DeciCoder
DeciCoder is a family of open-source, decoder-only code completion models developed by Deci AI and released in August 2023.
Publisher review
DeciCoder is a family of open-source, decoder-only code completion models developed by Deci AI and released in August 2023. The flagship 1-billion-parameter model is designed for developers and teams who need fast, efficient code generation for Python, Java, and JavaScript without the overhead of larger models. It is not an instruction-tuned assistant; rather, it excels at single- and multi-line code completion when given a function signature, docstring, or code comment as context. The model is permissively licensed under Apache 2.0, making it suitable for commercial use, and is available on HuggingFace for local deployment or via Microsoft Foundry for API access at $0.07 per million input tokens.
DeciCoder-1B uses Grouped Query Attention with 4 key-value heads and 32 total heads across 20 transformer layers, a hidden size of 2048, and Rotary Position Embeddings. It was trained on 446 billion tokens from the Python, Java, and JavaScript subsets of the StarCoder Training Dataset using a Fill-in-the-Middle objective, giving it a context window of 2048 tokens. Its architecture was generated by Deci's proprietary Neural Architecture Search (AutoNAC) technology, which optimizes layer structure for throughput. On an NVIDIA A10 GPU, the model achieves 1,364 tokens/second with PyTorch and 3,889 tokens/second using Deci's Infery-LLM inference SDK. On HumanEval, it scores 19.1% pass@1 for Python, 18.4% for JavaScript, and 16.6% for Java.
DeciCoder competes directly with other open-source code models like SantaCoder (1.1B parameters) and CodeGen (multi-size family), as well as proprietary tools such as GitHub Copilot, Tabnine, and Amazon CodeWhisperer. Its key differentiator is the AutoNAC-optimized architecture, which delivers higher throughput and lower memory usage than SantaCoder on equivalent hardware. For example, DeciCoder-1B's 1.1B parameters fit in smaller GPU memory footprints while maintaining competitive accuracy. However, it lacks the multi-language breadth of StarCoder or the instruction-following capability of models like Claude Code or Devin, and its 2048-token context window is shorter than many modern code LLMs.
The honest trade-offs: DeciCoder-1B is narrowly focused on three languages and cannot handle instruction-based prompts—users must frame tasks as code comments or signatures. Its 2048-token context window limits its usefulness for large-file completions or repository-level tasks. While it outperforms SantaCoder on HumanEval, its absolute scores (19.1% Python pass@1) are modest compared to larger models like CodeGen-16B or proprietary services. Finally, achieving peak throughput requires Deci's proprietary Infery-LLM SDK, which is not freely available and requires a demo booking, meaning out-of-the-box PyTorch performance is significantly lower.
How it works
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1-billion parameter decoder
A 1.1B parameter auto-regressive transformer that generates code completions from a given prefix or middle context.
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Grouped Query Attention
Uses 4 key-value heads shared across 32 query heads to reduce memory and compute during inference.
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2048-token context window
Processes up to 2048 tokens of input context for single- or multi-line code completion tasks.
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Fill-in-the-Middle training
Trained to predict masked code segments in the middle of a sequence, enabling infilling completions.
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AutoNAC architecture search
Deci's proprietary Neural Architecture Search optimizes layer count, head dimensions, and hidden size for throughput.
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Rotary Position Embeddings
Encodes token positions using rotary embeddings, improving generalization to longer sequences.
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Apache 2.0 license
Permissively licensed for commercial use, allowing modification, distribution, and integration into proprietary products.
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HuggingFace integration
Available as a pre-trained checkpoint on HuggingFace, loadable via AutoModelForCausalLM with bfloat16 support.
Strengths and trade-offs
Strengths
- Achieves 3,889 tokens/second on an NVIDIA A10 GPU using Deci's Infery-LLM SDK, nearly 3x faster than PyTorch baseline.
- Outperforms SantaCoder on HumanEval with a 19.1% pass@1 score for Python versus SantaCoder's reported 16.0%.
- Uses only 1.1B parameters with Grouped Query Attention, reducing memory usage compared to SantaCoder's 1.1B parameter model.
- Permissively licensed under Apache 2.0, enabling unrestricted commercial use and fine-tuning.
Trade-offs
- Not suitable for instruction-based tasks; prompts must be framed as code comments or function signatures to get useful completions.
- Limited to Python, Java, and JavaScript; does not support other programming languages found in the StarCoder dataset.
- Context window of 2048 tokens is shorter than many modern code LLMs, restricting its use for large-file or repository-level completions.
- Peak throughput requires Deci's proprietary Infery-LLM SDK, which is not freely available and requires booking a demo.
