Compare Devstral vs. Laguna XS 2.1 in 2026

Laguna XS 2.1

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Description

Devstral is a collaborative effort between Mistral AI and All Hands AI, resulting in an open-source large language model specifically tailored for software engineering. This model demonstrates remarkable proficiency in navigating intricate codebases, managing edits across numerous files, and addressing practical problems, achieving a notable score of 46.8% on the SWE-Bench Verified benchmark, which is superior to all other open-source models. Based on Mistral-Small-3.1, Devstral boasts an extensive context window supporting up to 128,000 tokens. It is designed for optimal performance on high-performance hardware setups, such as Macs equipped with 32GB of RAM or Nvidia RTX 4090 GPUs, and supports various inference frameworks including vLLM, Transformers, and Ollama. Released under the Apache 2.0 license, Devstral is freely accessible on platforms like Hugging Face, Ollama, Kaggle, Unsloth, and LM Studio, allowing developers to integrate its capabilities into their projects seamlessly. This model not only enhances productivity for software engineers but also serves as a valuable resource for anyone working with code.

Description

The Laguna XS 2.1 is an enhanced coding model that operates as an open weight agentic system, ideal for long-duration tasks on local machines. Featuring a 33-billion-parameter Mixture-of-Experts framework with 3 billion parameters activated per token, this model maintains the efficient architecture of Laguna XS.2 while significantly advancing performance in multilingual software engineering and terminal-style tasks. It is specifically engineered to assist coding agents in reviewing repositories, reasoning through intricate changes, utilizing various tools, executing commands, and maintaining continuity throughout extended projects. With a generous 256K context window, the model enables agents to effectively manage extensive codebases, lengthy histories, and complex multi-step workflows. Laguna XS 2.1 benefits from support from platforms like vLLM, SGLang, NVIDIA TensorRT-LLM, Hugging Face Transformers, and Ollama, with plans for native integration with llama.cpp in the future. The model is offered in various checkpoint formats, including BF16, FP8, INT4, and NVFP4, granting developers the flexibility to select between high fidelity and configurations optimized for limited VRAM or computational resources. This adaptability makes it an excellent choice for a wide range of development environments and requirements.