# Introducing the Core2x processor for improved compute control on the Task API

As AI systems begin to query the web orders of magnitude beyond regular human usage patterns, compute allocation becomes a fundamental infrastructure challenge.

Our belief is that there is dramatic variance in computational requirements across different research tasks. A routine data enrichment query should not consume the same resources as a complex multi-hop investigation requiring extensive validation and synthesis.

Parallel's processor[processor]($https://parallel.ai/pricing) architecture addresses this challenge through granular compute allocation, spanning from Lite processors at 5 CPM to Ultra8x at 2,400 CPM. Today, we're introducing Core2x at 50 CPM—filling a critical gap between Core and Pro processors for moderately complex research tasks that require more depth than basic enrichment but less compute than full exploratory analysis.

## **Why Core2x?**

The jump from Core (25 CPM) to Pro (100 CPM) represented a 4x increase in cost for 4x increase in compute budget. For many production workflows, this previously meant that Core processors occasionally lacked the depth for multi-hop workflows that require complex synthesis, while Pro processors over-allocated compute for tasks that didn't require as much extensive exploration.

Core2x sits precisely in this gap. At 50 CPM with 2-5 minute latency, it delivers 2x the compute of Core while maintaining efficient turnaround times for moderate-complexity research tasks.

## **When to use Core2x**

Core2x is optimized for research tasks that require:

• - Cross-validation across multiple sources without deep research level exploration
• - Moderately complex synthesis where Core might fall short
• - Structured outputs with 10 fields requiring verification
• - Production workflows where Pro's compute budget exceeds requirements

Consider Core2x when your task demands more rigor than standard enrichment but doesn't require the multi-source deep research capabilities of Pro or Ultra+ processors.

Get started in our Developer Platform[Developer Platform]($https://platform.parallel.ai/) or dive into the documentation[documentation]($https://docs.parallel.ai/task-api/guides/choose-a-processor).