Pioneer model catalog: encoders, decoders, and inference

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

These LLMs are available for LoRA fine-tuning via POST /felix/training-jobs. When you submit a job, Pioneer automatically routes it to the best available provider.

Model ID	Label	Input	Output	Context
`Qwen/Qwen2.5-Coder-0.5B`	Qwen2.5 Coder 0.5B	$0.10	$0.10	32,768
`Qwen/Qwen3-1.7B-Base`	Qwen3 1.7B Base	$0.10	$0.10	32,768
`Qwen/Qwen3-32B`	Qwen3 32B	$0.90	$0.90	32,768
`Qwen/Qwen3-4B-Base`	Qwen3 4B Base	$0.15	$0.15	32,768
`Qwen/Qwen3-4B-Instruct-2507`	Qwen3 4B Instruct	$0.20	$0.20	32,768
`Qwen/Qwen3-8B`	Qwen3 8B	$0.20	$0.20	40,960
`Qwen/Qwen3.5-9B`	Qwen3.5 9B	$0.30	$0.30	32,768
`Qwen/Qwen3.6-27B`	Qwen3.6 27B	$0.60	$0.60	32,768
`google/gemma-3-4b-pt`	Gemma 3 4B (Pretrained)	$0.15	$0.15	32,768
`google/gemma-4-12B-it`	Gemma 4 12B IT	$0.25	$0.25	32,768
`google/gemma-4-31B-it`	Gemma 4 31B IT	$0.50	$0.50	32,768
`google/gemma-4-E2B-it`	Gemma 4 E2B IT	$0.10	$0.10	32,768
`google/gemma-4-E4B-it`	Gemma 4 E4B IT	$0.20	$0.20	32,768
`meta-llama/Llama-3.1-8B-Instruct`	Llama 3.1 8B Instruct	$0.20	$0.20	16,384
`meta-llama/Llama-3.2-1B-Instruct`	Llama 3.2 1B Instruct	$0.10	$0.201	60,000
`mistralai/Mistral-7B-Instruct-v0.3`	Mistral 7B Instruct v0.3	$0.20	$0.20	32,768
`HuggingFaceTB/SmolLM3-3B-Base`	SmolLM3 3B Base	$0.15	$0.15	32,768

Decoder models — serverless inference

These decoder models are pre-deployed and available for inference immediately. Trainable decoder rows that also support serverless inference are listed in the training table above and are not repeated here.

Anthropic

Model ID	Label	Input	Output	Context
`claude-fable-5`	Claude Fable 5	$10.00	$50.00	1,000,000
`claude-haiku-4-5`	Claude Haiku 4.5	$1.00	$5.00	200,000
`claude-opus-4-1`	Claude Opus 4.1	$15.00	$75.00	200,000
`claude-opus-4-5`	Claude Opus 4.5	$5.00	$25.00	200,000
`claude-opus-4-6`	Claude Opus 4.6	$5.00	$25.00	1,000,000
`claude-opus-4-7`	Claude Opus 4.7	$5.00	$25.00	1,000,000
`claude-opus-4-8`	Claude Opus 4.8	$5.00	$25.00	1,000,000
`claude-sonnet-4-5`	Claude Sonnet 4.5	$3.00	$15.00	200,000
`claude-sonnet-4-6`	Claude Sonnet 4.6	$3.00	$15.00	1,000,000
`claude-sonnet-5`	Claude Sonnet 5	$2.00	$10.00	1,000,000

OpenAI

Model ID	Label	Input	Output	Context
`gpt-4o`	GPT-4o	$2.50	$10.00	128,000
`gpt-4o-mini`	GPT-4o mini	$0.15	$0.60	128,000
`gpt-4.1`	GPT-4.1	$2.00	$8.00	1,047,576
`gpt-4.1-mini`	GPT-4.1 mini	$0.40	$1.60	1,047,576
`gpt-4.1-nano`	GPT-4.1 nano	$0.10	$0.40	1,047,576
`gpt-5-mini`	GPT-5 mini	$0.25	$2.00	400,000
`gpt-5-nano`	GPT-5 nano	$0.05	$0.40	400,000
`gpt-5.1`	GPT-5.1	$1.25	$10.00	400,000
`gpt-5.3-codex`	GPT-5.3 Codex	$1.75	$14.00	400,000
`gpt-5.4`	GPT-5.4	$2.50	$15.00	1,047,576
`gpt-5.4-mini`	GPT-5.4 mini	$0.75	$4.50	400,000
`gpt-5.4-nano`	GPT-5.4 nano	$0.20	$1.25	1,047,576
`gpt-5.5`	GPT-5.5	$5.00	$30.00	1,000,000
`gpt-5.6-luna`	GPT-5.6 Luna	$1.00	$6.00	1,050,000
`gpt-5.6-terra`	GPT-5.6 Terra	$2.50	$15.00	1,050,000
`gpt-5.6-sol`	GPT-5.6 Sol	$5.00	$30.00	1,050,000
`openai/gpt-oss-20b`	GPT-OSS 20B	$0.07	$0.30	131,072
`openai/gpt-oss-120b`	GPT-OSS 120B	$0.15	$0.60	131,072

