Pioneer model catalog: encoders, decoders, and inference

Browse Pioneer’s encoder (GLiNER) and decoder (LLM) models for fine-tuning and inference. Covers on-demand vs. serverless and how to query the live catalog.

Pioneer supports two model families: encoder models (GLiNER) for structured extraction tasks like named entity recognition, and decoder models (LLMs) for text generation, classification, and open-ended prompting. The tables below are a snapshot of the current catalog — use GET /base-models to query the live list, which always reflects current availability and capabilities.

Some rollout-stage models are feature-gated. They appear in the live catalog only for workspaces that have the corresponding rollout enabled.

Encoder models (GLiNER)

GLiNER models perform named entity recognition and structured extraction. Most GLiNER base models support both training and on-demand inference after training. Prices are per 1M tokens.

Model ID	Label	Input	Output	Training	Inference
fastino/gliner2-base-v1	GLiNER2 Base	$0.15	$0.15	LoRA, Full	Serverless, On-demand
fastino/gliner2-large-v1	GLiNER2 Large	$0.15	$0.15	LoRA, Full	Serverless, On-demand
fastino/gliner2-multi-v1	GLiNER2 Multi	$0.15	$0.15	LoRA, Full	Serverless, On-demand
fastino/gliner2-multi-large-v1	GLiNER2 Multi Large	$0.15	$0.15	LoRA, Full	Serverless, On-demand
fastino/gliguard-LLMGuardrails-300M	GLiGuard LLM Guardrails 300M	$0.15	$0.15	—	Serverless
fastino/gliner2-privacy-filter-PII-multi	GLiNER2 Privacy Filter PII (Multi)	$0.15	$0.15	—	Serverless
fastino/gliguard-PII-multi	GLiNER2-Guardrails-PII-Multi	$0.15	$0.15	—	Serverless

fastino/gliner2-multi-v1 and fastino/gliner2-multi-large-v1 are multilingual variants suitable for non-English text.

Decoder models — training

LoRA fine-tuning via POST /felix/training-jobs is limited to the Nemotron 3.5 Lightning family:

Model ID	Label	Training
nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16	Nemotron 3.5 Lightning 30B-A3B	LoRA
fastino/Fastino-Nemotron-3.5-Lightning-Finance	Fastino Nemotron 3.5 Lightning Finance	LoRA
fastino/Fastino-Nemotron-3.5-Lightning-Healthcare	Fastino Nemotron 3.5 Lightning Healthcare	LoRA

Together with the trainable GLiNER2 Base, Large, Multi, and Multi Large encoder targets above, these are the only supported base models for new training jobs. Query GET /base-models?supports_training=true before submitting a job; it is the live source of truth for availability.

Decoder models — serverless inference

These are the supported pre-deployed inference families. Rates are USD per 1M tokens. Query GET /base-models?supports_inference=true for live availability, context limits, and pricing — it is the source of truth if it ever disagrees with this page.

Cached input is what a prompt-cache hit costs (see prompt caching). Where it equals the input rate, that route does not price cache reads separately. A dash means the rate is not published for that model — rollout-gated models return their rates only to workspaces with the rollout enabled.

Nemotron 3.5 Lightning

Model ID	Label	Input	Output	Cached input	Context
nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16	Nemotron 3.5 Lightning 30B-A3B	$0.50	$0.50	$0.50	8,192
fastino/Fastino-Nemotron-3.5-Lightning-Finance	Fastino Nemotron 3.5 Lightning Finance	$0.50	$0.50	$0.50	8,192
fastino/Fastino-Nemotron-3.5-Lightning-Healthcare	Fastino Nemotron 3.5 Lightning Healthcare	$0.50	$0.50	$0.50	8,192

Anthropic

Model ID	Label	Input	Output	Cached input	Context
claude-opus-5	Claude Opus 5	$5.00	$25.00	$0.50	1,000,000
claude-sonnet-5	Claude Sonnet 5	$2.00	$10.00	$0.20	1,000,000
claude-opus-5-fast	Claude Opus 5 (Fast)	$10.00	$50.00	$1.00	1,000,000
claude-fable-5	Claude Fable 5	$11.00	$55.00	$1.10	1,000,000
claude-haiku-4-5	Claude Haiku 4.5	$1.00	$5.00	$0.10	200,000

