GPT-oss-120b outperforms OpenAI o3-mini and matches or exceeds OpenAI o4-mini on competition coding (Codeforces), general problem solving (MMLU and HLE) and tool calling (TauBench). It furthermore does even better than o4-mini on health-related queries (HealthBench) and competition mathematics (AIME 2024 & 2025). This performance profile positions the larger GPT-OSS model as competitive with OpenAI’s most capable small models while offering the significant advantage of local deployment and customization.
GPT-oss-20b matches or exceeds OpenAI o3-mini on these same evaluations, despite its smaller size, even outperforming it on competition mathematics and health. This is particularly impressive given that the 20b model can run on consumer hardware with just 16GB of memory. GPT-oss-120B delivers reasoning, code generation, math, and health performance nearly on par with OpenAI’s proprietary o4-mini, while GPT-oss-20B matches or outperforms o3-mini on core reasoning, coding, and math benchmarks.
The performance advantages extend beyond raw benchmark scores to practical capabilities. On the AIME math competition, GPT-oss-120B delivers approximately 96.6% accuracy using tools, demonstrating exceptional mathematical reasoning ability. Both models support configurable reasoning effort levels, full chain-of-thought access, and native agentic capabilities including function calling, web browsing, Python code execution, and Structured Outputs. The key advantage is that while achieving comparable or superior performance to OpenAI’s proprietary models, GPT-OSS models can be fine-tuned, customized, and deployed without API dependencies, rate limits, or ongoing usage costs. This makes them particularly valuable for applications requiring consistent performance, data privacy, or specialized domain adaptation.
For more detailed information, see: GPT-oss vs o4-mini: Edge-Ready, On-Par Performance — Dependable, Not Mind-Blowing