What are open source LLMs?

Large Language Models (LLMs) are machine learning models that can understand and generate human language based on large-scale datasets. Unlike proprietary models developed by companies like OpenAI and Google, open source LLMs are licensed to be freely used, modified, and distributed by anyone. They offer transparency and flexibility, which can be particularly useful for research, development of AI agents, and customization in various applications.

Researchers and developers can access the underlying code, training mechanisms, and datasets, enabling them to deeply understand and improve these models. This openness fosters a community-driven approach to innovation, which can lead to rapid advancements not possible with closed source models.

This is part of a series of articles about open source AI.

Editor’s note: Updated the article to cover the best performing open source LLMs, according to leading benchmarks, as of 2026.

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Open source vs closed source LLMs

Open source LLMs are fully accessible for anyone to use, modify, and distribute (although some models require prior approval to use, and some might restrict commercial use of the model). This transparency allows for extensive customization and examination, enabling users to adapt the models to their needs. Open source models offer more freedom, often requiring less financial investment and enabling users to mitigate vendor lock-in risks.

Closed source LLMs are proprietary, with restricted access to the code, training methods, and datasets, limiting user control and customization. Closed source LLMs often provide improved performance and capabilities due to significant resources invested by their creators. However, this comes at a cost—both literally and figuratively. Commercial models are typically priced per token, which can be significant for large-scale usage, and users are dependent on the vendor for updates and support.

Related content: Read our guide to open source databases

Benefits of using open source LLMs

Open source large language models offer several advantages:

  • Enhanced data security and privacy: Users have full control over the data processed by these models, eliminating concerns of third-party access or data mishandling. Organizations can deploy open source LLMs on their private infrastructure, ensuring sensitive information remains in-house and complies with data protection requirements.
  • Cost savings and reduced vendor dependency: Since the code and models are freely available, organizations save on pay-per-use and licensing fees and can allocate resources toward customizing and optimizing the models to meet their needs. They can also avoid vendor lock-in scenarios where they are tied to a specific provider for updates, support, and future developments.
  • Code transparency: Users have full visibility into the model’s architecture, training data, and algorithms. This transparency fosters trust and enables detailed audits to ensure the model’s integrity and performance. Developers can modify the code to fix bugs or improve features.
  • Language model customization: Organizations can tweak the models to better suit their requirements, from adjusting the training processes to incorporating domain-specific knowledge. With closed source models, customization is often limited and might require special permissions and additional costs.
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Tips from the expert

Chris Carter

Chris Carter

Principal Product Manager

With extensive expertise in open source technologies, Chris drives innovation and excellence in the NetApp Instaclustr Managed Platform with a focus on Apache Kafka and Cassandra. With his passion for classical statistics and process improvement, Chris leverages his skills to ensure the integration of business strategy and direction into NetApp solutions.

In my experience, here are tips that can help you better leverage open source large language models (LLMs):

  • Optimize for hardware compatibility: While deploying LLMs, ensure you tailor model configurations to leverage the specific capabilities of your hardware, such as GPUs or TPUs, to achieve maximum efficiency.
  • Utilize model quantization: Implement quantization techniques to reduce model size and computational requirements without significantly compromising performance, making deployment on edge devices feasible.
  • Fine-tune with domain-specific data: Enhance the relevance and accuracy of LLMs by fine-tuning them with data specific to your industry or application domain, improving their contextual understanding and performance.
  • Integrate with complementary tools: Combine LLMs with other AI tools such as vector databases for improved search capabilities or knowledge graphs for enhanced reasoning and contextualization.
  • Implement differential privacy: Apply differential privacy techniques to ensure that the model does not inadvertently expose sensitive information from the training data, enhancing data security.

Top open source LLMs in 2026

1. DeepSeek-V3.2 / DeepSeek-R1

Deepseek logo

DeepSeek’s open source LLM family includes two complementary approaches: DeepSeek-V3 as a high-performance general-purpose model and DeepSeek-R1 as a reasoning-focused model. DeepSeek-V3 uses a mixture-of-experts architecture optimized for efficient training and inference at scale, while DeepSeek-R1 builds on this foundation with reinforcement learning to enhance reasoning capabilities. Together, they provide both strong general language performance and advanced problem-solving abilities.

