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GET
https://api.sutro.sh
/
jobs
/
{job_id}
/
results
Retrieving Results
curl --request GET \
  --url https://api.sutro.sh/jobs/{job_id}/results \
  --header 'Authorization: <authorization>'
{
    "outputs": [
        "The capital of France is Paris.",
        "Quantum computing uses quantum mechanics principles to process information in ways that classical computers cannot..."
    ],
    "inputs": [
        "What is the capital of France?",
        "Explain quantum computing in simple terms"
    ],
    "cumulative_logprobs": [-0.5234, -1.2456]
}
Using the API directly is not recommended for most users. Instead, we recommend using the Python SDK.
Download the complete results of a batch inference job. Results can be downloaded in multiple formats optimized for different use cases.

​
Path Parameters

​
job_id
string
required
The job_id returned when you submitted the batch inference job

​
Query Parameters

​
result_format
enum
required
The format to download results in:
  • csv - CSV file (zipped for compression)
  • parquet - Parquet file
  • json - JSON object
​
include_inputs
boolean
default:"false"
Whether to include the input prompts in the results
​
include_cumulative_logprobs
boolean
default:"false"
Whether to include the cumulative log probabilities in the results

​
Headers

​
Authorization
string
required
Your Sutro API key using Key authentication scheme.Format: Key YOUR_API_KEYExample: Authorization: Key sk_abc123...

​
Response

Returns a downloadable file in the requested format.

​
Parquet

  • Returns a single Parquet file
  • Recommended for large datasets

​
CSV

  • Returns a ZIP file containing a CSV
  • File is compressed for efficient transfer
  • Column names: inputs, {job_id} outputs, cumulative_logprobs (if requested)

​
JSON

  • Returns a JSON object
  • Best for smaller datasets

​
Structured Outputs

When using structured outputs (by providing a json_schema when creating the job), the outputs will be JSON strings that conform to your specified schema.

​
Standard Models

For non-reasoning models, the output will be a JSON string following your schema: 
{
  "outputs": [
    "{\"name\": \"John Doe\", \"age\": 30, \"email\": \"[email protected]\"}",
    "{\"name\": \"Jane Smith\", \"age\": 25, \"email\": \"[email protected]\"}"
  ]
}

​
Reasoning Models

For reasoning models (like o1), the output includes both the structured content and the reasoning process: 
{
  "outputs": [
    "{\"content\": {\"name\": \"John Doe\", \"age\": 30}, \"reasoning_content\": \"First, I identified the name from the text...\"}",
    "{\"content\": {\"name\": \"Jane Smith\", \"age\": 25}, \"reasoning_content\": \"I analyzed the passage and extracted...\"}"
  ]
}
The output structure for reasoning models:
  • content: The structured output following your JSON schema (can be a text string or JSON string containing an object matching your schema)
  • reasoning_content: The model’s step-by-step reasoning process (string)
Currently, when using structured outputs or reasoning models, one will need to run json.loads or similar on each output JSON, ie json.loads(outputs[0]) to transform from a string to a dict (or equivalent in other languages). 
{
    "outputs": [
        "The capital of France is Paris.",
        "Quantum computing uses quantum mechanics principles to process information in ways that classical computers cannot..."
    ],
    "inputs": [
        "What is the capital of France?",
        "Explain quantum computing in simple terms"
    ],
    "cumulative_logprobs": [-0.5234, -1.2456]
}

​
Code Examples

import requests

response = requests.get(
    'https://api.sutro.sh/jobs/job_12345/results',
    headers={
        'Authorization': 'Key YOUR_SUTRO_API_KEY'
    },
    params={
        'result_format': 'csv',
        'include_inputs': True,
        'include_cumulative_logprobs': False
    }
)

# Save the zip file
with open('results.zip', 'wb') as f:
    f.write(response.content)

# Extract and read with pandas
import zipfile
import pandas as pd

with zipfile.ZipFile('results.zip') as z:
    csv_filename = z.namelist()[0]
    with z.open(csv_filename) as csv_file:
    df = pd.read_csv(csv_file)
    print(df.head())

​
Notes

  • Results can only be retrieved for jobs that have completed successfully
  • The order of results matches the order of the original inputs
  • CSV format: outputs are in a column named after the job_id
  • Parquet format uses zstd compression internally
  • For very large datasets (>100MB), Parquet is recommended
  • CSV files are automatically zipped to reduce download size