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Once you have a trained model — or want to use a base model directly — you run inference by sending a request to the Pioneer API. The model_id field accepts either a base model ID (like fastino/gliner2-base-v1) or the job ID returned from a completed training job (like job_abc123). Pioneer routes the request to the right deployment automatically. Pioneer supports three request formats: its own native format, an OpenAI-compatible format, and an Anthropic-compatible format. All three reach the same underlying models.

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Pioneer native format

Use POST /inference with the Pioneer schema format. This is the most expressive option and gives you full control over extraction tasks. 
curl -X POST https://api.pioneer.ai/inference \
  -H "X-API-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model_id": "job_abc123",
    "text": "Apple announced the MacBook Pro at WWDC in Cupertino.",
    "schema": {
      "entities": ["organization", "product", "event", "location"]
    },
    "threshold": 0.5
  }'

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Schema structure

The schema field is a dictionary with optional keys. Include only the keys that apply to your task.
KeyTypeDescription
entitiesstring[]Entity type labels for named entity recognition (NER).
classificationsobject[]Classification tasks, each with a task name and labels list.
structuresobjectNamed structure definitions for JSON extraction.
relationsobject[]Relation definitions linking extracted entities.

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Decoder models

For decoder models (LLMs), replace schema with "task": "generate": 
curl -X POST https://api.pioneer.ai/inference \
  -H "X-API-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model_id": "Qwen/Qwen3-8B",
    "task": "generate",
    "messages": [                                                                                                     
        {"role": "user", "content": "Summarize the following article in two sentences."}
      ]
  }'

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OpenAI-compatible format

Pioneer exposes an OpenAI-compatible endpoint at https://api.pioneer.ai/v1. Point any existing OpenAI SDK or integration at this base URL and use your Pioneer API key — no other changes required. 
curl -X POST https://api.pioneer.ai/v1/chat/completions \
  -H "X-API-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "job_abc123",
    "messages": [
      {"role": "user", "content": "Extract entities from: Apple launched the iPhone."}
    ],
    "schema": {"entities": ["organization", "product"]}
  }'
Available OpenAI-compatible endpoints:
MethodEndpointDescription
POST/v1/chat/completionsChat completions
POST/v1/completionsText completions
POST/v1/responsesResponses API
GET/v1/modelsList available models
When using the OpenAI Python or Node SDK, pass Pioneer-specific fields like schema via the extra_body parameter. For example:
client.chat.completions.create(
    model="job_abc123",
    messages=[{"role": "user", "content": "Extract entities from: Apple launched the iPhone."}],
    extra_body={"schema": {"entities": ["organization", "product"]}}
)

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Anthropic-compatible format

Pioneer also exposes an Anthropic-compatible endpoint. Set your SDK’s base_url to https://api.pioneer.ai/v1 and use your Pioneer API key in place of an Anthropic key. 
curl -X POST https://api.pioneer.ai/v1/messages \
  -H "X-API-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "job_abc123",
    "max_tokens": 1024,
    "messages": [
      {"role": "user", "content": "Extract entities from: Apple launched the iPhone."}
    ],
    "schema": {"entities": ["organization", "product"]}
  }'
Both the OpenAI-compatible and Anthropic-compatible endpoints support streaming.

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Inference history

Pioneer records every inference call. You can retrieve past results and submit corrections to improve future training data. 
# List recent inferences
curl https://api.pioneer.ai/inferences \
  -H "X-API-Key: YOUR_API_KEY"

# Get a specific inference result
curl https://api.pioneer.ai/inferences/INFERENCE_ID \
  -H "X-API-Key: YOUR_API_KEY"

# Mark as correct
curl -X POST https://api.pioneer.ai/inferences/INFERENCE_ID/feedback \
  -H "X-API-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"verdict": "correct"}'

# Submit a correction
curl -X POST https://api.pioneer.ai/inferences/INFERENCE_ID/feedback \
  -H "X-API-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"verdict": "incorrect", "corrected_output": {...}}'
Optional query filters for GET /inferences: limit, offset, model_id, task, project_id, training_job_id.