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Documentation Index

Fetch the complete documentation index at: https://docs.pioneer.ai/llms.txt

Use this file to discover all available pages before exploring further.

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title: “Pioneer inference: native, OpenAI, and Anthropic formats” sidebarTitle: “Inference” description: “Run predictions on any base or fine-tuned model using Pioneer’s native API, the OpenAI-compatible endpoint, or the Anthropic-compatible messages endpoint.”

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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Opting out of inference persistence

By default, Pioneer stores every inference — the input, output, and metadata — so it can drive evaluation, use-case clustering, and adapter training. Pass store: false to skip persistence for a specific request. 
curl -X POST https://api.pioneer.ai/v1/chat/completions \
  -H "Authorization: Bearer pio_sk_..." \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-opus-4-7",
    "messages": [
      {"role": "user", "content": "Hello, world!"}
    ],
    "store": false
  }'
store: false is supported on all three request formats — native /inference, /v1/chat/completions, and /v1/messages — and works identically for streaming and non-streaming requests.

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What changes with store: false

Default (store: true)store: false
Inference executesYesYes
Input/output storedYesNo
Evaluation runYesNo
Use-case clusteringYesNo
Adapter training feedYesNo
Token billingYesYes
inference_id in responseYesYes (for correlation)
Billing still applies. Token usage, COGS, and metered billing are recorded even when store: false is set — only the full request/response payload is not retained.

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When to use it

  • Health checks — liveness and readiness probes that run continuously - Internal benchmarks — evaluations you run against your own ground truth that shouldn’t pollute user-facing inference history - Development and testing — exploratory calls during integration work where accumulating inference rows adds noise

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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.