Project information:

Key features include:

  • Mixture-of-experts architecture: Uses large total parameters with selective activation (e.g., 671B total, 37B active) to improve efficiency and scalability
  • Reinforcement learning-driven reasoning: DeepSeek-R1 develops advanced reasoning behaviors such as chain-of-thought, self-reflection, and verification through RL
  • Multi-stage training pipeline: Combines pretraining, supervised fine-tuning, and reinforcement learning to improve both general and reasoning capabilities
  • Distillation into smaller models: Transfers reasoning capabilities from large models into smaller, more efficient variants
  • Extended context support: Handles long inputs with context windows up to 128K tokens
  • Efficient training and inference design: Includes innovations such as multi-token prediction and optimized training strategies to reduce compute cost

2. Google Gemma 4

Gemma 4 logo

Gemma 4 is a family of open models derived from Gemini research, aiming to maximize performance relative to model size while remaining efficient enough for local deployment. It includes multiple model sizes ranging from edge devices to workstation-scale systems, and supports multimodal reasoning, multilingual tasks, and agentic workflows.

Project information:

Key features include:

  • Agentic workflows: Supports function calling and task planning for building autonomous agents
  • Multimodal reasoning: Processes audio and visual inputs alongside text for richer applications
  • Multilingual support: Handles over 140 languages with contextual understanding beyond translation
  • Efficient architecture: Optimized to run on local hardware, including edge devices and consumer GPUs
  • Flexible fine-tuning: Can be adapted to specific tasks using common training frameworks
  • Edge deployment capability: Smaller variants run offline with low latency on devices like phones and embedded systems

3. Z.ai GLM 5

Z.ai GLM 5 logo

GLM-5 is a large-scale mixture-of-experts model for complex reasoning and long-horizon agentic tasks. It expands over previous versions in total parameters and training data, while introducing optimizations such as sparse attention to reduce deployment cost. The model combines large-scale pretraining with reinforcement learning to improve reasoning, coding, and tool-use performance across a range of benchmarks.

Project information:

Key features include:

  • Mixture-of-experts scaling: Uses a large parameter pool (744B total, 40B active) to improve efficiency while maintaining high performance
  • Long-context support: Handles extended contexts with optimized attention mechanisms such as DeepSeek Sparse Attention
  • Reinforcement learning infrastructure: Uses an asynchronous RL system to improve training efficiency and enable iterative refinement
  • Agentic task optimization: Designed for complex multi-step tasks such as systems engineering and tool-based workflows
  • Broad benchmark performance: Demonstrates strong results across reasoning, coding, and tool-use evaluations

4. Kimi K2.5

Kimi K2.5 logo

Kimi K2.5 is a multimodal mixture-of-experts model for agentic workflows, combining vision and language understanding with coordinated multi-agent execution. It is trained on a large corpus of mixed visual and text data and supports both fast responses and deeper reasoning modes. The model is intended to handle complex workflows by decomposing tasks and executing them across multiple agents.

Project information:

Key features include:

  • Native multimodality: Trained on joint vision and text data for cross-modal reasoning and visual grounding
  • Agent swarm execution: Decomposes tasks into parallel subtasks handled by coordinated agents
  • Dual interaction modes: Supports both fast responses and step-by-step reasoning depending on task needs
  • Large context window: Handles up to 256K tokens for long interactions and documents
  • Tool and coding integration: Generates code from visual inputs and orchestrates tools for complex workflows

5. MiniMax M2.5

MiniMax M2.5 logo

MiniMax M2.5 is a reinforcement learning–driven model focused on real-world productivity tasks such as coding, tool use, and office workflows. It is trained across a large number of simulated environments to improve task decomposition, planning, and execution. The model emphasizes efficiency in reasoning and cost, enabling faster completion of complex tasks with fewer steps.

Project information:

Key features include:

  • Reinforcement learning at scale: Trained on hundreds of thousands of environments to improve real-world task performance
  • Strong coding capabilities: Supports full software lifecycle tasks, from system design to testing across multiple languages
  • Efficient task decomposition: Breaks down complex problems into structured steps with reduced token usage
  • Advanced tool use and search: Performs multi-step retrieval and tool interactions with improved decision-making
  • High throughput and low cost: Optimized for fast inference and reduced operational cost in agentic workflows

6. LLaMA 4

Meta AI logo

LlaMA 4 is the latest generation of Meta’s open-weight language models, designed for scalability, efficiency, and multimodal capabilities. It supports both text and visual inputs natively, enabling more advanced reasoning across different data types. The models are optimized for deployment across environments, from single-GPU setups to large-scale distributed systems, and include variants tailored for performance and cost efficiency.

