What is an Open Weight AI

An open weight model is a type of machine learning model, commonly in artificial intelligence, where the model's weights—the numerical parameters that dictate how it transforms input data into outputs—are made publicly available. These weights, honed during training, define the model’s capabilities. Unlike proprietary models, where weights and often the architecture are kept secret (e.g., OpenAI’s ChatGPT historically), open weight models let anyone download, inspect, and adapt the weights. This transparency allows developers, researchers, or hobbyists to fine-tune the model, integrate it into projects, or study its mechanics without retraining it from scratch—a process that’s resource-intensive.

Recent examples highlight this trend’s evolution as of March 31, 2025. OpenAI, long a closed-model advocate, just announced its first open-weight model since GPT-2, teased by Sam Altman on X as a reasoning-capable rival to models like o3-mini, set for release in the coming months with accessible weights.

Similarly, DeepSeek V3 from China leads in non-reasoning benchmarks, offering fully downloadable weights alongside its R1 reasoning model, which competes with OpenAI’s o1 in math and coding. Mistral Large 2, with its 128k token context and coding prowess, and Google’s Gemma 3, excelling in enterprise tasks, also provide open weights, as does NVIDIA’s Nemotron series (e.g., Super 49B), targeting reasoning applications. These releases—often from major players like OpenAI, Google, and NVIDIA—show a shift toward openness, though full open-source status (including training data) varies.

Here are a few examples of some of the latest open weight models as of March 31, 2025, based on recent developments in the AI landscape:

1. OpenAI's Upcoming Open-Weight Model

   OpenAI, known for its proprietary models like ChatGPT, has announced plans to release its first open-weight model since GPT-2 (released in 2019). This new model, teased by CEO Sam Altman, will feature reasoning capabilities similar to the o3-mini model and is expected to launch in the coming months. While details like parameter size or exact release date remain under wraps, it’s positioned to compete with other open-weight leaders, offering developers the ability to customize its weights for specific tasks.

   
   

2. DeepSeek V3

   Developed by the Chinese AI startup DeepSeek, the V3 model (released as DeepSeek V3-0324) is a standout in the open-weight space. It’s notable for being the first open-weight model to lead in non-reasoning benchmarks, as highlighted in posts on X. DeepSeek also offers the R1 reasoning model, which matches or exceeds OpenAI’s o1 in areas like math and coding. These models are fully accessible, with weights available for developers to download and adapt, making them a powerful option for cost-effective customization.

   
   

3. Mistral Large 2

   Released by Mistral AI, Mistral Large 2 is an open-weight model designed to rival top models like Meta’s Llama 3.1 405B. With a 128k token context window and multilingual capabilities, it excels in coding tasks across languages like Python and Java. Its weights are publicly available, allowing fine-tuning for specialized applications, and it’s been praised for reduced hallucinations compared to its predecessor, Mistral 7B.

   
   

4. Google Gemma 3

   Google’s latest open-weight offering, Gemma 3, builds on the success of the Gemma series. It’s been noted for impressive performance in enterprise tasks like data extraction, scoring close to Google’s proprietary Gemini 2.0 Flash in evaluations. The weights are openly available, making it a strong choice for developers looking for a cost-effective, powerful model that can be run locally or fine-tuned.

   
   

5. NVIDIA Nemotron Series

   NVIDIA recently introduced its Nemotron family of open-weight models, including Nano (8B), Super (49B), and Ultra (249B). These are reasoning-focused models based on Llama architecture. Early testing suggests the Super 49B model achieves strong results, like 64% on the GPQA Diamond benchmark in reasoning mode, with weights available for public use, targeting applications requiring robust logical processing.