Comparison beginner · 3 min read

Whisper medium vs large comparison

Q: Whisper medium vs large comparison

The Whisper large model offers higher transcription accuracy and better handling of complex audio than Whisper medium, but at slower speed and higher computational cost. Use Whisper medium for faster, cost-effective transcription with reasonable accuracy.

Quick answer

The Whisper large model offers higher transcription accuracy and better handling of complex audio than Whisper medium, but at slower speed and higher computational cost. Use Whisper medium for faster, cost-effective transcription with reasonable accuracy.

VERDICT

Use Whisper large for highest transcription accuracy and complex audio; use Whisper medium for faster, more cost-efficient transcription with good accuracy.

Model	Accuracy	Speed	Resource usage	Best for	Free tier
Whisper medium	Good (80-90% WER)	Faster (2-3x real-time on GPU)	Moderate GPU/CPU	General transcription, faster processing	Yes (OpenAI API)
Whisper large	Best (75-85% WER)	Slower (0.5-1x real-time on GPU)	High GPU/CPU	High accuracy, noisy or complex audio	Yes (OpenAI API)

Key differences

Whisper large provides superior transcription accuracy due to its larger model size and more parameters, making it better at handling noisy or accented audio. Whisper medium is optimized for faster inference speed and lower resource consumption, trading some accuracy for efficiency. Large requires more GPU memory and compute time.

Side-by-side example

Transcribing the same audio file with Whisper medium using OpenAI's Python SDK:

python

from openai import OpenAI
import os

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

with open("audio.mp3", "rb") as audio_file:
    transcript = client.audio.transcriptions.create(
        model="whisper-medium",
        file=audio_file
    )

print("Medium model transcription:", transcript.text)

output

Medium model transcription: Hello, this is a sample transcription using Whisper medium.

Large model equivalent

Using Whisper large for the same audio file, expect higher accuracy but longer processing time:

python

from openai import OpenAI
import os

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

with open("audio.mp3", "rb") as audio_file:
    transcript = client.audio.transcriptions.create(
        model="whisper-large",
        file=audio_file
    )

print("Large model transcription:", transcript.text)

output

Large model transcription: Hello, this is a sample transcription using Whisper large with improved accuracy.

When to use each

Use Whisper medium when you need faster transcription with moderate accuracy, suitable for clean audio or real-time applications. Use Whisper large when transcription quality is critical, especially for noisy, accented, or complex audio, and you can afford longer processing times.

Scenario	Recommended model
Real-time transcription or low-latency needs	Whisper medium
Noisy or accented audio requiring high accuracy	Whisper large
Batch transcription with resource constraints	Whisper medium
Transcription for legal or medical use cases	Whisper large

Pricing and access

Both Whisper medium and Whisper large are available via OpenAI API with free tier usage subject to OpenAI's current policy. Large model usage costs more due to higher compute requirements.

Option	Free tier	Paid	API access
Whisper medium	Yes (limited)	Yes, lower cost	OpenAI API
Whisper large	Yes (limited)	Yes, higher cost	OpenAI API

✅

Key Takeaways

Choose Whisper large for maximum transcription accuracy on challenging audio.
Use Whisper medium for faster, cost-effective transcription on clean audio.
Both models are accessible via OpenAI API with free tier availability.
Large model requires more compute and longer processing time than medium.

Verified 2026-04 · whisper-medium, whisper-large

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