Reduce the tokens your models consume without compromising the quality of their output. Lower costs, faster responses, more usable context.
Context compression is the process of reducing the number of tokens required to produce a given model output, without compromising the quality of that output.
A long input goes in. A shorter, equivalent input comes out. The model receives less to process, costs less to run, and responds faster. The downstream task performs the same.
Hybrid approaches combining multiple compression techniques tend to produce the strongest results in practice.
Compression families
Extractive
Identifies and keeps the highest-signal tokens, removing the rest while preserving the original wording.
Abstractive
Rewrites the input as a shorter summary that conveys the same operational meaning.
Learned encoders
Transforms the input into compact representations the model can interpret directly.
Compression is one of the highest-leverage optimizations available to any team running large language models in production.
Input tokens dominate cost
Input tokens, not output tokens, drive the overwhelming majority of total spend in agentic workloads - even with caching enabled. Reducing input scales directly into savings.
Long contexts degrade output
Models pay less attention to information in the middle of long contexts. Accuracy often peaks at intermediate token counts and degrades as context grows. Compression is frequently sharper, not just cheaper.
Latency scales with input
Every additional thousand tokens extends model response time. For interactive applications and real-time agents, compression translates directly into a faster product.
Demand is accelerating
Programming alone grew from 11% to over 50% of all LLM token usage on OpenRouter between early and late 2025. Agentic workflows, RAG pipelines, and document analysis are driving sustained demand.
SOMARIZER delivers context compression as a continuously improving service. The architecture is designed to surface the strongest available implementation at any moment, automatically.
Static methods age out
A compression method tuned for one task family fails on another. The moment underlying models change, static approaches become liabilities.
Continuous improvement
SOMARIZER treats compression as an open and continuous problem. Users always receive the current best implementation with no engineering work to upgrade or swap providers.
Submit
The platform receives the input context along with the downstream task it needs to support.
Compete
Independent providers run their own compression implementations against the input - extractive, abstractive, learned encoders, or hybrids of all three.
Score and return
An evaluation layer measures every output against two criteria: compression ratio and fidelity to the original task result. The strongest output is returned. The strongest providers earn a larger share of the network.
SOMA is a marketplace for MCP (Model Context Protocol) services, built for AI agents that need to integrate, coordinate, and execute reliably at scale. The first service live on SOMA is Somarizer, with more MCP services in the pipeline.
MCP stands for Model Context Protocol, an open standard for connecting AI models with external tools, data sources, and execution environments. It gives AI agents a consistent way to exchange context and take action across different systems. SOMA delivers MCP services as a continuously optimized layer, so teams plug in performance instead of building it from scratch.
Context Compression is the process of reducing the number of tokens an AI model needs to produce a given output, while preserving the quality of that output. Fewer tokens translate directly into lower costs, faster responses, and more usable space inside any context window. For production AI workloads, the savings compound with every request.
Right now, SOMA has one live tool: SOMARIZER - a context compression service for AI agents and LLM applications. It plugs into existing pipelines and shrinks input tokens without compromising downstream task performance. More tools are in the works and coming soon.