Ox Alpha is a reasoning-focused AI model built for coding, sustained agentic work, and complex problem solving. It offers a 1M-token context window, multimodal input for text, images, and video, plus tool calling and structured JSON output. You can try it free at oxalpha.com without signing up, or access it through the OpenRouter API. It is suited to repository-scale coding, long-running agent tasks, cross-layer debugging, and multimodal UI analysis, with enough context to work with large codebases without chunking.

• 1M-Token Context Window: Fits entire codebases, long specs, or hours of transcripts in a single prompt without chunking or retrieval workarounds.
• Reasoning-First Architecture: Plans and verifies its work with a visible chain of thought across low, high, and max effort levels.
• Multimodal Input: Accepts text, images, and video, plus PDF, Word, Excel, and PowerPoint files for richer coding and debugging context.
• Tool & Function Calling: Supports structured JSON output and function calls for direct integration into agentic workflows and production systems.
• Long Output Support: Supports large responses for extended coding tasks, documentation, and other workloads that need more output in a single response.
• OpenAI-Compatible API: Available through OpenRouter, allowing developers to use a familiar API format and switch between supported models with minimal code changes.
• Coding Agent Support: Its long context, reasoning, and tool-use capabilities make it suitable for coding agents, automated workflows, and large software projects.

• Massive Context: The 1M-token window handles full repositories and long specs without splitting content.
• Strong for Agentic Coding: Users report solid results on bulk branch maintenance, CI debugging, and multi-step refactors.
• Free to Try: No login on the web app and $0 API pricing during the preview period lowers the barrier to testing.
• Tool Support: Native tool calling and structured JSON output make Ox Alpha useful for AI agents and automated software workflows.
• Transparent Reasoning: Visible chain of thought helps you audit how the model arrived at answers.
• Multimodal Debugging: Pair screenshots or short video with prompts to reason across visual and textual signals.

• Newer Model: Ox Alpha is relatively new, so performance, availability, and access terms may change over time.
• Large Context Has Limits: A 1M-token context window does not guarantee accurate results on every large document or codebase.
• Preview Availability: Ox Alpha is currently free to try, but its pricing, access limits, availability, or model details could change as the project moves beyond its stealth preview.

• Repository-Scale Code Analysis: Drop an entire codebase into the context window and ask Ox Alpha to map modules, spot dead code, or explain how services connect. Useful when onboarding to an unfamiliar project.
• Pull Request and Branch Maintenance: Use Ox Alpha to review changes, investigate conflicts, suggest fixes, and work through multi-step software maintenance tasks. Its long-horizon coding design is suited to sustained engineering work.
• CI Debugging: Feed failing tests, logs, and related modules together so the model can trace regressions across layers instead of guessing at retries.
• Multimodal UI Reasoning: Combine screenshots or short clips of a broken UI with source files to get targeted fixes for layout, state, or event issues.
• Agent Workflow Design: Use tool calling and structured JSON output to build multi-step AI agents that can plan tasks, call external tools, and return predictable data to your application.
• Long-Form Refactoring: Generate detailed multi-file refactor plans and diffs using the 131K output token capacity, then validate with your normal test suite.