Overview
Stable Code is Stability AI’s family of code-focused large language models (3B‑parameter class) designed for code completion, instruction-following, code translation, explanation, Fill‑in‑the‑Middle (FIM), and long‑context code tasks. The models emphasize hardware efficiency for on‑device/edge use, expanded context windows (defaults up to 16,384 tokens with options to extend far higher), support for many popular programming languages, and availability via downloadable weights on Hugging Face as well as commercial access through Stability AI membership or community/enterprise agreements. Releases include Stable Code Alpha (2023), Stable Code 3B (Jan 2024), and Stable Code Instruct 3B (Mar 2024). Pricing and public per-tier commercial terms are not published on the Stability AI Core Models page; commercial use is indicated as available via Membership / Community / Enterprise agreements.
Key Features
Edge and on-device inference
Models engineered to run efficiently on laptops and edge hardware without dedicated GPUs, enabling private offline use.
Instruction tuning
Stable Code Instruct 3B is tuned to follow natural language instructions for coding tasks including generation, explanation, translation, and debugging prompts.
Long context windows
Default 16k token context with capability to extend (via Rotary Embeddings) to extremely long contexts for multi-file or large codebases.
Fill-in-the-Middle (FIM) support
Enables flexible completion patterns within existing code bodies to support mid-sequence edits and completions.
Multi-language code coverage
Trained and evaluated across many programming languages (notably Python, JavaScript, Java, C, C++, Go, SQL, PHP, Rust) with generalization to others.
Performance and efficiency optimizations
Smaller footprint (3B parameters) designed for hardware efficiency with support for FlashAttention and other runtime optimizations to improve latency and memory use.
Who Can Use This Tool?
- Developers:Accelerate coding, autocompletion, and refactoring with on‑device, instruction‑tuned code assistance.
- Researchers:Experiment with compact code LLMs and evaluate long‑context code modeling using published weights and model cards.
- Enterprises:Integrate commercially via membership or enterprise agreements for private, on‑prem or edge deployment and production usage.
Pricing Plans
Pricing information is not available yet.
Pros & Cons
✓ Pros
- ✓Compact and efficient: 3B‑parameter models optimized to be much smaller than many high‑performing code LLMs while keeping strong performance.
- ✓Edge/offline capable: Designed to run on laptops and edge devices, supporting private, local inference.
- ✓Long-context support: Default context up to 16,384 tokens; Rotary Embeddings enable extension to very long contexts (documented up to 100k tokens or more with adjustable rotary base).
- ✓Instruction‑tuned variant: Stable Code Instruct 3B supports natural‑language prompting and task instruction following.
- ✓Open availability: Model weights and cards published on Hugging Face for research/non‑commercial use; community resources and quantized builds exist.
✗ Cons
- ✗Commercial licensing unclear publicly: Commercial use requires a Membership or Community/Enterprise agreement; specific pricing and tiers are not publicly listed.
- ✗Smaller‑scale tradeoffs: At 3B parameters there may be cases where larger models provide stronger few‑shot or edge‑case performance.
- ✗Potential deployment complexity: Achieving highest performance (long context, FlashAttention, quantization) may require engineering work and specific runtimes.
Compare with Alternatives
| Feature | Stable Code | StarCoder | Code Llama |
|---|---|---|---|
| Pricing | N/A | N/A | N/A |
| Rating | 8.5/10 | 8.7/10 | 8.8/10 |
| Edge Readiness | Yes | Partial | Yes |
| Instruction Tuning | Yes | No | Partial |
| Fill-in-the-Middle | Yes | Yes | No |
| Context Window Size | Long context windows | Long context window | Varied context sizes |
| Multilingual Coverage | Yes | Yes | Yes |
| Inference Efficiency | Performance optimized inference | Multi query efficient inference | GGML optimized local inference |
| Local Tooling | Yes | Partial | Yes |
| Model Transparency | Yes | Yes | Yes |
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