Groq Whisper vs OpenAI Whisper: Speed, Cost, and Accuracy for Windows Dictation
Both Groq and OpenAI offer Whisper as an API service. Both run the same underlying model. But they are not interchangeable - the performance difference between them is significant enough to change whether a dictation app feels instant or feels like it's waiting.
This post compares the two APIs directly on the metrics that matter for real-time Windows dictation: latency, throughput, cost, and accuracy. It's written for developers building or evaluating dictation tools, and for technical users who want to understand what's actually running under the hood.
Background: What Whisper Is and Why the Backend Matters
OpenAI's Whisper is an encoder-decoder transformer model for automatic speech recognition (ASR). The large-v3 variant - used by both APIs - achieves approximately 2.7% Word Error Rate (WER) on English LibriSpeech benchmarks. That's competitive with human transcription for standard speech.
The model architecture is fixed. What differs between providers is inference hardware and infrastructure. OpenAI runs Whisper on standard GPU clusters. Groq runs it on their custom Language Processing Units (LPUs) - silicon designed specifically to accelerate transformer inference. The same model, very different execution speed.
Latency: The Number That Changes Everything
For real-time dictation, end-to-end latency is the most important metric. This includes: audio capture time + API round trip + inference time + response parsing.
| Provider | Typical Latency (5s audio) | Typical Latency (10s audio) | Hardware |
|---|---|---|---|
| OpenAI Whisper API | 800ms–1.5s | 1.2s–2.5s | GPU (A100/H100) |
| Groq Whisper API | 150ms–280ms | 200ms–350ms | Groq LPU |
Groq's latency is roughly 5–8x faster than OpenAI's for typical dictation-length audio clips (3–15 seconds). This isn't a marginal improvement - it's the difference between a tool that feels like typing and one that feels like submitting a form and waiting.
The latency gap is largest for shorter clips, which is exactly what push-to-talk dictation generates. When someone holds a hotkey and speaks a sentence, the audio is typically 2–8 seconds. Groq handles this in under 250ms; OpenAI takes 800ms–1.5s.
Throughput and Rate Limits
For a dictation app used by one person, rate limits aren't usually the constraint. But for anyone building a service or using dictation heavily throughout the workday, limits matter.
| Provider | Free Tier | Paid Rate Limit (audio/min) | Concurrent Requests |
|---|---|---|---|
| OpenAI Whisper | None (pay-as-you-go) | ~50 req/min (Tier 1) | Scales with tier |
| Groq Whisper | 7,200 seconds audio/day free | ~100 req/min (paid) | Higher throughput |
Groq's free tier (7,200 seconds of audio per day) covers approximately 2 hours of continuous dictation - more than enough for most users. OpenAI has no free tier for Whisper; you pay from the first second.
Cost Comparison
| Provider | Price per Audio Minute | Price per Audio Hour | Model |
|---|---|---|---|
| OpenAI | $0.006 | $0.36 | whisper-1 |
| Groq | $0.002 | $0.12 | whisper-large-v3 |
Groq is 3x cheaper per audio minute than OpenAI, while also running a newer and more capable model version. OpenAI's API uses whisper-1 (equivalent to large-v2). Groq uses large-v3, which has better multilingual accuracy and lower hallucination rates on short clips.
For a user dictating an hour of audio per day (a heavy user), that's $0.12/day on Groq vs $0.36/day on OpenAI - but since Groq's free tier covers most of that at 7,200 seconds/day, the practical cost for dictate.app users is near zero at typical usage.
Accuracy: Same Model, Same Output?
Technically, both APIs run Whisper. But there are differences worth noting:
- OpenAI uses whisper-1 - equivalent to large-v2. WER on standard English is approximately 3.5–4%.
- Groq uses whisper-large-v3 - the improved successor. WER is approximately 2.7–3.2%. Better on accents, better on short clips, lower hallucination rate when audio ends abruptly.
The accuracy difference is real but not dramatic for most standard English dictation. Where it shows up more clearly is with:
- Non-native speaker accents
- Technical vocabulary and proper nouns
- Short utterances where the model has less context to work with
- Audio that ends mid-sentence (Groq's v3 hallucinates less)
Which Should You Use for Windows Dictation?
For real-time push-to-talk dictation, Groq is the clear choice:
- 5–8x lower latency - the difference between instant and noticeable lag
- 3x lower cost - significant at scale, irrelevant at personal use
- Better model version (large-v3 vs large-v2)
- Generous free tier for individual users
OpenAI's Whisper API makes more sense for:
- Batch processing (transcribing audio files where latency doesn't matter)
- Existing applications already integrated with the OpenAI SDK
- Use cases requiring OpenAI's ecosystem (fine-tuning, specific features)
How dictate.app Uses Groq Whisper
dictate.app routes all audio to Groq's Whisper API. When you hold the hotkey and speak, audio is captured locally, sent to Groq, and the transcription is returned in ~150–250ms. The text is then pasted into whichever Windows app has focus using clipboard injection - so it works in any application.
Audio goes to Groq and nowhere else. Groq's API does not store audio or use it for model training. dictate.app's own servers are not in the audio path.
Groq Whisper, Built for Windows
dictate.app uses Groq's Whisper API - whisper-large-v3, ~200ms latency, system-wide paste. $8.99/month with a 7-day free trial.
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If you're evaluating which Whisper backend to use for a Windows dictation integration, the numbers point clearly to Groq for real-time use cases. The latency alone makes it the right choice - and the better model version and lower cost make it an easy one.
Questions? Reach out at support@dictate.app or check the homepage for the full feature breakdown.