Better performance than any single LLM

Route every request to the LLM that gives the best result at the lowest cost. By leveraging the collective intelligence of many models, we can exceed the quality of even the best individual frontier models at a fraction of the cost.

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SKIP ↓

Imagine 2 students take a test. Anna scores 70%, Ben scores 30%.

Individually, neither meets the 90% production threshold.

But if Anna got the first questions right and Ben hits the last ones...

Models will do better at different prompts because they are trained on different datasets.

Routing each question to the correct student would achieve 100% overall.

The key is to predict who handles which question best. And we do.

Even five students each scoring only 20% could together achieve more than 90%

if their correct answers don't overlap and you know which student handles which question best.

Now, you can see how Martian can achieve a radical increase in quality at a lower cost

You just have to think of each student as an LLM, and each question as a prompt.

35%

CLAUDE
HAIKU

55%

CLAUDE
SONNET

75%

MARTIAN
ROUTER®

SOLUTIONS

Routing for AI Agents

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Customer Service

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AI TOOLS

Pioneers in interpretability tools

We are pioneering a groundbreaking research approach focused on understanding model capabilities while creating and patenting advanced AI tools.

Airlock®

Maintain necessary compliance standards while accelerating AI integration timelines. As soon as a new model is released, you know whether you can use it.

Model Router

Our flagship tool. Dynamically route each prompt to hundreds of models to get the best quality and lower cost.

LLM Judge

Unique anotation tools that evaluate the performance of various models on specific tasks and improve with your feedback.

Model Gateway

Access hundreds of LLMs through a simple interface that requires changing just a couple lines of code.

RESEARCH LAB

Advancing AI Safety through Model Mapping Research

Rather than analyzing black-box systems, Martian's approach creates simplified representations that maintain critical information about model performance, accuracy, and ethical behavior. With this mapping techniques, we aim to establish a general theory of intelligence that leads to safer AI systems.

OUR RESEARCH

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Research Highlight: Approximating Human Preferences Using a Multi-Judge Learned System