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.
Each processor varies in performance characteristics and supported features. Use the table below to compare latency, output type, basis features, and more.
ProcessorInput TypeOutput TypeStrengthsLatencyCost ($/1000)Max FieldsBasis Features
liteText, JSONText or JSONBasic metadata, fallback, low latency5s-60s5~2 fieldsCitations, Reasoning
baseText, JSONText or JSONReliable standard enrichments15s-100s10~5 fieldsCitations, Reasoning
coreText, JSONText or JSONCross-referenced, moderately complex outputs60s - 5min25~10 fieldsCitations, Reasoning, Confidence, Excerpts
proText, JSONText or JSONExploratory web research3 min - 9min100~20 fieldsCitations, Reasoning, Confidence, Excerpts
ultraText, JSONText or JSONAdvanced multi-source deep research5 min - 25 min300~20 fieldsCitations, Reasoning, Confidence, Excerpts
ultra2xText, JSONText or JSONDifficult deep research5 min - 25 min600~25 fieldsCitations, Reasoning, Confidence, Excerpts
ultra4xText, JSONText or JSONVery difficult deep research8 min - 30 min1200~25 fieldsCitations, Reasoning, Confidence, Excerpts
ultra8xText, JSONText or JSONThe most difficult deep research8 min - 30 min2400~25 fieldsCitations, Reasoning, Confidence, Excerpts
Cost is measured per 1000 Task Runs in USD. For example, 1 Task Run executed on the lite processor would cost $0.005.

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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.

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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)

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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)
Then use the result as input to 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)
This lets you use a fast processor for initial retrieval, then switch to a more capable one for analysis and context-building.