Paritok vs Semantica: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Paritok and Semantica — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Paritok

Freemium

Non-destructive compression gateway that drops between coding agents and LLMs to cut input tokens by up to 85% without changing the agent.

Drop-In Gateway: One environment variable (ANTHROPIC_BASE_URL) reroutes your agent through Paritok — no agent, prompt, or tool changes.
Tool Schema Compression: 46-schema tool blocks (~29K tokens) drop to ~8K per turn by keeping relevant tools and stubbing the rest, frozen per conversation for cache stability.
Code-Native 4B Model: A 4B compression model trained on 45K real agent trajectories keeps identifiers, paths, and errors while shrinking file reads and outputs to ~26% of original.
read_original Recall: Every compressed segment is tagged; the agent asks read_original(ref) and gets the exact bytes locally without spending an extra turn.
Stale History Summarization: Turns beyond a configurable recent window get summarized once when your context budget fills, so recent turns stay pristine and overflows drop to zero.
Multi-Agent Compatibility: Works today with Claude Code, Cursor, Codex, OpenHands, and any OpenAI-compatible upstream — Anthropic and OpenAI both supported.
Compounding Savings: Saved share grows across a session — 25% at 1 turn, 54% at 10 turns, 63% at 20 turns — against a 96,500-token baseline.
Open Weights and Benchmark: SWE-bench Lite floor of 86.5% quality retained at 25.7% compression rate, with weights and training pipeline published.

MCP-Heavy Workflows: Cut input bills for agents that ship 70+ MCP tool schemas on every turn.
Long Coding Sessions: Run 3× longer coding-agent sessions before context saturation forces a hard compact.
Bill Reduction: Estimate 54% off input tokens on a 5-developer team at 20-turn Claude Sonnet sessions (~$6,550/year).
Self-Hosted Privacy: Route agent traffic through your own hardware with no data leaving your network — 8GB GPU is enough.
Enterprise Cost Governance: Add compression at the gateway layer so all coding agents on the team benefit without engineering per-agent.
Cursor/Codex/Claude Code Fleet: Standardize compression across a mixed toolchain of coding agents behind one gateway.

semantica-agi

Free

Open-source, graph-native context and knowledge-graph infrastructure for accountable AI agents in regulated domains.

Graph-Native Ingestion: Ingest enterprise data and extract entities, relationships, and provenance into a Context Graph plus a formal Knowledge Graph.
Polyglot Graph Storage: Native support for both RDF and Labeled Property Graphs so you can pick the model that fits each domain.
W3C Standards & Interoperability: SPARQL, OWL, and related standards keep the graph portable across tooling with zero vendor lock-in.
Deterministic Reasoning: Rule-based and causal reasoning over the graph so agent decisions are reproducible, not black-box.
Decision Provenance: Every decision is traceable back to the ingested evidence and the reasoning steps that produced it.
Ontology & Knowledge Modeling: First-class tools for defining, evolving, and enforcing the domain ontology that governs agent context.
Self-Hostable: Deploy the whole stack inside your own infrastructure — the code is MIT-licensed and open.
Regulated-Domain Ready: Built for high-stakes, governed use cases where auditability and end-to-end traceability are mandatory.

Auditable Agent Decisions: Build agents in finance, healthcare, or public sector where every decision must be traceable to source data.
Enterprise Context Management: Turn scattered enterprise data into a queryable Context Graph the agent uses as its long-term memory.
Causal Analysis: Run causal reasoning over the graph to explain outcomes, not just correlations.
Ontology-Driven Extraction: Enforce a domain ontology so extracted entities and relationships remain consistent across sources.
Regulated Deployment: Self-host in a compliance-bounded environment with zero third-party data egress.
Knowledge Graph Bootstrapping: Ingest documents, databases, and events into a formal KG that agents and BI tools share.