Whisper AI Models Explained: Tiny vs Base vs Small vs Large

OpenAI's Whisper comes in 5 sizes. Here's which one you should actually use — with real benchmarks on accuracy, speed, and RAM usage on Apple Silicon.

Choosing the Right Whisper Model

OpenAI's Whisper is the engine behind most modern voice-to-text tools. But it comes in 5 sizes, and picking the wrong one means either slow performance or poor accuracy.

Here's the practical guide — no theory, just benchmarks.

The 5 Models

Model	Size	RAM Usage	Speed (M1)	English Accuracy
Tiny	75 MB	~400 MB	80x real-time	~88%
Base	140 MB	~500 MB	42x real-time	~93%
Small	460 MB	~1 GB	15x real-time	~96%
Large V3 Turbo	1.5 GB	~2.5 GB	5x real-time	~98%
Large V3	3 GB	~4 GB	2x real-time	~99%

*Benchmarks on Apple M1 Pro via WhisperKit. Real-time means 1 second of audio processed in X seconds.*

Which One Should You Use?

For daily typing/dictation: Base (140 MB)

This is the sweet spot. 93% accuracy catches virtually everything in normal speech. At 42x real-time, transcription feels instant. 500MB RAM means it runs alongside your other apps without issues.

This is what IndianWhisper recommends by default.

For noisy environments: Small (460 MB)

If you're in a coffee shop, coworking space, or have background noise, Small handles it better. The 3% accuracy bump comes from better noise separation and context understanding.

For professional/medical/legal: Large V3 Turbo (1.5 GB)

When accuracy matters more than speed — recording meeting notes, transcribing interviews, or dictating legal documents — Large V3 Turbo gives you 98% accuracy at a reasonable 5x real-time speed.

For maximum accuracy: Large V3 (3 GB)

Academic research, subtitling, or any use case where every word must be perfect. This is the same model OpenAI uses in their API. The downside: it uses 4GB RAM and is relatively slow.

For quick notes/commands: Tiny (75 MB)

Short phrases, voice commands, quick searches. If you're just saying "open terminal" or "send message", Tiny is more than enough and uses almost no resources.

Accuracy vs Speed: The Real Tradeoff

The jump from Tiny to Base is massive — 5% accuracy improvement for just 65MB more. That's the best value upgrade.

Base to Small gives you 3% more accuracy but triples the download size. Worth it only if accuracy matters a lot for your use case.

Small to Large is diminishing returns — 2-3% accuracy for 3-7x more resources. Most people will never notice the difference in daily use.

Indian English and Accents

Whisper was trained on 680,000 hours of multilingual audio. It handles Indian English well, especially the Base model and above. For Hindi/Hinglish mixed speech, Small or higher is recommended.

IndianWhisper adds an optional AI cleanup layer on top of Whisper — 7 LLM providers (Groq, Claude, OpenAI, Gemini) that fix any remaining transcription errors and add proper formatting.

Try Them All

IndianWhisper gives you all 5 models for free. Switch between them in Settings → Models. The model downloads once and works offline forever.

Start with Base. If you need more accuracy, try Small. Most people never need to go higher.