That input is just a press release/blog post announcing new open-weight models from OpenAI. I don't care about their marketing fluff, but here is the requested summary.

Abstract:

OpenAI announced the release of two new state-of-the-art open-weight language models, gpt-oss-120b and gpt-oss-20b, under the Apache 2.0 license. These models, trained with advanced techniques including reinforcement learning informed by frontier systems, emphasize strong reasoning and efficient deployment. The gpt-oss-120b model approaches the performance of OpenAI o4-mini on reasoning benchmarks and runs on a single 80 GB GPU, while the gpt-oss-20b model matches o3-mini and is deployable on edge devices with only 16 GB of memory. Both models show strong tool use and agentic capabilities, even outperforming proprietary models like o1 and GPT-4o on specialized benchmarks (Tau-Bench, HealthBench). The release prioritizes safety, which was assessed via adversarial fine-tuning and external review, and the weights are available on Hugging Face, with ecosystem support from numerous providers for flexible deployment.

Announcing gpt-oss: New Open-Weight Models Focused on Reasoning and Efficiency

• 0:00 Release of Models: OpenAI released gpt-oss-120b and gpt-oss-20b, described as state-of-the-art open-weight language models.
• Licensing and Cost: Models are available under the flexible Apache 2.0 license and are optimized for low-cost deployment.
• Performance Benchmarks:
  • gpt-oss-120b: Near parity with OpenAI o4-mini on core reasoning benchmarks. Can run on a single 80 GB GPU.
  • gpt-oss-20b: Similar results to o3-mini on common benchmarks. Can run on devices with 16 GB memory.
• Key Capabilities: Models excel in reasoning (CoT), tool use (including web search/code execution), and function calling. They outperform proprietary models like o1 and GPT-4o on HealthBench and competition mathematics.
• Reasoning Effort Control: Models support adjustable reasoning efforts (low, medium, high) via system message, trading latency for performance.
• Architecture (MoE): Both are Transformer models using Mixture-of-Experts (MoE).
  • gpt-oss-120b: 117B total parameters, activating 5.1B per token, 36 layers, 128 experts per layer (4 active).
  • gpt-oss-20b: 21B total parameters, activating 3.6B per token, 24 layers, 32 experts per layer (4 active).
• Training Details: Trained on English-heavy dataset focusing on STEM/coding. Uses the o200k_harmony tokenizer (also open-sourced). Post-training used supervised fine-tuning and high-compute RL, aligning with the OpenAI Model Spec but without direct supervision on Chain-of-Thought (CoT).
• Safety Focus: Comprehensive safety training, evaluation via adversarial fine-tuning using the Preparedness Framework. External experts reviewed the methodology. Developers are cautioned not to show raw CoTs to end-users.
• Deployment & Availability: Weights are available on Hugging Face (natively quantized in MXFP4). Partnerships exist with major platforms (Azure, AWS, vLLM, etc.) and hardware vendors (NVIDIA, AMD) for broad accessibility. Microsoft is bringing optimized versions to Windows devices locally.
• Ecosystem Role: Open models are intended to complement hosted API models, fostering research, innovation, and lowering barriers for resource-constrained entities.