Parallel
An agent-optimized index where the ranking follows a natural-language objective; results arrive as pre-compressed excerpts.
Where Parallel lands
Composite scores out of 10, from benchmark runs where every provider faces the identical task corpus. Rank is within that capability's field; the newest run backing this page is 2026-06-21. Full method: how we measure.
- Correctness
- 1.00
- Citation faithfulness
- 0.48
- Latency p50
- 16.3 s
- Cost
- $0.01 / call
- Errors
- 0%
- Recall@10
- 0.64
- Latency p50
- 2.7 s
- Cost
- $0.0062 / useful result
- Errors
- 0%
The honest pitch
Pick it for the least in-loop friction: a Search MCP that needs no key, with P50 under a second.
Not the pick if the Search response itself must include full content, relevance scores, or an answer already synthesized.
About Parallel
Parallel treats the AI agent, not the human clicker, as its user. An agent states its goal as a natural-language objective; the API responds with the most relevant sources rendered as token-dense, compressed excerpts, already cut down to context-window size. The payoff is fewer round-trips, better accuracy and reduced token spend. The agent-first slant registers in the numbers — on demanding research benchmarks its accuracy leads the category — and search is one part of a wider suite covering continuous monitoring, extraction and deep research.
Reaching it through NativePort
Identical paths, parameters and responses to Parallel's own documentation — the gateway holds the upstream credential and meters your balance at Parallel's real published usage price.
curl https://api.nativeport.ai/parallel/<native-path> \ -H "Authorization: Bearer $NATIVEPORT_API_KEY"