diff --git a/src/pipecat/services/groq/stt.py b/src/pipecat/services/groq/stt.py
index 1267dd85f..28d0c6f83 100644
--- a/src/pipecat/services/groq/stt.py
+++ b/src/pipecat/services/groq/stt.py
@@ -58,7 +58,8 @@ class GroqSTTService(BaseWhisperSTTService):
         kwargs = {
             "file": ("audio.wav", audio, "audio/wav"),
             "model": self.model_name,
-            "response_format": "json",
+            # Use verbose_json to get probability metrics
+            "response_format": "verbose_json" if self._include_prob_metrics else "json",
             "language": self._language,
         }
 
diff --git a/src/pipecat/services/openai/stt.py b/src/pipecat/services/openai/stt.py
index 208c68d34..75a6f920c 100644
--- a/src/pipecat/services/openai/stt.py
+++ b/src/pipecat/services/openai/stt.py
@@ -61,6 +61,15 @@ class OpenAISTTService(BaseWhisperSTTService):
             "language": self._language,
         }
 
+        if self._include_prob_metrics:
+            # GPT-4o-transcribe models only support logprobs (not verbose_json)
+            if self.model_name in ("gpt-4o-transcribe", "gpt-4o-mini-transcribe"):
+                kwargs["response_format"] = "json"
+                kwargs["include"] = ["logprobs"]
+            else:
+                # Whisper models support verbose_json
+                kwargs["response_format"] = "verbose_json"
+
         if self._prompt is not None:
             kwargs["prompt"] = self._prompt
 
diff --git a/src/pipecat/services/sambanova/stt.py b/src/pipecat/services/sambanova/stt.py
index 71a709420..2f4e70c77 100644
--- a/src/pipecat/services/sambanova/stt.py
+++ b/src/pipecat/services/sambanova/stt.py
@@ -8,6 +8,8 @@
 
 from typing import Any, Optional
 
+from loguru import logger
+
 from pipecat.services.whisper.base_stt import BaseWhisperSTTService, Transcription
 from pipecat.transcriptions.language import Language
 
@@ -54,6 +56,14 @@ class SambaNovaSTTService(BaseWhisperSTTService):  # type: ignore
     async def _transcribe(self, audio: bytes) -> Transcription:
         assert self._language is not None  # Assigned in the BaseWhisperSTTService class
 
+        if self._include_prob_metrics:
+            # https://docs.sambanova.ai/docs/en/features/audio#request-parameters
+            logger.warning(
+                "SambaNova STT does not support probability metrics "
+                "(include_prob_metrics parameter has no effect). "
+                "Check their docs: https://docs.sambanova.ai/docs/en/features/audio#request-parameters for more details."
+            )
+
         # Build kwargs dict with only set parameters
         kwargs = {
             "file": ("audio.wav", audio, "audio/wav"),
diff --git a/src/pipecat/services/whisper/utils.py b/src/pipecat/services/whisper/utils.py
new file mode 100644
index 000000000..5d685d459
--- /dev/null
+++ b/src/pipecat/services/whisper/utils.py
@@ -0,0 +1,85 @@
+#
+# Copyright (c) 2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import math
+from typing import Optional
+
+from pipecat.frames.frames import TranscriptionFrame
+
+
+def extract_whisper_probability(frame: TranscriptionFrame) -> Optional[float]:
+    """Extract probability from Whisper-based TranscriptionFrame result.
+
+    Works with Groq, OpenAI Whisper, or other Whisper-based services that use
+    verbose_json format with segments containing avg_logprob.
+
+    Converts avg_logprob to probability.
+
+    Args:
+        frame: TranscriptionFrame with result from GroqSTTService or OpenAISTTService
+            (when include_prob_metrics=True and using Whisper models).
+
+    Returns:
+        Probability (0-1) if available, None otherwise.
+
+    Example:
+        >>> from pipecat.services.groq.stt import GroqSTTService
+        >>> from pipecat.services.whisper.utils import extract_whisper_probability
+        >>>
+        >>> stt = GroqSTTService(include_prob_metrics=True)
+        >>> # ... use stt in pipeline ...
+        >>> # In your frame processor:
+        >>> if isinstance(frame, TranscriptionFrame):
+        >>>     prob = extract_whisper_probability(frame)
+        >>>     if prob:
+        >>>         print(f"Transcription confidence: {prob:.2%}")
+    """
+    if not frame.result:
+        return None
+
+    # Whisper verbose_json format: response.segments[0].avg_logprob
+    if hasattr(frame.result, "segments") and frame.result.segments:
+        segment = frame.result.segments[0]
+        avg_logprob = getattr(segment, "avg_logprob", None)
+        if avg_logprob is not None:
+            return math.exp(avg_logprob)
+
+    return None
+
+
+def extract_openai_gpt4o_logprobs(frame: TranscriptionFrame) -> Optional[list]:
+    """Extract logprobs from OpenAI GPT-4o-transcribe TranscriptionFrame result.
+
+    Args:
+        frame: TranscriptionFrame with result from OpenAISTTService
+            using GPT-4o-transcribe model (when include_prob_metrics=True).
+
+    Returns:
+        List of logprobs if available, None otherwise.
+
+    Example:
+        >>> from pipecat.services.openai.stt import OpenAISTTService
+        >>> from pipecat.services.whisper.utils import extract_openai_gpt4o_logprobs
+        >>>
+        >>> stt = OpenAISTTService(model="gpt-4o-transcribe", include_prob_metrics=True)
+        >>> # ... use stt in pipeline ...
+        >>> # In your frame processor:
+        >>> if isinstance(frame, TranscriptionFrame):
+        >>>     logprobs = extract_openai_gpt4o_logprobs(frame)
+        >>>     if logprobs:
+        >>>         # Calculate average logprob
+        >>>         avg_logprob = sum(logprobs) / len(logprobs)
+        >>>         prob = math.exp(avg_logprob)
+        >>>         print(f"Transcription confidence: {prob:.2%}")
+    """
+    if not frame.result:
+        return None
+
+    # OpenAI GPT-4o-transcribe format: response.logprobs
+    if hasattr(frame.result, "logprobs"):
+        return frame.result.logprobs
+
+    return None