Merge pull request #42069 from BerriAI/litellm_redacted_thinking_prompt_caching_pin

fix(token_counter): count replayed redacted_thinking blocks so prompt_caching keeps pinning

litellm/litellm_core_utils/token_counter.py

@@ -46,6 +46,8 @@ from litellm.types.llms.openai import (
     AllMessageValues,
     ChatCompletionDocumentObject,
     ChatCompletionNamedToolChoiceParam,
+    ChatCompletionRedactedThinkingBlock,
+    ChatCompletionThinkingBlock,
     ChatCompletionToolParam,
     OpenAIMessageContentListBlock,
 )
@@ -854,6 +856,8 @@ def _count_content_list(
     content_list: str
     | Iterable[
         OpenAIMessageContentListBlock
+        | ChatCompletionThinkingBlock
+        | ChatCompletionRedactedThinkingBlock
         | AnthropicMessagesTextParam
         | AnthropicMessagesImageParam
         | AnthropicMessagesDocumentParam
@@ -898,9 +902,9 @@ def _count_content_list(
                     use_default_image_token_count,
                     default_token_count,
                 )
-            elif c["type"] == "thinking":
+            elif c["type"] in ("thinking", "redacted_thinking"):
                 # Claude extended thinking content block
-                # Count the thinking text and skip signature (opaque signature blob)
+                # Count the thinking text and skip the opaque blobs (signature, redacted data)
                 thinking_text = str(c.get("thinking", ""))
                 if thinking_text:
                     num_tokens += count_function(thinking_text)
@@ -920,7 +924,8 @@ def _count_content_list(
                 raise ValueError(
                     f"Invalid content item type: {content_type}. "
                     f"Expected str or dict with 'type' field "
-                    f"(text, image_url, image, document, file, tool_use, tool_result, thinking, tool_reference)."
+                    f"(text, image_url, image, document, file, tool_use, tool_result, thinking, redacted_thinking, "
+                    f"tool_reference)."
                 )
         return num_tokens
     except Exception as e:

tests/test_litellm/litellm_core_utils/test_token_counter.py

@@ -1257,6 +1257,25 @@ def test_token_counter_with_thinking_content():
     ), f"Expected minimal token count for empty thinking block, got {tokens_no_thinking}"
 
 
+
+def test_token_counter_with_redacted_thinking_content():
+    """
+    A replayed redacted_thinking block (Anthropic redacted reasoning, or the /v1/messages bridge's stand-in
+    for a reasoning item with no summary) counts zero tokens for its encrypted payload, like a thinking
+    block with no text. It used to raise, which made is_prompt_caching_valid_prompt return False and the
+    prompt_caching pre-call check stop pinning the deployment that held the cached prefix.
+    """
+    model = "anthropic/claude-sonnet-4-5-20250929"
+    reply = {"type": "text", "text": "Draw from the box labeled Mixed, because that label must be wrong."}
+    redacted_block = {"type": "redacted_thinking", "data": "EqQBCkYIBRgCKkBjZ2xhc3M" * 30}
+    user_turn = {"role": "user", "content": [{"type": "text", "text": "Which box do you draw from?"}]}
+    follow_up = {"role": "user", "content": [{"type": "text", "text": "Restate that in one sentence."}]}
+
+    without_block = [user_turn, {"role": "assistant", "content": [reply]}, follow_up]
+    with_block = [user_turn, {"role": "assistant", "content": [redacted_block, reply]}, follow_up]
+
+    assert token_counter(model=model, messages=with_block) == token_counter(model=model, messages=without_block)
+
 def test_token_counter_with_tool_reference_block():
     """
     Regression test: a message containing an Anthropic tool-search

tests/unit/router_utils/pre_call_checks/test_prompt_caching_deployment_check.py

@@ -210,6 +210,58 @@ async def test_async_filter_deployments_narrows_for_group_whose_model_minimum_is
 AUTO_CACHING_MODEL = "anthropic/claude-sonnet-4-5"
 
 
+@pytest.mark.asyncio
+async def test_replayed_redacted_thinking_block_still_records_and_pins():
+    """
+    A model that returns no reasoning summary (gpt-5.x through the /v1/messages bridge, Anthropic with
+    redacted reasoning) hands the client a `redacted_thinking` block, and the client replays it on every
+    later turn. The token count behind `is_prompt_caching_valid_prompt` raised on that block, the helper
+    swallowed it to False, and the check neither recorded the serving deployment nor pinned it, so the
+    conversation bounced across the group and paid a cache write on each deployment.
+    """
+    cache = DualCache()
+    check = PromptCachingDeploymentCheck(cache=cache)
+    model = "openai/gpt-5.6-sol"
+    deployments = _deployments(model, model, model)
+    messages = cast(
+        list[AllMessageValues],
+        [
+            *_messages(word_count=3000),
+            {
+                "role": "assistant",
+                "content": [
+                    {"type": "redacted_thinking", "data": "litellm_encrypted_reasoning:" + "Z" * 400},
+                    {"type": "text", "text": "Draw from the box labeled Mixed."},
+                ],
+            },
+            {"role": "user", "content": "Restate that in one sentence."},
+        ],
+    )
+
+    assert is_prompt_caching_valid_prompt(model=model, messages=messages) is True
+
+    await check.async_log_success_event(
+        kwargs={
+            "standard_logging_object": {
+                "call_type": "anthropic_messages",
+                "model": model,
+                "messages": messages,
+                "model_id": "dep-2",
+            }
+        },
+        response_obj=None,
+        start_time=None,
+        end_time=None,
+    )
+    filtered = await check.async_filter_deployments(
+        model=MODEL_GROUP_ALIAS,
+        healthy_deployments=deployments,
+        messages=messages,
+    )
+
+    assert filtered == [deployments[1]]
+
+
 def _auto_caching_messages() -> list[AllMessageValues]:
     """A prompt over the model minimum that carries no client cache_control."""
     return cast(