fix(anthropic): translate raw adaptive thinking for chat completions on pre-4.6 models

Clients that pass thinking={"type": "adaptive"} directly (not via the
reasoning_effort alias) on the /chat/completions interface had it forwarded
unmodified to pre-4.6 Anthropic models, which reject the shape. Mirrors the
translation already applied on the native /v1/messages passthrough (#32867):
translate to legacy thinking={type: enabled, budget_tokens}, capped below
max_tokens, dropping thinking when max_tokens can't fit even the minimum
budget. Hoists the shared budget-capping helper onto AnthropicConfig so both
paths use one implementation.

litellm/llms/anthropic/chat/transformation.py

@@ -227,6 +227,10 @@ DROP_UNSUPPORTED_OUTPUT_CONFIG_WARNING = (
     "Sonnet 4.6+, and Mythos Preview."
 )
 
+DROP_UNSUPPORTED_ADAPTIVE_THINKING_WARNING = (
+    "Dropping adaptive `thinking` for model=%s: max_tokens is too small to fit the minimum thinking budget."
+)
+
 DROP_UNSUPPORTED_SPEED_WARNING = (
     "Dropping unsupported `speed` for model=%s (drop_params=True). Fast mode is only supported on select Opus models."
 )
@@ -1220,6 +1224,23 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
                 llm_provider=llm_provider,
             )
 
+    @staticmethod
+    def _cap_thinking_budget_to_max_tokens(
+        thinking: AnthropicThinkingParam, max_tokens: Optional[int]
+    ) -> Optional[AnthropicThinkingParam]:
+        """Cap a legacy ``thinking.budget_tokens`` below ``max_tokens`` (Anthropic
+        requires ``max_tokens > budget_tokens``). Returns the (possibly capped)
+        thinking dict, or ``None`` when ``max_tokens`` is too small to fit even the
+        minimum thinking budget and thinking should be dropped."""
+        budget = thinking.get("budget_tokens")
+        if max_tokens is None or not isinstance(budget, int):
+            return thinking
+        if max_tokens <= ANTHROPIC_MIN_THINKING_BUDGET_TOKENS:
+            return None
+        if budget < max_tokens:
+            return thinking
+        return AnthropicThinkingParam(type=thinking.get("type", "enabled"), budget_tokens=max_tokens - 1)
+
     def _extract_json_schema_from_response_format(self, value: Optional[dict]) -> Optional[dict]:
         if value is None:
             return None
@@ -1463,7 +1484,37 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
             ):
                 optional_params["metadata"] = {"user_id": value}
             elif param == "thinking":
-                optional_params["thinking"] = value
+                if (
+                    isinstance(value, dict)
+                    and value.get("type") == "adaptive"
+                    and not AnthropicConfig._is_adaptive_thinking_model(model)
+                ):
+                    # Callers (e.g. Claude Code) send adaptive thinking
+                    # unconditionally; translate it down to the legacy
+                    # `thinking={type: enabled, budget_tokens}` interface a
+                    # pre-4.6 model actually supports instead of forwarding a
+                    # shape the model will reject.
+                    max_tokens = non_default_params.get("max_completion_tokens") or non_default_params.get("max_tokens")
+                    legacy_thinking = AnthropicConfig._map_reasoning_effort(
+                        reasoning_effort="medium",
+                        model=model,
+                        llm_provider=self.custom_llm_provider or "anthropic",
+                    )
+                    capped_thinking = (
+                        AnthropicConfig._cap_thinking_budget_to_max_tokens(legacy_thinking, max_tokens)
+                        if legacy_thinking is not None
+                        else None
+                    )
+                    if capped_thinking is not None:
+                        optional_params["thinking"] = capped_thinking
+                    else:
+                        litellm.verbose_logger.warning(
+                            DROP_UNSUPPORTED_ADAPTIVE_THINKING_WARNING,
+                            model,
+                        )
+                        optional_params.pop("thinking", None)
+                else:
+                    optional_params["thinking"] = value
             elif param == "reasoning_effort":
                 # Accept both string ("low") and dict ({"effort": "low",
                 # "summary": "concise"}). The Responses->Chat parser keeps the

litellm/llms/anthropic/experimental_pass_through/messages/transformation.py

@@ -3,7 +3,6 @@ from typing import Any, AsyncIterator, Dict, List, Optional, Tuple
 import httpx
 
 from litellm.constants import (
-    ANTHROPIC_MIN_THINKING_BUDGET_TOKENS,
     DEFAULT_REASONING_EFFORT_HIGH_THINKING_BUDGET,
     DEFAULT_REASONING_EFFORT_MEDIUM_THINKING_BUDGET,
     DEFAULT_REASONING_EFFORT_XHIGH_THINKING_BUDGET,
@@ -353,7 +352,7 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
         except _BadRequestError as e:
             raise AnthropicError(message=str(e.message), status_code=400)
         capped_thinking = (
-            AnthropicMessagesConfig._cap_thinking_budget_to_max_tokens(legacy_thinking, max_tokens)
+            AnthropicConfig._cap_thinking_budget_to_max_tokens(legacy_thinking, max_tokens)
             if legacy_thinking is not None
             else None
         )
@@ -371,21 +370,6 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
             else:
                 optional_params.pop("output_config", None)
 
