Can gpt-oss-120b Outperform Llama 3, Mixtral, and DeepSeek?

OpenAI has recently released its long-awaited open-weight models, gpt-oss-120b and gpt-oss-20b, marking the first major open-weight launch since GPT-2. These models are designed for local use and carry an Apache 2.0 license, making them available for developers and businesses that need private, customizable AI solutions.

Overview of OpenAI gpt-oss Models

The new models deliver robust reasoning, tool use, and coding performance while running directly on consumer hardware. Key points include:

gpt-oss-120b: Comprising 117B parameters with 80GB VRAM requirements, it is optimized for high-end tasks such as multi-step logic and complex problem solving.
gpt-oss-20b: With 21B parameters and 16GB VRAM requirements, this model is well-suited for desktop and laptop applications.
Built for efficient local inference without relying on cloud resources.
Benefit from techniques like MXFP4 Quantization and Mixture-of-Experts architecture, which improve performance and memory efficiency.

Performance and Benchmark Comparisons

According to benchmark data, gpt-oss-120b holds its own against well-known competitors in various tasks. Consider the following highlights:

Model	Reasoning (MMLU)	Math (AIME 2025)	Science (GPQA Diamond)	Coding (Codeforces Elo)	Function Use (Tau-Bench)	Health (HealthBench)
gpt-oss-120b	90%	97.9%	80.1%	2622	67.8%	57.6%
gpt-oss-20b	85.3%	98.7%	71.5%	2516	54.8%	42.5%
Llama 3 70B	82-88%	86-89%	~77-83%	2470-2510	~61%	~54%
Mixtral 8x7B	82-84%	~85%	~72-80%	2410-2480	~62%	~52%
DeepSeek R1-0528	87%	97.6%	76.8%	2560	~60%	~53%

These results indicate that gpt-oss-120b frequently matches or exceeds the performance of its competitors across reasoning, math, and coding tasks.

Real-World Pros and Cons

The new OpenAI models offer several benefits as well as some challenges:

Benefits:
- Runs entirely on local systems, ensuring data privacy and compliance.
- Full customization for specific workflows and applications.
- Zero recurring API or royalty fees.
- Excellent performance in complex reasoning and function calling tasks.
Drawbacks:
- A higher propensity for hallucinations when answering complex factual questions.
- The high-end model requires significant compute resources, making it less suitable for lower-end hardware.
- Lacks built-in safety controls, requiring developers to manage potential biases and toxicity.

Expert Opinions and Comparative Insights

Several industry experts have weighed in on these models. One reviewer noted, 'OpenAI's gpt-oss models deliver state-of-the-art performance for reasoning and code execution, making them a strong choice for offline applications.' Another specialist mentioned, 'The open licensing and customizable nature of these models shift the focus toward tailored AI solutions rather than one-size-fits-all approaches.'

Final Thoughts

OpenAI's release of the gpt-oss-120b and gpt-oss-20b provides a viable alternative for those seeking powerful and customizable local AI solutions. With competitive benchmark scores and a clear focus on reasoning-intensive tasks, these models offer developers a pathway to build private, efficient, and cost-effective AI applications.