gpt-oss vs GPT-5: which OpenAI model is right for you?

This August, OpenAI announced two new models: GPT-5, the latest edition in OpenAI’s incredibly popular GPT series, and gpt-oss, the first open-weight model since GPT-2 in 2019. Both models have two variants. GPT-5 has (i) a standalone and (ii) a thinking version. gpt-oss has two parameters counts, (i) gpt-oss-20b and (ii) gpt-oss-120b. All of these models have drummed up a fair amount of fanfare in the AI and tech community.

Previously, OpenAI seemed to only be invested in their line of proprietary state-of-the-art models. As a result, open-weight enthusiasts shifted from OpenAI to other models like Qwen3 by Alibaba and Mistral. Now, that might change: with a competitive open-weight offering, open-weight developers can leverage a model that’s competitive with some of OpenAI’s proprietary models like o4-mini.

However, GPT-5 is a stunning model and will also command a lot of traction. Today, we want to discuss when GPT-5 or gpt-oss makes sense, uncovering the differences between their performance and constraints.

What is an open-weight model?

An open-weight model is an open-source model where the model’s source code, the model’s weights, and the model’s architecture is public and available for free use. Under the Apache 2.0 license, developers can build on top of gpt-oss, making their own tweaks and changes to satisfy their use cases. For example, if developers wanted to re-train gpt-oss and incorporate a different Activation Function (e.g. reverting to GLU from Swish), they would be free to do that.

GPT-5 and gpt-oss are both decoder-only transformer models with support for Chain-of-Thought (CoT) reasoning, tool use, and structured outputs. They likely share similar architectures; however, GPT-5’s architecture is not open-source.

Side-by-side comparison of GPT-5 and gpt-oss

There are some easy side-by-side comparisons between GPT-5 and gpt-oss that help visualize the difference between them. On benchmarks specifically, GPT-5 performs better than gpt-oss, but gpt-oss still produces strong scores against recent models that were considered state-of-the-art.

Context Window

GPT-5 has a 400K token context window. This is significantly larger than gpt-oss’s 131K token context window. However, both of these context windows are medium-sized, smaller than models like Gemini that have context windows in the millions.

GPT-5 supports images and videos as inputs. gpt-oss does not. However, because gpt-oss is open-source, it can easily be deployed in tandem with image and video models.

Intelligence

Based on the Artificial Analysis Intelligence Index—a metric that spans many intelligence metrics like GPQA Diamond, AIME, and SciCode—GPT-5 (scoring 68) outperforms gpt-oss (scoring 59). However, a score of 59 still positions gpt-oss as a top industry model.

When should you use GPT-5 over gpt-oss?

Today, GPT-5 is OpenAI’s most advanced model. It outperforms not only gpt-oss but also other competitive proprietary models like Google Gemini, though models like Claude Opus 4.1 are holding ground. GPT-5’s design makes it particularly attuned to writing code, solving mathematical problems, and engaging in the scientific process. Similar to its predecessors like GPT-4.5 or GPT-4, it is also strong at literary and synthesis tasks and can easily invoke 3rd-party tools.

The Takeaways:

GPT-5 is best for organizations that need the most powerful model
GPT-5 is straightforward to use through OpenAI’s APIs
GPT-5 is multi-modal, making it apt for organizations that also need to process images and videos.

When should you use gpt-oss over GPT-5?

gpt-oss is an incredibly advanced open-weight model that is entirely configurable by developers. Accordingly, gpt-oss is ideal for companies that are trying to closely fine-tune a model for their specialized needs, even beyond the fine-tuning options allowed of GPT-5. Because gpt-oss can be self-hosted, developers can also pair it with any custom inference engine they choose that suits their needs.

While GPT-5 achieves better scores on benchmarks, gpt-oss is competitive with the previous iteration of state-of-the-art OpenAI models. gpt-oss-120b is shown to fare similarly to OpenAI’s successful o4-mini model; likewise, gpt-oss-20b achieves similar benchmarks as o4-mini’s immediate predecessor, o3-mini.

Because gpt-oss models are open-weight, they can be deployed to downloaded applications and operate without an Internet connection—something that’s impossible with GPT-5 which is only accessible through OpenAI’s APIs. This makes gpt-oss significantly more extensible than OpenAI’s flagship line.

This extensibility is particularly evidenced by gpt-oss’s lighter variant, gpt-oss-20b. The model can be trained on just 16GB of RAM. Its lightweight footprint makes it an ideal model to deploy on devices with minimal hardware resources, such as wearables or sensors.

The Takeaways:

gpt-oss is best for organizations that need a fully customizable model for a specific need
gpt-oss is cheaper than GPT-5 as it can be set-up on internal infrastructure where costs are virtually “bare-metal”
gpt-oss is lightweight and proficient, ideal for organizations that need a fast model with minimal headache

A Closing Thought

GPT-5 and gpt-oss are both exciting new editions to OpenAI’s model line. GPT-5 offers state-of-the-art performance with incredible mathematical and coding scores. gpt-oss is OpenAI’s first open-weight model in years and offers a powerful open source model that’s strongly competitive with last year’s offerings.

Depending on your organizations needs, either model might be the preferred choice. Companies that value model performance and don’t mind costs will find value in GPT-5. Companies that want to customize models and control costs will prefer gpt-oss. If you are interested in self-hosting gpt-oss on a developer-friendly AI infrastructure platform, check out our example on how to deploy it on Modal.