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OpenCode Zen
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Code-optimized AI for developer workflows
ActionFlows connects to OpenCode Zen — the AI coding platform providing model access optimized for code generation, completion, and developer-facing workflows. Connect once with your OpenCode Zen API key, then drop coding-specialized models into any flow as native steps.
Authentication, streaming, fill-in-the-middle support, and code-specific structured output — all handled at the integration layer. For builders shipping developer tools, code generation features, or agentic coding workflows, this is the inference layer where code is the design center.
Models tuned for code, not text-with-code
OpenCode Zen serves models specifically tuned for code generation, completion, and understanding — strong performance on HumanEval, MBPP, and SWE-bench across major programming languages. Trained with code-heavy distribution rather than general text with code as one slice.
For builders shipping coding tools where code quality is the user-perceived product quality, this is the inference layer trained for the use case.
Features built for actual developer workflows
- Fill-in-the-middle (FIM) support for editor autocomplete integrations
- Multi-language coverage across Python, TypeScript, Go, Rust, Java, and 80+ languages
- Structured output for code review, refactoring suggestions, and inline edits
- Long-context support for multi-file refactors and codebase-wide reasoning
- OpenAI-compatible API for portable code across providers
Built for agentic coding workflows
OpenCode Zen's models support tool use, structured output, and multi-step reasoning patterns that agentic coding workflows depend on — file system manipulation, test execution, error analysis, and iterative debugging. Not just code completion but the full agentic loop that modern developer tools require.
For builders shipping coding agents, autonomous developer tools, or AI-assisted IDEs, this is the inference layer where the capability profile matches the workflow.
Why OpenCode Zen
For developer tool products, the structural quality bar is code generation accuracy on real codebases — not general benchmarks but the practical question of whether the model produces code that passes review.
OpenCode Zen is the inference layer where code is the training center rather than a slice of broader text generation. For builders treating code generation as the core product capability, this is the right model line.
Code-tuned models. Developer-first.
Code Generation
Models tuned for code-heavy distribution. Strong HumanEval, MBPP, and SWE-bench performance across major languages.
Fill-in-the-Middle
FIM support for editor autocomplete and inline completion. The capability that turns chat completion into developer tooling.
Agentic Coding
Tool use, structured output, and multi-step reasoning for agentic coding workflows. The full loop modern developer tools require.
Multi-File Reasoning
Long-context support for codebase-wide refactors and cross-file analysis. Reasoning across repositories without manual chunking.
Frequently asked questions
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