OpenAI Shakes Up the Scene with gpt-oss-120b & gpt-oss-20b: A New Milestone for Open-Source AI? A Deep Dive into Architecture, Performance, and Security Challenges

OpenAI has officially open-sourced two powerful inference models, gpt-oss-120b and gpt-oss-20b. This article provides an in-depth analysis of their innovative MoE architecture, performance comparison with models like GPT-4o, multilingual capabilities, and OpenAI’s considerations and countermeasures for open-source model security.

Just yesterday (August 5, 2025), OpenAI dropped a bombshell, announcing the release of two new open-source weight inference models: gpt-oss-120b and gpt-oss-20b. This is not only a significant contribution from OpenAI to the open-source community but may also signal another shift in the AI development paradigm.

These two models, released under the developer-friendly Apache 2.0 license, are specifically designed for “agentic workflows” that require strong instruction following, tool use (like web search and Python code execution), and complex reasoning capabilities.

However, open-sourcing has always been a double-edged sword. While it grants developers immense freedom, it also brings potential risks. Once a model is released, malicious actors could fine-tune it to bypass safety guardrails. So, how did OpenAI strike a balance between innovation and safety this time? Let’s take a deep dive into the ins and outs of these models.

Not Just Bigger Models: A Deep Dive into MoE Architecture and Quantization

First, let’s look at the hardware specs of these two models. The gpt-oss series are not traditional monolithic models but employ a smarter, more efficient “Mixture-of-Experts” (MoE) architecture.

You can think of MoE as a top-tier consulting team. A traditional model is like a generalist consultant trying to solve all problems, whereas an MoE model has a group of specialists, activating only the most relevant few for each task. This design significantly improves the model’s efficiency.

• gpt-oss-120b: Has a total of 116.8 billion parameters, but for each inference, only about 5.1 billion “active” parameters are used per token.
• gpt-oss-20b: Has a total of 20.9 billion parameters, with 3.6 billion active parameters.

More importantly, OpenAI used the MXFP4 format for weight quantization. This technique drastically reduces the model’s memory footprint, making once-inaccessible giant models much more approachable. Now, the 120b model can run on a single 80GB GPU, and the 20b model can even run smoothly on a system with 16GB of memory. This undoubtedly opens new doors for a wide range of independent developers and researchers.

Performance Evaluation: How Capable is gpt-oss Really?

So, with all that said, how do they actually perform? OpenAI compared gpt-oss with its other models (including o3, o3-mini, o4-mini) on several authoritative benchmarks.

Challenging Top-Tier Models in Reasoning and Knowledge

According to the officially released data, the performance of gpt-oss-120b is quite impressive:

• In tests like AIME (math competition) and MMLU (university-level multitask understanding), gpt-oss-120b’s accuracy comprehensively surpasses o3-mini and closely follows o4-mini.
• Even the gpt-oss-20b, which is 6 times smaller, is surprisingly competitive, even rivaling o3-mini on some tasks.

A Dark Horse in the Medical Field

What’s most surprising is its performance in the medical domain. In the HealthBench (simulating real doctor-patient conversations) evaluation, gpt-oss-120b’s performance not only significantly surpassed GPT-4o (gpt-4o) and o4-mini but was nearly on par with the top-tier closed-source model o3.

This achievement is highly significant. For many medical environments worldwide constrained by privacy and cost, a powerful and locally deployable open-source AI model could bring revolutionary changes to smart healthcare.

Powerful Multilingual and Coding Capabilities

In MMMLU (multilingual benchmark), gpt-oss-120b demonstrated excellent capabilities in 14 languages, with an average performance very close to the high-reasoning mode of o4-mini. In tests like Codeforces (programming competition) and SWE-Bench (software engineering), its performance was also outstanding, proving its strong capabilities in code generation and understanding.

Unique Features: Harmony Chat Format and Agentic Tools

The power of gpt-oss lies not only in its performance but also in its design, which is tailor-made for “agentic” applications.

Harmony Chat Format

This is a custom chat format that uses special tokens to delineate message boundaries and clearly defines a hierarchy of instruction levels for different roles: System > Developer > User > Assistant > Tool. This hierarchical structure allows developers to control the model’s behavior more precisely, preventing users from overriding system instructions with malicious prompts.

Furthermore, the format introduces the concept of “channels,” such as analysis (for Chain-of-Thought reasoning), commentary (for tool calls), and final (for the final answer presented to the user), making the model’s thought process more transparent and controllable.

Variable Reasoning and Built-in Tools

Developers can dynamically adjust the model’s “depth of thought” by including keywords like Reasoning: low/medium/high in the system prompt. This allows developers to find the optimal balance between performance and latency costs.