Project information:

Key features include:

  • Native multimodal capabilities: Processes both text and images using a unified architecture with early fusion of modalities
  • Long context window: Supports context lengths up to 10 million tokens for handling long documents and extended conversations
  • Efficient deployment: Designed to run efficiently even on a single high-performance GPU while scaling to larger infrastructures
  • Advanced reasoning and coding performance: Demonstrates strong results across benchmarks for reasoning, coding, and knowledge tasks
  • Image grounding capabilities: Provides detailed understanding and reasoning over visual inputs
  • Built-in safety mechanisms: Includes system-level protections and tools to mitigate risks in generative AI applications

7. Qwen 3.5

Qwen 3.5 logo

Qwen 3.5 is a large-scale open-weight model designed as a native multimodal agent, combining vision and language capabilities within a unified architecture. It uses a hybrid design that mixes sparse mixture-of-experts with linear attention mechanisms to improve efficiency while maintaining strong performance. The model is trained on large multimodal datasets and supports long-context reasoning, tool use, and multilingual tasks.

Project information:

Key features include:

  • Hybrid MoE architecture: Combines sparse mixture-of-experts with linear attention to balance performance and inference efficiency
  • Native multimodality: Integrates text and visual inputs through early fusion for cross-modal reasoning
  • Large context window: Supports up to 1M tokens for long documents, conversations, and video inputs
  • Agentic tool use: Includes built-in support for tool calling, search, and code execution within workflows
  • Expanded multilingual support: Covers over 200 languages and dialects for global use cases
  • Efficient inference design: Activates a small subset of parameters per request to reduce compute cost while maintaining capability

Related content: Read our guide to managed open source

NetApp Instaclustr: Empowering open source large language models

Open source large language models have revolutionized natural language processing (NLP) and artificial intelligence (AI) applications by enabling advanced text generation, sentiment analysis, language translation, and more. However, training and deploying these models can be resource-intensive and complex. NetApp Instaclustr steps in to support open source large language models, providing a robust infrastructure and managed services that simplify the process. In this article, we will explore how NetApp Instaclustr empowers organizations to leverage the full potential of open source large language models.

Training large language models requires substantial computational resources and storage capacity. NetApp Instaclustr offers a scalable and high-performance infrastructure that can handle the demanding requirements of model training. By leveraging the distributed computing capabilities and storage capacity provided by NetApp Instaclustr, organizations can efficiently train large language models, reducing the time and resources required for the training process.

Once trained, deploying large language models can present challenges due to their size and resource requirements. NetApp Instaclustr simplifies the deployment process by offering managed services that handle the infrastructure and operational aspects. It takes care of provisioning the necessary compute resources, managing storage, and ensuring high availability and fault tolerance. This allows organizations to focus on utilizing the models for their specific NLP and AI applications without the burden of managing the underlying infrastructure.

NetApp Instaclustr leverages its scalable infrastructure to support the deployment of open source large language models. As the demand for processing power and storage increases, organizations can easily scale their infrastructure up or down to accommodate the workload. This scalability ensures optimal performance, enabling efficient and fast processing of text data using large language models.

Open source large language models often deal with sensitive data, and ensuring data security is crucial. NetApp Instaclustr prioritizes data security by providing robust security measures, including encryption at rest and in transit, role-based access control, and integration with identity providers. These security features help organizations protect their data and comply with industry regulations and privacy standards.

NetApp Instaclustr offers comprehensive monitoring and support services for open source large language models. It provides real-time monitoring capabilities, allowing organizations to track the performance and health of their models. In case of any issues or concerns, NetApp Instaclustr’s support team is readily available to provide assistance and ensure minimal downtime, enabling organizations to maintain the reliability and availability of their language models.

Managing the infrastructure for open source large language models can be costly. NetApp Instaclustr helps organizations optimize costs by offering flexible pricing models. With pay-as-you-go options, organizations can scale their resources based on demand and pay only for what they use. This eliminates the need for upfront investments and provides cost predictability, making it more accessible for organizations of all sizes to leverage open source large language models.

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