ -1985,7 +2004,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
             optional_params["tools"] = tools
 
         ## Load Config
-        config: Final = litellm.AnthropicConfig.get_config(model=model)
+        config: Final = self.get_config(model=model)
         for k, v in config.items():
             if (
                 k not in optional_params

litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py

@@ -9,6 +9,7 @@ from litellm.litellm_core_utils.prompt_templates.image_handling import RemoteMed
 from litellm.llms.base_llm.chat.transformation import LiteLLMLoggingObj
 from litellm.types.llms.openai import AllMessageValues
 from litellm.types.utils import ModelResponse
+from litellm.utils import get_max_tokens
 
 from ....anthropic.chat.transformation import AnthropicConfig
 from .output_params_utils import sanitize_vertex_anthropic_output_params
@@ -58,6 +59,16 @@ class VertexAIAnthropicConfig(AnthropicConfig):
     def should_strip_billing_metadata(self) -> bool:
         return True
 
+    @staticmethod
+    def get_max_tokens_for_model(model: str | None = None) -> int:
+        if model is not None:
+            vertex_key: Final = f"vertex_ai/{model}"
+            if vertex_key in litellm.model_cost:
+                vertex_max: Final = get_max_tokens(vertex_key)
+                if vertex_max is not None:
+                    return vertex_max
+        return AnthropicConfig.get_max_tokens_for_model(model)
+
     def _add_context_management_beta_headers(self, beta_set: set, context_management: dict) -> None:
         """
         Add context_management beta headers to the beta_set.

litellm/model_prices_and_context_window_backup.json

@@ -45891,8 +45891,8 @@
         "input_cost_per_token": 1e-06,
         "litellm_provider": "vertex_ai-anthropic_models",
         "max_input_tokens": 200000,
-        "max_output_tokens": 8192,
-        "max_tokens": 8192,
+        "max_output_tokens": 64000,
+        "max_tokens": 64000,
         "mode": "chat",
         "output_cost_per_token": 5e-06,
         "regional_endpoint_uplift_multiplier": 1.1,
@@ -45916,8 +45916,8 @@
         "input_cost_per_token": 1e-06,
         "litellm_provider": "vertex_ai-anthropic_models",
         "max_input_tokens": 200000,
-        "max_output_tokens": 8192,
-        "max_tokens": 8192,
+        "max_output_tokens": 64000,
+        "max_tokens": 64000,
         "mode": "chat",
         "output_cost_per_token": 5e-06,
         "regional_endpoint_uplift_multiplier": 1.1,

model_prices_and_context_window.json

@@ -45891,8 +45891,8 @@
         "input_cost_per_token": 1e-06,
         "litellm_provider": "vertex_ai-anthropic_models",
         "max_input_tokens": 200000,
-        "max_output_tokens": 8192,
-        "max_tokens": 8192,
+        "max_output_tokens": 64000,
+        "max_tokens": 64000,
         "mode": "chat",
         "output_cost_per_token": 5e-06,
         "regional_endpoint_uplift_multiplier": 1.1,
@@ -45916,8 +45916,8 @@
         "input_cost_per_token": 1e-06,
         "litellm_provider": "vertex_ai-anthropic_models",
         "max_input_tokens": 200000,
-        "max_output_tokens": 8192,
-        "max_tokens": 8192,
+        "max_output_tokens": 64000,
+        "max_tokens": 64000,
         "mode": "chat",
         "output_cost_per_token": 5e-06,
         "regional_endpoint_uplift_multiplier": 1.1,

tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/test_vertex_ai_partner_models_anthropic_transformation.py

@@ -775,3 +775,45 @@ def test_vertex_ai_anthropic_tool_based_response_format_still_upgrades_legacy_th
     assert "tools" in result_params
     assert result_params["thinking"] == {"type": "adaptive"}
     assert result_params["output_config"] == {"effort": "high"}
+
+
+def test_vertex_ai_anthropic_versioned_model_default_max_tokens(local_model_cost_map):
+    config = VertexAIAnthropicConfig()
+    data = config.transform_request(
+        model="claude-haiku-4-5@20251001",
+        messages=[{"role": "user", "content": "hi"}],
+        optional_params={},
+        litellm_params={},
+        headers={},
+    )
+    assert data["max_tokens"] == 64000
+
+    unversioned = config.transform_request(
+        model="claude-haiku-4-5",
+        messages=[{"role": "user", "content": "hi"}],
+        optional_params={},
+        litellm_params={},
+        headers={},
+    )
+    assert unversioned["max_tokens"] == 64000
+
+
+def test_vertex_ai_anthropic_base_config_defaults_reach_subclass(local_model_cost_map):
+    from litellm import AnthropicConfig
+
+    AnthropicConfig(max_tokens=123, temperature=0.5)
+    try:
+        data = VertexAIAnthropicConfig().transform_request(
+            model="claude-haiku-4-5@20251001",
+            messages=[{"role": "user", "content": "hi"}],
+            optional_params={},
+            litellm_params={},
+            headers={},
+        )
+    finally:
+        for attr in ("max_tokens", "temperature"):
+            if attr in AnthropicConfig.__dict__:
+                delattr(AnthropicConfig, attr)
+
+    assert data["max_tokens"] == 123
+    assert data["temperature"] == 0.5