Google

Model ID	Label	Input	Output	Context
`google/diffusiongemma-26B-A4B-it`	DiffusionGemma 26B-A4B IT	$0.50	$0.50	262,144
`gemini-3.1-pro`	Gemini 3.1 Pro	$2.00	$12.00	1,048,576
`gemini-3.1-flash-lite`	Gemini 3.1 Flash Lite	$0.25	$1.50	1,000,000
`gemini-3.5-flash`	Gemini 3.5 Flash	$1.50	$9.00	1,048,576
`gemini-3.5-flash-lite`	Gemini 3.5 Flash Lite	$0.30	$2.50	1,000,000
`gemini-3.6-flash`	Gemini 3.6 Flash	$1.50	$7.50	1,000,000
`gemini-3-flash`	Gemini 3 Flash	$0.50	$3.00	1,048,576

Mistral

Model ID	Label	Input	Output	Context
`mistral-medium-3.5`	Mistral Medium 3.5	$1.50	$7.50	262,144
`mistralai/Mistral-Small-4-119B-2603`	Mistral Small 4	$0.15	$0.60	262,144
`mistralai/Mistral-Nemo-Instruct-2407`	Mistral Nemo	$0.02	$0.03	131,072

DeepSeek

Model ID	Label	Input	Output	Context
`deepseek-ai/DeepSeek-V4-Pro`	DeepSeek V4 Pro	$0.435	$0.87	1,000,000
`deepseek-ai/DeepSeek-V4-Flash`	DeepSeek V4 Flash	$0.10	$0.20	1,000,000
`deepseek-ai/DeepSeek-V3`	DeepSeek V3 0324	$0.27	$1.12	163,840

Qwen

Model ID	Label	Input	Output	Context
`Qwen/Qwen3.6-35B-A3B`	Qwen3.6 35B A3B	$0.14	$1.00	262,144
`qwen3.6-flash`	Qwen3.6 Flash	$0.1875	$1.125	1,000,000
`qwen3.6-max-preview`	Qwen3.6 Max Preview	$1.04	$6.24	262,144
`qwen3.6-plus`	Qwen3.6 Plus	$0.325	$1.95	1,000,000
`qwen3.7-max`	Qwen3.7 Max	$1.25	$3.75	1,000,000
`qwen3.7-plus`	Qwen3.7 Plus	$0.32	$1.28	1,000,000

Model ID	Label	Input	Output	Context
`meta-llama/Llama-3.2-3B-Instruct`	Llama 3.2 3B Instruct	$0.10	$0.335	80,000
`meta-llama/Llama-3.3-70B-Instruct`	Llama 3.3 70B Instruct	$0.90	$0.90	16,384
`meta/muse-spark-1.1`	Muse Spark 1.1	$1.25	$4.25	1,048,576

Z.ai

Model ID	Label	Input	Output	Context
`zai-org/GLM-5.1`	GLM 5.1	$0.98	$3.08	202,752
`zai-org/GLM-5.2`	GLM 5.2	$1.40	$4.40	1,048,576

Liquid AI

Model ID	Label	Input	Output	Context
`LiquidAI/LFM2-24B-A2B`	LFM2 24B A2B	$0.03	$0.12	128,000

Xiaomi MiMo

Model ID	Label	Input	Output	Context
`XiaomiMiMo/MiMo-V2.5`	MiMo V2.5	$0.14	$0.28	1,048,576
`XiaomiMiMo/MiMo-V2.5-Pro`	MiMo V2.5 Pro	$0.435	$0.87	1,048,576