The model also comes with several built-in agentic tools:

• Browser Tool: Allows the model to search and open web pages to obtain real-time information beyond its knowledge base.
• Python Tool: Enables the model to execute code in a secure Jupyter Notebook environment.
• Custom Functions: Developers can define their own tool functions for the model to call, just like with the OpenAI API.

Security: Thoughtful Considerations in the Wave of Open-Sourcing

When it comes to open-sourcing, security is always an unavoidable topic. OpenAI has clearly given this deep thought and preparation. They posed a sharp question: Could a malicious actor fine-tune gpt-oss-120b into a tool with highly dangerous capabilities?

To answer this, OpenAI conducted rigorous “adversarial fine-tuning” tests:

1. Simulated Attack: They simulated a highly skilled attacker with ample computing resources attempting to fine-tune the model to reach a “high capability” threshold in high-risk areas like “bioweapons,” “cybersecurity,” and “AI self-improvement.”
2. Test Results: The conclusion was reassuring. Even with enhanced fine-tuning using OpenAI’s leading training stack, gpt-oss-120b failed to reach the threshold for high-risk capabilities.
3. Comparison with Existing Open-Source Models: Furthermore, the evaluation found that releasing gpt-oss-120b would not significantly raise the capability ceiling of existing open-source models in areas like biosecurity, as other open-source models on the market already have comparable performance.

This indicates that while risks still exist, OpenAI has taken responsible steps to evaluate and communicate them.

Existing Challenges and What Developers Should Know

Of course, gpt-oss is not perfect. The official report candidly points out several challenges to be aware of:

• Instruction Following: Although the model has good defense against known “jailbreak” attacks, its performance in strictly following the “instruction hierarchy” (System prompt over User prompt) is not as good as o4-mini. This means developers need to design more comprehensive protection mechanisms themselves.
• Hallucinated Chain-of-Thought (CoT): OpenAI decided not to place content restrictions on the model’s thought chain. The benefit is that it facilitates academic research on the monitorability of CoT, but it also means that developers must never directly display the model’s raw thought process to end-users; it must be filtered or summarized first.
• Factual Hallucinations: Like all large language models, gpt-oss can generate factual errors. While the built-in browser tool can mitigate this problem, its accuracy without using the tool is still lower than that of larger, closed-source models.

Conclusion

The release of gpt-oss-120b and gpt-oss-20b is undoubtedly a great boon for the open-source AI community. They are not only powerful and efficient but, more importantly, were designed from the ground up with the needs of agentic applications in mind and have lowered the barrier to entry through quantization.

OpenAI’s prudent evaluation of security also sets a good example for other companies. However, the ball is now in the court of the developer community. How to use these powerful tools responsibly and how to ensure safety while innovating will be a collective challenge we all face.

This is an exciting start. We can expect that, driven by gpt-oss, a more open, diverse, and vibrant AI ecosystem will accelerate its arrival.

Frequently Asked Questions (FAQ)

Q1: What kind of hardware do I need to run these models? A1: Thanks to the MXFP4 quantization technique, the hardware barrier is significantly lowered. The gpt-oss-120b model can run on a single GPU with 80GB of VRAM (like an NVIDIA H100). The gpt-oss-20b model has even lower requirements and can run on a system with 16GB of memory, making it accessible to more developers.

Q2: How do these models compare to GPT-4o? A2: According to official data, the performance of gpt-oss-120b is very close to o4-mini (a model in the same class as GPT-4o but likely smaller in scale) on several benchmarks, and even surpasses it in specific areas (like medical conversations). However, it is not designed to completely replace top-tier closed-source models like GPT-4o, which may still have stronger overall capabilities in some respects. The core advantages of gpt-oss lie in its openness, customizability, and specific features designed for agentic workflows.

Q3: Are there security risks associated with using these open-source models? A3: Yes, all open-source models carry a risk of misuse. However, OpenAI has conducted proactive risk assessments. They simulated malicious actors fine-tuning the model and concluded that even so, the model is unlikely to reach “high-risk” capabilities in fields like biology or cybersecurity. Nevertheless, OpenAI emphasizes that the responsibility for maintaining security now rests with the entire developer community, and developers must implement their own safety measures when using them.

Q4: What is the “Harmony Chat Format,” and what’s special about it? A4: The Harmony Chat Format is a special chat structure used by gpt-oss. Its main feature is a strict instruction hierarchy (System > Developer > User > Assistant > Tool), which helps prevent users from manipulating or overriding system-set safety guardrails with malicious prompts. Additionally, it uses “channels” to distinguish between the model’s thought process and the final answer, increasing transparency and controllability, which is crucial for developing complex agentic applications.