Infographic

Model Architecture & Efficiency

117 B

gpt-oss-120b Total Parameters

Overall model size.

5.1 B

gpt-oss-120b Active Parameters

Parameters engaged during inference.

280‑fold

Cost Reduction

Year‑over‑year inference cost drop.

1.7% vs 8%

Performance Gap Reduction

Open‑weight vs closed‑source model gap in benchmarks.

Parameter Utilization in gpt-oss Models

The chart highlights the massive total capacity of each model versus the fraction used during inference thanks to the Mixture-of-Experts (MoE) architecture. gpt-oss-120b boasts 117B total parameters but activates only 5.1B, while gpt-oss-20b achieves 21B total with 3.6B active.

Inference Cost Reduction

Between November 2022 and October 2024, the cost of running GPT‑3.5‑level systems dropped over 280‑fold, underscoring the economic advantage of deploying efficient models like gpt‑oss.

Key Insights

MoE combined with 4‑bit quantization delivers high efficiency, enabling single‑GPU deployment while maintaining competitive performance, and a dramatic cost advantage that fuels open‑weight adoption.

Strategic Significance of the Open-Weight Release

OpenAI’s decision to release gpt‑oss‑120b and gpt‑oss‑20b under an Apache 2.0 license marks a calculated pivot. By giving developers on‑premise flexibility, OpenAI aims to lead the democratization wave, build an ecosystem that offsets API revenue, and position itself as a thought leader in safety and interpretability through raw Chain‑of‑Thought access. This move also sends a strong signal to competitors, forcing a re‑evaluation of how much capability is safe or desirable to open‑source.

Core Strengths of gpt-oss

1

Efficient Architecture

MoE activates only a subset (5.1B/3.6B) of the 117B/21B parameters, enabling deployment from single GPUs to consumer hardware.

2

Advanced Reasoning

Strong performance in mathematics and code generation, with adjustable reasoning effort levels.

3

Agentic Capabilities

Built‑in support for tool use, multi‑step workflows, and optimized for code execution.

4

Full Transparency

Raw Chain‑of‑Thought is available for research, with filtered summaries safe for end‑users.

5

Deployment Flexibility

128k‑token context window, compatible with multiple frameworks and environments.

Key Innovations at a Glance

📦

Open Weight

Model weights publicly released under Apache 2.0.

🧠

Mixture‑of‑Experts

Sparse activation yields efficiency.

🔍

Chain‑of‑Thought Transparency

Raw reasoning for safety research.

🛠

Hybrid Strategy

Mix open and proprietary models for cost and control.

Key open‑source models advancing the field:

Competitive Landscape - Open Source Contenders

Llama 3.3

Mixtral 8x7B

DeepSeek‑R1 & V2.1

Falcon Series

Gemma