OpenAI

Model ID	Label	Input	Output	Cached input	Context
gpt-5.5	GPT-5.5	$5.00	$30.00	$0.50	1,000,000
gpt-5.6-luna	GPT-5.6 Luna	$1.00	$6.00	$0.10	1,050,000
gpt-5.6-terra	GPT-5.6 Terra	$2.50	$15.00	$0.25	1,050,000
gpt-5.6-sol	GPT-5.6 Sol	$5.00	$30.00	$0.50	1,050,000

GPT-5 models bill a long-context tier. Requests whose prompt exceeds 272,000 tokens are charged at roughly double the rates above — GPT-5.5 and GPT-5.6 Sol go to $10.00 / $45.00 / $1.00, GPT-5.6 Terra to $5.00 / $22.50 / $0.50, and GPT-5.6 Luna to $2.00 / $9.00 / $0.20. The rest of this page’s rates are the standard tier.

DeepSeek

Model ID	Label	Input	Output	Cached input	Context
deepseek-ai/DeepSeek-V4-Flash	DeepSeek V4 Flash	$0.14	$0.28	$0.028	1,000,000

The dated pin deepseek-ai/DeepSeek-V4-Flash-0731 remains a working alias for this entry — both resolve to the same 0731 checkpoint upstream.

Z.ai

Model ID	Label	Input	Output	Cached input	Context
zai-org/GLM-5.2	GLM 5.2	$1.40	$4.40	$0.14	1,040,000

Moonshot AI

Model ID	Label	Input	Output	Cached input	Context
moonshotai/Kimi-K3	Kimi K3	$3.00	$15.00	$0.30	1,000,000
moonshotai/Kimi-K3-Fast	Kimi K3 Fast	$4.50	$22.50	$0.45	1,000,000

Models outside these families are not supported inference targets. Use GET /base-models for the live catalog and lifecycle state before integrating.

Prompt caching

Many serverless models bill cached input tokens at a discount, and some providers bill a one-time surcharge to write tokens into the cache. Pioneer passes each provider’s published cache rates straight through — these are the same rates GET /base-models returns as cache_read_price_per_million and cache_write_price_per_million, and the same rates Pioneer bills you.

Cache rates are derived from each model’s input rate using the multipliers below. Where a provider has no separate cache-write line item, cache writes bill at the standard input rate.

Provider	Cache read	Cache write
Anthropic (Claude)	0.1× input	1.25× input
OpenAI (GPT-5 family)	0.1× input	input rate (1.25× on GPT-5.6 Luna/Sol/Terra)

Query the live catalog for exact cache rates. Any model without an explicit cache discount bills cached input at the standard input rate.

On-demand vs. serverless inference

Pioneer offers two ways to serve predictions, and the right choice depends on your workflow.

Serverless inference uses pre-deployed base model endpoints. There is no startup delay and you are billed per token. This is ideal when you want to call a frontier model without fine-tuning.

On-demand inference provisions a dedicated GPU after fine-tuning completes. Your LoRA adapter is loaded onto the GPU and served exclusively for your requests. Pioneer routes inference calls to an on-demand deployment automatically when you pass a training job ID as model_id.

Querying the live catalog

The tables above may lag behind newly added models. Use GET /base-models to get the current catalog at runtime.

# All models
curl https://api.pioneer.ai/base-models \
  -H "X-API-Key: YOUR_API_KEY"

# Only models that support inference
curl "https://api.pioneer.ai/base-models?supports_inference=true" \
  -H "X-API-Key: YOUR_API_KEY"

# Only models that support training
curl "https://api.pioneer.ai/base-models?supports_training=true" \
  -H "X-API-Key: YOUR_API_KEY"

# Filter by model family
curl "https://api.pioneer.ai/base-models?task_type=encoder" \
  -H "X-API-Key: YOUR_API_KEY"

curl "https://api.pioneer.ai/base-models?task_type=decoder" \
  -H "X-API-Key: YOUR_API_KEY"

Each entry in the response includes the model ID, its display label, context length, per-1M-token rates (input_price_per_million, output_price_per_million, cache_read_price_per_million, cache_write_price_per_million), and boolean flags for supports_training and supports_inference. Use the model ID value directly in training job requests and inference calls.

To list every model alongside its input, output, and cache rates:

curl -s "https://api.pioneer.ai/base-models?supports_inference=true" \
  -H "X-API-Key: YOUR_API_KEY" \
| jq -r '["model","input/M","output/M","cache_read/M","cache_write/M"],
    (.models[] | [.id, .input_price_per_million, .output_price_per_million,
      .cache_read_price_per_million, .cache_write_price_per_million]) | @tsv' \
| column -t -s $'\t'