Processors are the engines that execute Task Runs. The choice of Processor determines the performance profile and reasoning behavior used. Pricing is determined by which Processor you select, not by the Task Run itself. Any Task Run can be executed on any Processor.
Choose a processor based on the complexity of your task. Use lite or base for simple enrichments, core for reliable accuracy on up to 10 output fields, and pro or ultra when reasoning depth is critical.
| Processor | Latency | Cost ($/1000) | Strengths | Max Fields |
|---|
lite | 5s-60s | 5 | Basic metadata, fallback, low latency | ~2 fields |
base | 15s-100s | 10 | Reliable standard enrichments | ~5 fields |
core | 60s - 5min | 25 | Cross-referenced, moderately complex outputs | ~10 fields |
core2x | 2min - 5min | 50 | High complexity cross referenced outputs | ~10 fields |
pro | 3 min - 9min | 100 | Exploratory web research | ~20 fields |
ultra | 5 min - 25 min | 300 | Advanced multi-source deep research | ~20 fields |
ultra2x | 5 min - 25 min | 600 | Difficult deep research | ~25 fields |
ultra4x | 8 min - 30 min | 1200 | Very difficult deep research | ~25 fields |
ultra8x | 8 min - 30 min | 2400 | The most difficult deep research | ~25 fields |
Cost is measured per 1000 Task Runs in USD. For example, 1 Task Run executed on the lite processor would cost $0.005.
Examples
Processors can be used flexibly depending on the scope and structure of your task. The examples below show how to:
- Use a single processor (like
lite, base, core, pro, or ultra) to handle specific types of input and reasoning depth.
- Chain processors together to combine fast lookups with deeper synthesis.
This structure enables flexibility across a variety of tasks—whether you’re extracting metadata, enriching structured records, or generating analytical reports.
Sample Task for each Processor
task_run = client.task_run.create(
input="Parallel Web Systems (parallel.ai)",
task_spec={"output_schema":"The founding date of the company in the format MM-YYYY"},
processor="lite"
)
print(f"Run ID: {task_run.run_id}")
run_result = client.task_run.result(task_run.run_id, api_timeout=3600)
print(run_result.output)
Multi-Processor Workflows
You can combine processors in sequence to support more advanced workflows.
Start by retrieving basic information with base:
task_run_base = client.task_run.create(
input="Pfizer",
task_spec={"output_schema":"Who are the current executive leaders at Pfizer? Include their full name and title. Ensure that you retrieve this information from a reliable source, such as major news outlets or the company website."},
processor="base"
)
print(f"Run ID: {task_run_base.run_id}")
base_result = client.task_run.result(task_run_base.run_id, api_timeout=3600)
print(base_result.output)
core to generate detailed background information:
import json
task_run = client.task_run.create(
input=json.dumps(base_result.output.content),
task_spec={"output_schema":"For the executive provided, find their professional background tenure at their current company, and notable strategic responsibilities."},
processor="pro"
)
print(f"Run ID: {task_run.run_id}")
run_result = client.task_run.result(task_run.run_id, api_timeout=3600)
print(run_result.output)
