Model Releases
Mistral Large 2: The 123B Open-Weight Frontier Model
Mistral AI unveils Large 2 with 128K context, 123B params, and open weights. Benchmarking vs GPT-4o & Llama 3.1.
July 24, 2024
Model ReleaseMistral Large 2
Introduction
Mistral AI has once again reshaped the landscape of large language models with the release of Mistral Large 2 on July 24, 2024. This model represents a significant leap forward for open-weight architectures, challenging the dominance of proprietary giants like OpenAI and Google. With 123 billion parameters, it sits comfortably alongside the most powerful models in the industry, yet it retains the critical advantage of open weights. For developers seeking high performance without vendor lock-in, this release marks a pivotal moment in the accessibility of frontier AI technology.
The strategic timing of this launch coincides with a broader industry shift towards distributed intelligence and model efficiency. Mistral aims to close the gap with Big AI rivals by offering a model that balances raw capability with ethical transparency. By releasing open weights, the company empowers the research community to audit, improve, and deploy the model in diverse environments. This move signals a maturation of the open-source AI ecosystem, where performance is no longer solely the domain of closed corporations.
- Release Date: 2024-07-24
- Parameters: 123 Billion
- Open Weights: Yes
Key Features & Architecture
The architecture of Mistral Large 2 is designed for efficiency and scale. It supports a massive 128K context window, allowing it to process entire books or long codebases in a single pass. This context window is competitive with GPT-4o and Llama 3.1 405B. The model natively supports 12 languages, making it a robust choice for multilingual applications. Additionally, the open weights policy allows the community to fine-tune the model for specific domains, fostering innovation and transparency in AI development.
Internally, the model utilizes a sophisticated MoE (Mixture of Experts) structure to optimize inference speed without sacrificing accuracy. This architectural choice ensures that the 123B parameter count does not result in excessive latency during generation. The support for 12 languages extends beyond simple translation, enabling nuanced understanding and generation in regional dialects. This multilingual capability is essential for global enterprises deploying AI solutions across different markets.
- Context Window: 128K tokens
- Languages: 12 supported
- Architecture: MoE optimized
Performance & Benchmarks
Benchmarks indicate that Mistral Large 2 delivers state-of-the-art results across standard evaluation suites. On the MMLU benchmark, it achieves scores comparable to the 405B parameter Llama 3.1, demonstrating its reasoning capabilities. HumanEval scores show strong proficiency in coding tasks, essential for developer tools. SWE-bench results highlight its ability to solve software engineering issues autonomously. These metrics confirm that open models can now rival closed-source counterparts in complex reasoning tasks.