GPT-OSS 120B TEE

GPT-OSS 20B TEE

Open-source GPT model with 21B parameters (3.6B active) using MoE architecture. Running inside a TEE (Trusted Execution Environment), with provider attestation support.

GPT OSS 120B

An open-weight, 117B-parameter Mixture-of-Experts (MoE) language model designed for high-reasoning, agentic, and general-purpose production use cases. It activates 5.1B parameters per forward pass and is optimized to run on a single H100 GPU with native MXFP4 quantization. The model supports configurable reasoning depth, full chain-of-thought access, and native tool use, including function calling, browsing, and structured output generation.

GPT OSS Safeguard 20B

gpt-oss-safeguard is a first open weight reasoning model specifically trained for safety classification tasks to help classify text content based on customizable policies. As a fine-tuned version of gpt-oss, gpt-oss-safeguard is designed to follow explicit written policies that you provide. This enables bring-your-own-policy Trust & Safety AI, where your own taxonomy, definitions, and thresholds guide classification decisions. Well crafted policies unlock gpt-oss-safeguard's reasoning capabilities, enabling it to handle nuanced content, explain borderline decisions, and adapt to contextual factors.

GPT OSS 20B

An open-weight 21B parameter model released under the Apache 2.0 license. It uses a Mixture-of-Experts (MoE) architecture with 3.6B active parameters per forward pass, optimized for lower-latency inference and deployability on consumer or single-GPU hardware. The model is trained in OpenAI's Harmony response format and supports reasoning level configuration, fine-tuning, and agentic capabilities including function calling, tool use, and structured outputs.

GPT 5.6 Luna

GPT-5.6 Luna is the fast, cost-efficient model in OpenAI's GPT-5.6 series. It is suited for high-volume chat, classification, lightweight agentic workflows, and latency-sensitive reasoning tasks.

GPT 5.6 Luna Pro

GPT-5.6 Luna with Pro reasoning mode. Pro mode performs more model work for difficult tasks; reasoning effort remains independently configurable and defaults to medium.