StableLM Ratings (September 2025)
This page breaks down StableLM ratings from all verified review sources to give you a clear, unbiased view of what real users think in September 2025.
We’ve aggregated the latest scores from all major platforms like Trustpilot, G2, Capterra and many more to help you understand StableLM’s overall reputation. This data-driven overview will show you exactly how StableLM is rated - including how it compares to competitors.
StableLM's Overall Rating Summary
- Average Rating:4.8/5
- Total Reviews:11 reviews
- Sources:G2
- Last updated:September 1, 2025
StableLM Ratings by Source
| Platform | Rating | Reviews | Last Update |
|---|---|---|---|
 G2 | 4.8/5 | 11+ reviews | 01.09.25 |
What is StableLM?
StableLM 3B 4E1T is a transformer-based, decoder-only language model pre-trained on 1 trillion tokens of diverse English and code datasets for four epochs, featuring partial Rotary Position Embeddings, SwiGLU activation, and LayerNorm. The model is available in two versions: the base model and StableLM Zephyr 3B, an instruction fine-tuned version for chat-based applications. It supports integration with Flash Attention 2 for optimized performance and can be used for various tasks such as causal language modeling, sequence classification, and token classification.
🤔 StableLM vs Competitors: How Do Ratings Compare?
Check out StableLM AlternativesFrequently Asked Questions
How trustworthy is StableLM?
According to G2 review site StableLM holds rating of 4.8/5, highlights excellent reputation and user approval as of September 2025.
How many stars is StableLM rated?
StableLM has an average star rating of 4.8 out of 5 stars based on average of 11 reviews as of September 2025.
Where does the rating data come from?
The ratings are pulled from StableLM public reviews pages on platforms: G2. We update these ratings monthly to ensure they reflect the most current customer feedback.
How overall average rating for StableLM is calculated?
The overall average rating is calculated by taking the weighted average of all ratings, using the total reviews count from each platform as the weight.