SuperCompress
Query-aware prompt compression that cuts LLM input tokens by roughly 60% before inference.

Model router that sends each query to the best LLM in real time
Martian's differentiator is its 'model mapping' interpretability angle, which it uses to justify claims of beating a single frontier model via routing. That is an interesting technical bet, but the headline claims are vendor-stated and depend on your specific workloads, so treat them as hypotheses to test rather than guarantees. The free tier and trial make evaluation low-risk. Backing from NEA, General Catalyst, and Accenture Ventures lends credibility, but the routing category is competitive and pricing details beyond the free tier are largely sales-led.
Martian is an LLM model router that dynamically sends each query to the best-suited model based on quality, cost, and uptime, using an interpretability-based 'model mapping' technique. It aims to reduce costs and improve reliability compared with committing to a single provider, and it markets a free version plus a trial with advanced usage handled via sales. Backed by NEA, Prosus Ventures, General Catalyst, and Accenture Ventures, Martian targets engineering teams and enterprises building on multiple LLMs.
Martian builds a real-time model router that takes an incoming prompt and directs it to the most suitable LLM based on factors like skillset, cost-to-performance ratio, and uptime. Rather than treating models as black boxes, the company emphasizes a 'model mapping' interpretability approach intended to predict how different models will perform on a given request, which it has publicly claimed can outperform always using a top-tier model. The value proposition is familiar in the routing category: dynamically switching between LLMs can reduce costs and improve reliability while maintaining or improving output quality, and it insulates applications from any single provider's outages or price changes. Martian markets itself to engineering teams at a wide range of companies and has drawn strategic interest from Accenture Ventures, reflecting an enterprise-services angle. Martian offers a free version and a free trial, with more advanced usage handled through sales. As with all routers, real-world benefit depends on your workloads and the model set, and the interpretability claims are the company's own, so teams should benchmark on their own tasks. Martian raised a $9 million seed round from investors including NEA, Prosus Ventures, Carya Venture Partners, and General Catalyst.
Martian is an LLM model router that dynamically routes each query to the best model by quality, cost, and uptime, using an interpretability-based 'model mapping' technique. It offers a free version and trial, with advanced usage handled via sales. Martian raised a $9M seed round from NEA, Prosus Ventures, Carya Venture Partners, and General Catalyst, and received investment from Accenture Ventures. It targets engineering teams and enterprises building on multiple LLMs. Routing benefits are workload-dependent and worth benchmarking.
Martian (operating at withmartian.com) develops a model router aimed at optimizing LLM usage across providers. It publicly emphasizes an interpretability-driven 'model mapping' method as the basis for its routing decisions and has positioned itself for both developer and enterprise adoption.
The company reports usage across a wide range of companies and has attracted strategic investment from Accenture Ventures, reflecting an enterprise and professional-services orientation.
Martian's core product is a real-time router that selects the best model for each prompt based on skillset, cost-to-performance, and uptime. It supports routing across major commercial and open-source models via API.
The 'model mapping' approach is intended to predict model behavior more precisely than treating models as black boxes, supporting claims of improved cost-quality tradeoffs and reliability through provider failover.
Engineering teams and enterprises that use multiple LLMs and want automated orchestration to lower cost, improve reliability, and optimize quality per query.
Developers who route LLM calls and want automatic best-model selection.
Engineering and platform leaders seeking to reduce model spend and improve reliability.
AI infrastructure architects and enterprise consulting partners (e.g., systems integrators).
Mid-market and enterprise teams running multiple LLM providers that want an orchestration layer to optimize cost, quality, and uptime.
Martian raised a $9 million seed round from investors including NEA, Prosus Ventures, Carya Venture Partners, and General Catalyst. It also received an investment from Accenture Ventures (announced September 2024). Any later rounds should be verified directly with the company.
Its router evaluates each prompt against factors like model skillset, cost-to-performance, and uptime, using a 'model mapping' interpretability technique to predict which model will perform best, then routes the request accordingly.
Martian offers a free version and a free trial with a $0/month starting point. Higher-volume and enterprise usage is arranged through sales.
The company has publicly claimed its router can outperform relying on one top model, but these are vendor benchmarks; results depend on your workloads, so validate on your own tasks.
Martian raised a $9 million seed round from investors including NEA, Prosus Ventures, Carya Venture Partners, and General Catalyst, and received investment from Accenture Ventures.
Yes. By routing across multiple providers, Martian can fail over when a provider is down or degraded, improving overall application reliability.
Side-by-side pages for pricing, features, and best-fit use cases.
Query-aware prompt compression that cuts LLM input tokens by roughly 60% before inference.
One API for hundreds of AI models across providers.
GitHub Copilot is an AI-powered coding assistant that works across multiple environments including IDEs, terminals, and GitHub itself
Replit AI is an AI-powered coding platform that turns natural language into apps and websites, integrated into the Replit development environment