Martian has launched Ship, an innovative drop-in endpoint designed to replicate model outputs at a 50% lower cost without sacrificing performance. Unlike traditional cost-saving tools that downgrade to weaker models, Ship maintains the capabilities of the reference model by allowing for the blending of various models and tools. This system specializes in inference-time routing, adding flexibility that mirrors just-in-time compilation, rather than freezing a model beforehand. The launch highlights two key guarantees of the system: behavioral equivalence and capability equivalence, ensuring high-quality performance at a reduced price.

Opus: Opus is a frontier AI model referenced in the context of high-intelligence inference tasks. Ship integrates Opus as one of the supported reference models, offering equivalent behavior and capabilities through its specialized endpoint. This allows users to access Opus-level outputs without switching to weaker alternatives for cost savings.
Martian: Martian is an AI-focused company operating under the handle @withmartian that develops tools for model inference and optimization. It recently launched Ship, a specialized endpoint designed to deliver frontier model performance at reduced cost through runtime techniques. The company’s approach emphasizes blending multiple components like models, tools, and ensembles while maintaining equivalence guarantees to the original reference model.
GPT 5.6 Sol: GPT 5.6 Sol is a frontier AI model referenced in the context of high-intelligence inference tasks. Ship integrates GPT 5.6 Sol as one of the supported reference models, offering equivalent behavior and capabilities through its specialized endpoint. This allows users to access GPT 5.6 Sol-level outputs without switching to weaker alternatives for cost savings.
@withmartian: Martian is an AI-focused company operating under the handle @withmartian that develops tools for model inference and optimization. It recently launched Ship, a specialized endpoint designed to deliver frontier model performance at reduced cost through runtime techniques. The company’s approach emphasizes blending multiple components like models, tools, and ensembles while maintaining equivalence guarantees to the original reference model.

Quality Assurance: Ship provides a quality SLA focused on behavioral equivalence and capability equivalence to the reference model.
Model Compatibility: The endpoint supports blending models, tools, harnesses, cascades, or ensembles to match the exact abilities of the original model.
Inference Optimization: Ship introduces runtime specialization for inference-time routing instead of relying on frozen, ahead-of-time model training.