-    @staticmethod
-    def _cap_thinking_budget_to_max_tokens(thinking: Dict, max_tokens: Optional[int]) -> Optional[Dict]:
-        """Cap a legacy ``thinking.budget_tokens`` below ``max_tokens`` (Anthropic
-        requires ``max_tokens > budget_tokens``). Returns the (possibly capped)
-        thinking dict, or ``None`` when ``max_tokens`` is too small to fit even the
-        minimum thinking budget and thinking should be dropped."""
-        budget = thinking.get("budget_tokens")
-        if max_tokens is None or not isinstance(budget, int):
-            return thinking
-        if max_tokens <= ANTHROPIC_MIN_THINKING_BUDGET_TOKENS:
-            return None
-        if budget < max_tokens:
-            return thinking
-        return {**thinking, "budget_tokens": max_tokens - 1}
-
     def transform_anthropic_messages_request(
         self,
         model: str,

tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py

@@ -10,6 +10,7 @@ from unittest.mock import MagicMock, patch
 
 import litellm
 from litellm.constants import (
+    ANTHROPIC_MIN_THINKING_BUDGET_TOKENS,
     DEFAULT_REASONING_EFFORT_HIGH_THINKING_BUDGET,
     DEFAULT_REASONING_EFFORT_LOW_THINKING_BUDGET,
     DEFAULT_REASONING_EFFORT_MAX_THINKING_BUDGET,
@@ -2443,6 +2444,78 @@ def test_reasoning_effort_maps_to_adaptive_thinking_for_claude_4_6_models():
             assert result["output_config"]["effort"] == effort_map[effort]
 
 
+def test_raw_adaptive_thinking_translates_to_legacy_for_pre_46_model():
+    """Clients like Claude Code send ``thinking={"type": "adaptive"}`` directly
+    (not via ``reasoning_effort``) on every request, regardless of which model
+    the request routes to. For a pre-4.6 model that doesn't understand
+    adaptive thinking, this must be translated to the legacy
+    ``thinking={type: enabled, budget_tokens}`` interface instead of being
+    forwarded raw, which Anthropic would reject."""
+    config = AnthropicConfig()
+
+    result = config.map_openai_params(
+        non_default_params={"thinking": {"type": "adaptive"}, "max_tokens": 8192},
+        optional_params={},
+        model="claude-haiku-4-5-20251001",
+        drop_params=False,
+    )
+
+    assert result["thinking"]["type"] == "enabled"
+    assert result["thinking"]["budget_tokens"] == DEFAULT_REASONING_EFFORT_MEDIUM_THINKING_BUDGET
+
+
+def test_raw_adaptive_thinking_budget_capped_below_max_tokens():
+    """Anthropic requires ``max_tokens > thinking.budget_tokens``. When the
+    default medium budget wouldn't fit, it must be capped below max_tokens
+    rather than forwarded as an invalid combination."""
+    config = AnthropicConfig()
+
+    max_tokens = DEFAULT_REASONING_EFFORT_MEDIUM_THINKING_BUDGET - 100
+    result = config.map_openai_params(
+        non_default_params={"thinking": {"type": "adaptive"}, "max_tokens": max_tokens},
+        optional_params={},
+        model="claude-haiku-4-5-20251001",
+        drop_params=False,
+    )
+
+    assert result["thinking"]["type"] == "enabled"
+    assert result["thinking"]["budget_tokens"] == max_tokens - 1
+
+
+def test_raw_adaptive_thinking_dropped_when_max_tokens_too_small():
+    """When max_tokens can't fit even the minimum thinking budget, thinking
+    must be dropped entirely so the request still succeeds, matching how the
+    native /v1/messages passthrough already handles this."""
+    config = AnthropicConfig()
+
+    result = config.map_openai_params(
+        non_default_params={
+            "thinking": {"type": "adaptive"},
+            "max_tokens": ANTHROPIC_MIN_THINKING_BUDGET_TOKENS,
+        },
+        optional_params={},
+        model="claude-haiku-4-5-20251001",
+        drop_params=False,
+    )
+
+    assert "thinking" not in result
+
+
+def test_raw_adaptive_thinking_untouched_for_46_plus_model():
+    """Adaptive-thinking models understand ``thinking={"type": "adaptive"}``
+    natively, so it must pass through unmodified."""
+    config = AnthropicConfig()
+
+    result = config.map_openai_params(
+        non_default_params={"thinking": {"type": "adaptive"}, "max_tokens": 8192},
+        optional_params={},
+        model="claude-sonnet-4-6-20260219",
+        drop_params=False,
+    )
+
+    assert result["thinking"] == {"type": "adaptive"}
+
+
 @pytest.fixture
 def local_model_cost_map(monkeypatch):
     original_model_cost = litellm.model_cost