diff --git a/batchgen/batchgen_worker.py b/batchgen/batchgen_worker.py
index cef5bd2f..165279ba 100644
--- a/batchgen/batchgen_worker.py
+++ b/batchgen/batchgen_worker.py
@@ -1682,9 +1682,10 @@ def _report_completion(self, uuid: str, gathered_text: str = None) -> None:
 		# Use gathered text if provided, otherwise read from local buffer
 		text = gathered_text if gathered_text is not None else ""
 		if text == "" and seq.decoded_tokens is not None and seq.decoded_length > 0:
-			token_ids = seq.decoded_tokens[0, :seq.decoded_length].tolist()
 			try:
-				text = self.tokenizer.decode(token_ids)
+				text = self._decode_tokens_to_string(
+					seq.decoded_tokens[:, :seq.decoded_length]
+				)
 			except Exception:
 				text = ""
 		self._response_queue.put({
@@ -1693,7 +1694,10 @@ def _report_completion(self, uuid: str, gathered_text: str = None) -> None:
 			"batch_id": getattr(seq, "batch_id", None),
 			"global_idx": seq.global_idx,
 			"text": text,
-			"prompt_length": seq.prompt_length,
+			# Re-entry turns prior completion tokens into the internal prompt so
+			# the KV state can be recomputed.  OpenAI usage must still report the
+			# caller's original prompt, not that internal reconstructed length.
+			"prompt_length": seq.original_prompt_length,
 			"decoded_length": seq.decoded_length,
 			"finish_reason": self._get_finish_reason(seq),
 		})
@@ -1718,9 +1722,11 @@ def _gather_completed_tokens(self, completed_uuids: List[str]) -> dict:
 				local_idx = self._uuid_to_local_map[uuid]
 				seq = self.global_batch.get_sequence(uuid)
 				if seq is not None and local_idx in self.query_book:
-					token_ids = self.query_book[local_idx].decoded_tokens[0, :seq.decoded_length].tolist()
+					decoded_tokens = self.query_book[local_idx].decoded_tokens[
+						:, :seq.decoded_length
+					]
 					try:
-						text = self.tokenizer.decode(token_ids)
+						text = self._decode_tokens_to_string(decoded_tokens)
 					except Exception:
 						text = ""
 					my_tokens[uuid] = text
diff --git a/tests/test_completion_usage_accounting.py b/tests/test_completion_usage_accounting.py
new file mode 100644
index 00000000..6c3c5a15
--- /dev/null
+++ b/tests/test_completion_usage_accounting.py
@@ -0,0 +1,109 @@
+from queue import SimpleQueue
+from types import SimpleNamespace
+
+import torch
+
+import batchgen.batchgen_worker as worker_module
+from batchgen.batchgen_worker import BatchGenWorker
+from batchgen.sequence import SequenceEntry, SequenceStatus
+
+
+def test_report_completion_keeps_original_prompt_length_after_reentry():
+    seq = SequenceEntry(
+        "reentered",
+        global_idx=7,
+        prompt_length=100,
+        max_decode_length=512,
+    )
+    seq.prompt_length = 228
+    seq.decoded_length = 128
+    seq.status = SequenceStatus.COMPLETED
+
+    response_queue = SimpleQueue()
+    worker = SimpleNamespace(
+        global_batch=SimpleNamespace(get_sequence=lambda uuid: seq),
+        _uuid_to_local_map={},
+        _local_to_uuid_map={},
+        query_book={},
+        _free_local_indices=set(),
+        rank=0,
+        _response_queue=response_queue,
+        _get_finish_reason=lambda _seq: "stop",
+    )
+
+    BatchGenWorker._report_completion(worker, seq.uuid, gathered_text="answer")
+    result = response_queue.get()
+
+    assert result["prompt_length"] == 100
+    assert result["decoded_length"] == 128
+
+
+def test_gather_completed_tokens_applies_stop_token_trimming(monkeypatch):
+    seq = SequenceEntry(
+        "stopped",
+        global_idx=9,
+        prompt_length=10,
+        max_decode_length=32,
+    )
+    seq.decoded_length = 2
+
+    class Tokenizer:
+        def decode(self, token_ids, *, skip_special_tokens):
+            assert token_ids == [42]
+            assert skip_special_tokens is True
+            return "answer"
+
+    worker = object.__new__(BatchGenWorker)
+    worker._uuid_to_local_map = {seq.uuid: 0}
+    worker.global_batch = SimpleNamespace(get_sequence=lambda uuid: seq)
+    worker.query_book = {
+        0: SimpleNamespace(decoded_tokens=torch.tensor([[42, 154827]]))
+    }
+    worker.world_size = 1
+    worker.eos_token_ids = {154820, 154827, 154829}
+    worker.pad_token_id = 154820
+    worker.detokenization_include_special_tokens = False
+    worker.tokenizer = Tokenizer()
+
+    def gather(output, local):
+        output[0] = local
+
+    monkeypatch.setattr(worker_module.dist, "all_gather_object", gather)
+
+    assert worker._gather_completed_tokens([seq.uuid]) == {seq.uuid: "answer"}
+
+
+def test_report_completion_fallback_applies_stop_token_trimming():
+    seq = SequenceEntry(
+        "local-fallback",
+        global_idx=11,
+        prompt_length=10,
+        max_decode_length=32,
+    )
+    seq.decoded_tokens = torch.tensor([[42, 154827]])
+    seq.decoded_length = 2
+    seq.status = SequenceStatus.COMPLETED
+
+    class Tokenizer:
+        def decode(self, token_ids, *, skip_special_tokens):
+            assert token_ids == [42]
+            assert skip_special_tokens is True
+            return "answer"
+
+    worker = object.__new__(BatchGenWorker)
+    worker.global_batch = SimpleNamespace(get_sequence=lambda uuid: seq)
+    worker._uuid_to_local_map = {}
+    worker._local_to_uuid_map = {}
+    worker.query_book = {}
+    worker._free_local_indices = set()
+    worker.rank = 0
+    worker._response_queue = SimpleQueue()
+    worker.eos_token_ids = {154820, 154827, 154829}
+    worker.pad_token_id = 154820
+    worker.detokenization_include_special_tokens = False
+    worker.tokenizer = Tokenizer()
+    worker._get_finish_reason = lambda _seq: "stop"
+
+    worker._report_completion(seq.uuid)
+
+    assert worker._response_queue.get()["text"] == "answer"