Whisper Small vs Whisper Medium
Side-by-side comparison of hardware requirements, quantization options, and specifications to help you choose the right model for your device.
Specifications Comparison
| Spec | Whisper Small | Whisper Medium |
|---|---|---|
| Parameters | 0.24B | 0.77B |
| Architecture | whisper | whisper |
| License | MIT | MIT |
| Context Length | N/A | N/A |
| Category | Speech Recognition | Speech Recognition |
| Author | OpenAI | OpenAI |
| HF Downloads | 2.6M | 711.8K |
| VRAM Range | 0.95 - 0.95 GB | 1.93 - 1.93 GB |
| Quantizations | 1 options | 1 options |
| Best Quality Score | 85% | 92% |
Quantization Options
Whisper Small
Whisper Medium
In-depth comparison
Whisper Medium is the better choice for most users due to its higher accuracy (92% vs 85%). However, Whisper Small is ideal for resource-constrained environments due to its lower VRAM requirement (0.9GB vs 1.9GB).
When to choose Whisper Small
Whisper Small is the better pick when working with devices that have limited VRAM, such as older laptops or embedded systems. It offers a good balance between accuracy and resource usage, making it suitable for real-time applications where computational efficiency is crucial. Additionally, its smaller size means faster loading times and potentially lower latency, which can be beneficial for interactive applications.
When to choose Whisper Medium
Whisper Medium is the better choice when accuracy is a top priority, especially in professional settings or for tasks requiring high fidelity transcriptions. With a best quality score of 92%, it outperforms Whisper Small in terms of transcription accuracy, making it ideal for creating subtitles, transcribing meetings, and other scenarios where precision is critical. Despite its higher VRAM requirement, it is still manageable on modern consumer GPUs.
Quality
Whisper Medium clearly outperforms Whisper Small in terms of output quality, with a best quality score of 92% compared to 85%. The additional parameters in Whisper Medium allow it to capture more nuanced details in speech, leading to more accurate transcriptions. While both models are trained by OpenAI, the larger size of Whisper Medium provides a significant edge in accuracy.
Performance & hardware fit
Whisper Small requires only 0.9GB of VRAM, making it suitable for devices with limited resources, while Whisper Medium needs 1.9GB, which is more demanding but still within the range of many consumer GPUs. In terms of speed, Whisper Small will generally load and process faster due to its smaller size, which can be advantageous for real-time applications. However, the trade-off is a noticeable drop in accuracy.
Use-case fit
| coding | Whisper Small | Whisper Small's lower VRAM requirement makes it more suitable for coding on older or less powerful machines. |
| creative writing | Whisper Medium | Whisper Medium's higher accuracy ensures more precise transcriptions, which is beneficial for creative writing where nuance and detail matter. |
| RAG / retrieval | Whisper Medium | Whisper Medium's superior accuracy is crucial for RAG/retrieval tasks, where the quality of the transcribed text directly impacts the effectiveness of the retrieval system. |
| agent / tool use | Whisper Small | Whisper Small's lower resource requirements make it a better fit for agents or tools running on constrained devices, ensuring smoother operation. |
| running on consumer GPU (8-12GB) | Whisper Medium | Both models can run on consumer GPUs, but Whisper Medium's higher accuracy makes it the preferred choice for most users with sufficient VRAM. |
| long context (16K+) | Tie | Neither model is specifically designed for long context tasks, so the choice depends more on the available VRAM and the need for accuracy versus resource efficiency. |
Whisper Medium wins for most users due to its superior accuracy, making it ideal for professional and high-fidelity tasks. Whisper Small is the clear winner for users with limited VRAM or who prioritize real-time performance over absolute accuracy.