MiniMax

Model ID	Label	Input	Output	Context
`MiniMaxAI/MiniMax-M2.7`	MiniMax M2.7	$0.279	$1.20	196,608
`MiniMaxAI/MiniMax-M3`	MiniMax M3	$0.30	$1.20	524,288

Moonshot AI

Model ID	Label	Input	Output	Context
`moonshotai/Kimi-K2.6`	Kimi K2.6	$0.95	$4.00	256,000
`moonshotai/Kimi-K2.7-Code`	Kimi K2.7 Code	$0.95	$4.00	262,144

NVIDIA

Model ID	Label	Input	Output	Context
`nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16`	Nemotron 3 Nano	$0.05	$0.20	262,144
`nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8`	Nemotron 3 Super	$0.09	$0.45	1,000,000
`nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16`	Nemotron 3 Ultra	$0.50	$2.50	1,000,000

xAI

Model ID	Label	Input	Output	Context
`grok-4.5`	Grok 4.5	$2.00	$6.00	500,000

Sakana AI

Model ID	Label	Input	Output	Context
`sakana/fugu-ultra`	Sakana Fugu Ultra	$5.00	$30.00	1,000,000

Poolside

Model ID	Label	Input	Output	Context
`poolside/laguna-s-2.1`	Laguna S 2.1	$0.10	$0.20	1,000,000

poolside/laguna-s-2.1 is an open-weight model (OpenMDW-1.1 license) for agentic coding and long-horizon work.

Embeddings

Embedding-only models. They don’t support chat or text-generation inference, but they are live and fully servable through POST /v1/embeddings (the deprecated POST /felix/embeddings wraps the same path). Use them as the model value in an embeddings request — not as a model_id for chat/completions or training.

Model ID	Label	Input	Output	Context
`text-embedding-3-large`	text-embedding-3-large	$0.13	—	8,191
`text-embedding-3-small`	text-embedding-3-small	$0.02	—	8,191
`text-embedding-ada-002`	text-embedding-ada-002	$0.10	—	8,191
`BAAI/bge-m3`	BGE-M3	$0.01	—	8,192
`Qwen/Qwen3-Embedding-4B`	Qwen3 Embedding 4B	$0.02	—	32,768
`Qwen/Qwen3-Embedding-8B`	Qwen3 Embedding 8B	$0.01	—	32,768

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-4 family)	0.5× input	input rate
OpenAI (GPT-5 family)	0.1× input	input rate
Google (Gemini)	0.1× input	input rate
Qwen	0.2× input	1.25× input
Mistral	0.1× input	input rate
MiniMax	0.2× input	input rate
NVIDIA (Nemotron)	0.3× input	input rate
xAI (Grok)	0.25× input	input rate
Sakana AI	0.1× input	input rate

Self-hosted models served on Modal — and any provider without an explicit cache discount — bill cached input at the standard input rate. Fireworks-served models carry per-model cache rates rather than a single ratio, so query the live catalog for exact values. For example, claude-opus-4-8 lists $5.00 input, so cache reads bill at $0.50 and cache writes at $6.25 per 1M tokens. gpt-5.1 lists $1.25 input, so cache reads bill at $0.125 per 1M tokens.

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'

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Pioneer model catalog: encoders, decoders, and inference

Encoder models (GLiNER)

Decoder models — training

Decoder models — serverless inference

Anthropic

OpenAI

Google

Mistral

DeepSeek

Qwen

Meta

Z.ai

Liquid AI

Xiaomi MiMo

MiniMax

Moonshot AI

NVIDIA

xAI

Sakana AI

Poolside

Embeddings

Prompt caching

On-demand vs. serverless inference

Querying the live catalog

​Encoder models (GLiNER)

​Decoder models — training

​Decoder models — serverless inference

​Anthropic

​OpenAI

​Google

​Mistral

​DeepSeek

​Qwen

​Meta

​Z.ai

​Liquid AI

​Xiaomi MiMo

​MiniMax

​Moonshot AI

​NVIDIA

​xAI

​Sakana AI

​Poolside

​Embeddings

​Prompt caching

​On-demand vs. serverless inference

​Querying the live catalog

Encoder models (GLiNER)

Decoder models — training

Decoder models — serverless inference

Anthropic

OpenAI

Google

Mistral

DeepSeek

Qwen

Meta

Z.ai

Liquid AI

Xiaomi MiMo

MiniMax

Moonshot AI

NVIDIA

xAI

Sakana AI

Poolside

Embeddings

Prompt caching

On-demand vs. serverless inference

Querying the live catalog