Zen Coder: Code AI from 4B to 480B

Zen Coder is a complete family of code-specialized models from edge to frontier. The lineup spans a 4B model that runs on a laptop to a 480B MoE frontier model for cloud-scale agentic coding. Every model in the family is purpose-trained on code.

The Lineup

Model	Parameters	Active Params	Architecture	Context	Target
Zen Coder 4B	4B	4B	Dense	128K	Edge, IDE plugins
Zen Coder 35B	35B	35B	Dense	128K	Developer workstations
Zen Coder 480B	480B	~40B	MoE	128K	Cloud, agentic systems

Zen Coder 480B uses the Zen MoDE (Mixture of Distilled Experts) architecture: 480B total parameters with approximately 40B active per forward pass. Frontier-class output at a fraction of the inference compute of a comparable dense model.

Extended Thinking

Every Zen Coder model supports extended thinking mode, where the model reasons through a problem before generating output. For code generation, this means analyzing the problem structure, planning an approach, and verifying correctness before writing a line.

Standard context is 128K tokens. In extended thinking mode, the reasoning trace can consume up to 512K tokens of internal scratchpad before final output -- enabling the model to think through deeply complex algorithmic problems, multi-file refactoring plans, and system architecture decisions.

Language Coverage

100+ programming languages with deep training coverage in:

Tier 1 (highest coverage): Python, TypeScript, JavaScript, Go, Rust, Java, C, C++, C#, SQL

Tier 2: Swift, Kotlin, Ruby, PHP, Scala, Haskell, Lua, Bash/Zsh, PowerShell, R, Julia

Tier 3: Zig, Nim, Elixir, Erlang, Clojure, F#, COBOL, Fortran, Assembly, and 70+ more

Coverage means not just syntax but idiomatic patterns, ecosystem conventions, and framework-specific knowledge. Zen Coder 480B understands Django, FastAPI, Rails, Spring Boot, and similar frameworks as first-class targets.

MCP Integration

Zen Coder integrates natively with the Model Context Protocol. In an MCP-enabled environment, Zen Coder can:

• Read and write files through the filesystem tool
• Execute code and observe output
• Search codebases with semantic tools
• Call APIs with the fetch tool
• Maintain memory across sessions

This is not bolted-on tool use -- MCP integration was a training objective for the agentic variants.

Agentic Coding

Zen Coder is built for multi-step coding tasks. A single prompt can span:

1. Understanding the existing codebase from context
2. Planning the implementation
3. Writing tests first (TDD)
4. Implementing to pass the tests
5. Refactoring for clarity
6. Writing documentation

With 128K context, Zen Coder can hold an entire mid-size codebase in working memory. With extended thinking at 512K, it can reason about architectural decisions before touching a file.

Available Formats

Format	Description
SafeTensors	Full precision GPU inference
GGUF Q4_K_M	4-bit quantized, CPU/edge
GGUF Q8_0	8-bit quantized, near-lossless
MLX	Apple Silicon native

Zen Coder 4B in GGUF Q4 runs on any modern laptop with no GPU required.

Get Zen Coder

• HuggingFace: huggingface.co/zenlm
• Hanzo Cloud API: Available at api.hanzo.ai/v1/chat/completions with model zen-coder-480b
• Hanzo Dev: Zen Coder is the default model in Hanzo Dev, our coding agent
• Zen LM: zenlm.org -- benchmarks and agentic setup guides

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Zach Kelling is the founder of Hanzo AI, Techstars '17.