The GPT-OSS family consists of two distinct models with different parameter counts and intended use cases. The larger model is gpt-oss-120b, designed for production, general purpose, and high reasoning use cases that fits into a single H100 GPU, with 117B parameters and 5.1B active parameters. The smaller model is gpt-oss-20b, optimized for lower latency and local or specialized use cases, with 21B parameters and 3.6B active parameters.
Both models utilize a Mixture-of-Experts (MoE) architecture, which means they have significantly fewer active parameters during inference compared to their total parameter count. This design choice enables faster inference while maintaining strong performance capabilities. The gpt-oss-120b model achieves near-parity with OpenAI’s o4-mini on core reasoning benchmarks, while running efficiently on a single 80GB GPU. The gpt-oss-20b model delivers similar results to OpenAI’s o3-mini on common benchmarks and can run on edge devices with just 16GB of memory.
The performance differences between the models are notable, with gpt-oss-120b outperforming OpenAI o3-mini and matching or exceeding OpenAI o4-mini on competition coding, general problem solving, and tool calling benchmarks, while also performing better than o4-mini on health-related queries and competition mathematics. The smaller 20b model, despite its size, still matches or exceeds o3-mini performance on many evaluations. Both models support the same core features including reasoning effort adjustment, tool use capabilities, structured outputs, and fine-tuning, making the choice between them primarily dependent on computational resources and latency requirements rather than fundamental capability differences.
For more detailed information, see: GPT-oss vs o4-mini: Edge-Ready, On-Par Performance — Dependable, Not Mind-Blowing