diff --git a/python/freetoken/core.py b/python/freetoken/core.py
index 269b94ffe9..739f7f994d 100644
--- a/python/freetoken/core.py
+++ b/python/freetoken/core.py
@@ -27,7 +27,7 @@ class SamplingParams:
 
     @property
     def is_greedy(self) -> bool:
-        return (self.temperature <= 0.0 or self.top_k == 1) and self.top_p == 1.0
+        return self.temperature <= 0.0 or self.top_k == 1
 
 
 @dataclass(eq=False)
diff --git a/python/freetoken/engine/sample.py b/python/freetoken/engine/sample.py
index 01d14b1aab..3bdae76957 100644
--- a/python/freetoken/engine/sample.py
+++ b/python/freetoken/engine/sample.py
@@ -15,6 +15,7 @@ class BatchSamplingArgs:
     temperatures: torch.Tensor | None
     top_k: torch.Tensor | None = None
     top_p: torch.Tensor | None = None
+    greedy_mask: torch.Tensor | None = None
 
 
 def make_device_tensor(data: List, dtype: torch.dtype, device: torch.device) -> torch.Tensor:
@@ -57,24 +58,42 @@ class Sampler:
 
     def prepare(self, batch: Batch) -> BatchSamplingArgs:
         params = [r.sampling_params for r in batch.reqs]
-        if all(p.is_greedy for p in params):
+        is_greedy = [p.is_greedy for p in params]
+        if all(is_greedy):
             return BatchSamplingArgs(temperatures=None)
 
         MIN_P = MIN_T = 1e-6
-        ts = [max(0.0 if p.is_greedy else p.temperature, MIN_T) for p in params]
-        top_ks = [p.top_k if p.top_k >= 1 else self.vocab_size for p in params]
-        top_ps = [min(max(p.top_p, MIN_P), 1.0) for p in params]
+        # Greedy outputs are selected explicitly in sample(); use neutral sampling
+        # parameters for those rows instead of approximating argmax at low temperature.
+        ts = [1.0 if g else max(p.temperature, MIN_T) for p, g in zip(params, is_greedy)]
+        top_ks = [
+            p.top_k if not g and p.top_k >= 1 else self.vocab_size
+            for p, g in zip(params, is_greedy)
+        ]
+        top_ps = [
+            1.0 if g else min(max(p.top_p, MIN_P), 1.0)
+            for p, g in zip(params, is_greedy)
+        ]
         temperatures = make_device_tensor(ts, torch.float32, self.device)
         top_k, top_p = None, None
         if any(k != self.vocab_size for k in top_ks):
             top_k = make_device_tensor(top_ks, torch.int32, self.device)
         if any(p < 1.0 for p in top_ps):
             top_p = make_device_tensor(top_ps, torch.float32, self.device)
-        return BatchSamplingArgs(temperatures, top_k=top_k, top_p=top_p)
+        greedy_mask = (
+            make_device_tensor(is_greedy, torch.bool, self.device) if any(is_greedy) else None
+        )
+        return BatchSamplingArgs(temperatures, top_k=top_k, top_p=top_p, greedy_mask=greedy_mask)
 
     @nvtx_annotate("Sampler")
     def sample(self, logits: torch.Tensor, args: BatchSamplingArgs) -> torch.Tensor:
         with torch.cuda.nvtx.range("Sampler"):
             if args.temperatures is None:  # greedy sampling
                 return torch.argmax(logits, dim=-1)
-            return sample_impl(logits.float(), args.temperatures, args.top_k, args.top_p)
+            tokens = sample_impl(logits.float(), args.temperatures, args.top_k, args.top_p)
+            if args.greedy_mask is not None:
+                # Mixed batches still run probability sampling for all rows, but
+                # greedy rows must follow argmax's deterministic tie-breaking.
+                greedy_tokens = torch.argmax(logits, dim=-1).to(tokens.dtype)
+                tokens = torch.where(args.greedy_mask, greedy_tokens, tokens)
+            return tokens