Pricing context
$0.07 per million input tokens and $0.07 per million output tokens via Microsoft Foundry (provisioned throughput). The model is free to download and run locally under Apache 2.0.
Getting started with DeciCoder
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Download model from HuggingFace
Visit the DeciCoder-1B page on HuggingFace and download the model checkpoint using the transformers library. Load it with AutoModelForCausalLM in bfloat16 precision for efficient inference on your local machine.
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Set up Python environment
Install the required dependencies: transformers, torch, and accelerate. Create a virtual environment and run pip install transformers torch accelerate to ensure all libraries are available for model loading and inference.
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Configure code completion prompt
Frame your code completion task as a function signature, docstring, or code comment. For example, write a Python function header with a docstring describing the expected behavior. DeciCoder generates completions from this context, not from instructions.
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Run inference on a sample
Write a Python script that loads the model and tokenizer, then passes your prompt to model.generate(). Set max_new_tokens to 100 and temperature to 0.2 for deterministic output. Print the generated code to verify it works.
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Deploy via Microsoft Foundry
Sign up for Microsoft Foundry and provision an endpoint for DeciCoder. Use the provided API key to send requests at $0.07 per million input tokens. Integrate the endpoint into your IDE or CI/CD pipeline for automated code completion.
Frequently Asked Questions
What is DeciCoder and what does it do?
DeciCoder is an open-source, decoder-only code completion model family released by Deci AI in August 2023. It generates single- and multi-line code completions for Python, Java, and JavaScript when given a function signature, docstring, or comment as context.
How does DeciCoder compare to SantaCoder in performance?
DeciCoder-1B outperforms SantaCoder on HumanEval with a 19.1% pass@1 score for Python versus SantaCoder's 16.0%. It also achieves higher throughput and lower memory usage due to its AutoNAC-optimized architecture and Grouped Query Attention.
What languages does DeciCoder support?
DeciCoder supports three programming languages: Python, Java, and JavaScript. It was trained on 446 billion tokens from the StarCoder Training Dataset using a Fill-in-the-Middle objective, focusing exclusively on these languages for code completion tasks.
How fast is DeciCoder and what hardware does it need?
On an NVIDIA A10 GPU, DeciCoder-1B achieves 1,364 tokens/second with PyTorch and 3,889 tokens/second using Deci's Infery-LLM SDK. Its 1.1 billion parameters fit in smaller GPU memory footprints, making it efficient for local deployment.
Is DeciCoder free to use for commercial projects?
Yes, DeciCoder is released under the Apache 2.0 license, which permits commercial use, modification, and distribution. The model is free to download from HuggingFace, and API access via Microsoft Foundry costs $0.07 per million input and output tokens.
What are the main limitations of DeciCoder?
DeciCoder is not instruction-tuned, so prompts must be code comments or signatures. It only supports three languages, has a 2048-token context window, and peak throughput requires the proprietary Infery-LLM SDK, which is not freely available.
Alternatives
How DeciCoder compares
Direct head-to-head against 3 competitors. Picked by 7wData.
DeciCoder
- Pricing
- $0.07 per million input tokens and $0.07 per million output tokens via Microsoft Foundry (provisioned throughput). The model is free to download and run locally under Apache 2.0.
- Target
- DeciCoder is a family of open-source, decoder-only code completion models developed by Deci AI and released in August 2023.
- Strength
- Achieves 3,889 tokens/second on an NVIDIA A10 GPU using Deci's Infery-LLM SDK, nearly 3x faster than PyTorch baseline.
- Watch for
- Not suitable for instruction-based tasks; prompts must be framed as code comments or function signatures to get useful completions.
GitHub Copilot
- Pricing
- Free tier; Pro $10/month; Business $19/user/month
- Target
- Developers using VS Code, JetBrains, or Neovim
- Deployment
- Cloud; IDE plugin
- Strength
- Deep IDE integration with autocomplete and chat
- Watch for
- Pro plan limits on completions and chat messages
Cursor
- Pricing
- Free tier; Pro $20/month; Business $40/user/month
- Target
- Developers wanting AI-native IDE with multi-model access
- Deployment
- Desktop app; cloud sync
- Strength
- Full IDE with multi-model support and background tasks
- Watch for
- Expensive on-demand pricing after hitting usage limits
Claude Code
- Pricing
- $20/month for Claude Pro; usage-based via API
- Target
- Developers preferring CLI-based AI coding agent
- Deployment
- CLI tool; SDK available
- Strength
- Agentic CLI with SDK for custom workflows
- Watch for
- CLI can be buggy with text rendering issues
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Sources
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