diff --git a/libs/langchain/langchain/chains/flare/base.py b/libs/langchain/langchain/chains/flare/base.py
index 207b58b6645..d2621b3bfc3 100644
--- a/libs/langchain/langchain/chains/flare/base.py
+++ b/libs/langchain/langchain/chains/flare/base.py
@@ -249,7 +249,7 @@ class FlareChain(Chain):
     @classmethod
     def from_llm(
         cls,
-        llm: BaseLanguageModel,
+        llm: Optional[BaseLanguageModel],
         max_generation_len: int = 32,
         **kwargs: Any,
     ) -> FlareChain:
@@ -272,11 +272,36 @@ class FlareChain(Chain):
                 "pip install langchain-openai"
             )
             raise ImportError(msg) from e
-        llm = ChatOpenAI(
-            max_completion_tokens=max_generation_len,
-            logprobs=True,
-            temperature=0,
-        )
+        # Preserve supplied llm instead of always creating a new ChatOpenAI.
+        # Enforce ChatOpenAI requirement (token logprobs needed for FLARE).
+        if llm is None:
+            llm = ChatOpenAI(
+                max_completion_tokens=max_generation_len,
+                logprobs=True,
+                temperature=0,
+            )
+        else:
+            if not isinstance(llm, ChatOpenAI):
+                msg = (
+                    f"FlareChain.from_llm requires ChatOpenAI; got "
+                    f"{type(llm).__name__}."
+                )
+                raise TypeError(msg)
+            if not getattr(llm, "logprobs", False):  # attribute presence may vary
+                msg = (
+                    "Provided ChatOpenAI instance must be constructed with "
+                    "logprobs=True for FlareChain."
+                )
+                raise ValueError(msg)
+            current_max = getattr(llm, "max_completion_tokens", None)
+            if current_max is not None and current_max != max_generation_len:
+                logger.debug(
+                    "FlareChain.from_llm: supplied llm max_completion_tokens=%s "
+                    "differs from requested max_generation_len=%s; "
+                    "leaving model unchanged.",
+                    current_max,
+                    max_generation_len,
+                )
         response_chain = PROMPT | llm
         question_gen_chain = QUESTION_GENERATOR_PROMPT | llm | StrOutputParser()
         return cls(
diff --git a/libs/langchain/tests/unit_tests/chains/test_flare.py b/libs/langchain/tests/unit_tests/chains/test_flare.py
new file mode 100644
index 00000000000..5d7c679de2f
--- /dev/null
+++ b/libs/langchain/tests/unit_tests/chains/test_flare.py
@@ -0,0 +1,51 @@
+"""Tests for FlareChain.from_llm preserving supplied ChatOpenAI instance."""
+
+from typing import cast
+
+import pytest
+from langchain_core.documents import Document
+from langchain_core.retrievers import BaseRetriever
+from langchain_core.runnables import RunnableSequence
+
+from langchain.chains.flare.base import FlareChain
+
+
+class _EmptyRetriever(BaseRetriever):
+    """Minimal no-op retriever used only for constructing FlareChain in tests."""
+
+    def _get_relevant_documents(self, query: str) -> list[Document]:  # type: ignore[override]
+        del query  # mark used
+        return []
+
+    async def _aget_relevant_documents(self, query: str) -> list[Document]:  # type: ignore[override]
+        del query  # mark used
+        return []
+
+
+def test_from_llm_rejects_non_chatopenai() -> None:
+    class Dummy:
+        pass
+
+    with pytest.raises(TypeError):
+        FlareChain.from_llm(Dummy())  # type: ignore[arg-type]
+
+
+@pytest.mark.requires("langchain_openai")
+def test_from_llm_uses_supplied_chatopenai(monkeypatch: pytest.MonkeyPatch) -> None:
+    try:
+        from langchain_openai import ChatOpenAI
+    except ImportError:  # pragma: no cover
+        pytest.skip("langchain-openai not installed")
+
+    # Provide dummy API key to satisfy constructor env validation.
+    monkeypatch.setenv("OPENAI_API_KEY", "TEST")
+
+    supplied = ChatOpenAI(temperature=0.51, logprobs=True, max_completion_tokens=21)
+    chain = FlareChain.from_llm(
+        supplied,
+        max_generation_len=32,
+        retriever=_EmptyRetriever(),
+    )
+
+    llm_in_chain = cast("RunnableSequence", chain.question_generator_chain).steps[1]
+    assert llm_in_chain is supplied