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Open source LLMs (large language models) are transforming how businesses and developers build with AI. Unlike proprietary AI models, open source LLMs provide full access to their code, model weights, and architecture. This makes them easier to customise, audit and deploy across a wide range of applications.
An open source LLM is a large language model with publicly available code and model weights. You can use, modify, and deploy it without incurring licensing fees, making it ideal for flexible and transparent AI development.
By 2025, some of the best open-source LLMs are expected to rival commercial alternatives in terms of performance and scalability. This article compares the top open source LLMs available today, examines their real-world applications, and provides practical guidance on how to evaluate and deploy them effectively.
Open source LLMs offer greater flexibility, cost efficiency and transparency than proprietary models. For organisations looking to maintain control over data, fine-tune models for domain-specific tasks or deploy AI securely on-premise, open source options provide the freedom to adapt without being locked into a vendor ecosystem.
A recent study by the Linux Foundation highlights that nearly 90% of organisations adopting AI integrate open source technologies, emphasising the transformative impact of open source LLMs on business and development practices.
Unlike proprietary LLMs that often require paid APIs or restrictive licensing, open source models are typically free to use and modify. This allows developers to customise outputs, improve accuracy for niche tasks and deploy models within private infrastructure. Transparent training data and architecture also enable better auditing and bias detection.
Open-source large language models often require more technical expertise to deploy and maintain. They may lack polished interfaces or hosted infrastructure. Performance can vary depending on hardware, training methods and community support. Licensing terms also vary, so it is recommended to conduct legal and compliance reviews before implementation.
Whether you're deploying AI in production or evaluating research models, the best open source LLMs in 2025 strike a balance between performance, adaptability, and ease of access. Below is a curated list of top models, using the latest versions, structured for clear comparison.
Developer: Meta AI
Parameter Sizes:
Meta's LLaMA 4 represents a significant advancement in large language models, introducing native multimodality and a Mixture-of-Experts (MoE) architecture. This design enables the models to process both text and images, providing more versatile AI applications.
Key Features:
Both models are instruction-tuned and support 12 languages, making them suitable for a wide range of applications across different domains. Their open-weight nature allows for customisation and integration into various platforms, including Hugging Face and AWS.
Ideal if you're developing sophisticated AI systems that require handling of extensive context, multimodal inputs, and demand efficient performance across diverse tasks.
Developer: Mistral AI
Parameter sizes: Not publicly disclosed
Use cases: Coding, STEM reasoning, multimodal understanding, enterprise automation
License: Proprietary
Best for: Enterprises seeking high-performance AI with cost-effective deployment options
Mistral Medium 3 is a frontier-class dense language model optimised for enterprise use. It delivers state-of-the-art performance at significantly lower cost, while maintaining high usability, adaptability, and deployability in enterprise environments.
Key features:
Ideal if you're looking for a cost-effective, high-performance AI solution that can be tailored to your enterprise needs.
Developer: Technology Innovation Institute (TII)
Parameter sizes: 0.5B, 1.5B, 1.5 B-Deep, 3B, 7B, 34B
Use cases: Long-context processing, multilingual applications, edge deployments, STEM tasks
License: TII Falcon License (Apache 2.0-based)
Best for: Organisations seeking efficient, scalable, and multilingual open-source LLMs suitable for a range of applications from edge devices to enterprise systems.
Falcon-H1 is the latest addition to TII's Falcon series, introducing a hybrid architecture that combines the strengths of Transformer-based attention mechanisms with State Space Models (SSMs), specifically Mamba.
Key features:
Ideal if you're looking for versatile, high-performance LLMs that can be deployed across various platforms and use cases, from mobile devices to large-scale enterprise systems.
Developer: Microsoft
Parameter size: 14B
Use cases: Complex reasoning, mathematical problem-solving, coding tasks
License: MIT (fully open)
Best for: Developers and organisations seeking a compact model that delivers high performance in reasoning-intensive tasks without the need for extensive computational resources.
Phi-4 is Microsoft's latest small language model, designed to excel in complex reasoning tasks, including mathematical and coding applications.
Key features:
Ideal for building AI features in lightweight apps, embedded systems, or CPU-constrained environments that require strong performance without relying on GPUs.
Developer: Mistral AI
Parameter sizes: 12.9B active parameters (Mixture of Experts)
Use cases: RAG systems, scalable AI assistants, enterprise automation
Licence: Apache 2.0 (fully open)
Best for: Enterprises needing high-throughput, cost-effective models with strong output quality
Mixtral is a sparse Mixture of Experts (MoE) model that activates only a fraction of its full parameter set per inference call, usually two out of eight experts. This design offers significant efficiency improvements, allowing it to deliver high-quality outputs with reduced compute costs.
Its strengths lie in customer-facing applications such as dynamic assistants and search-augmented workflows. Mixtral is open-source under Apache 2.0 and is gaining traction among teams that need scalable, enterprise-grade models with manageable costs.
Ideal if you require performance at scale but want to optimise for latency and infrastructure expenditure.
Developer: OpenChat Community
Parameter size: 8B
Use cases: Instruction following, conversational agents, internal knowledge bots
Licence: Apache 2.0
Best for: Teams building aligned, open, high-performance chat models without vendor lock-in
OpenChat 3.6 is the latest version of the OpenChat series, fine-tuned on the LLaMA 3 8B base model. It’s designed for high-quality, instruction-following chat tasks and rivals proprietary models like ChatGPT in terms of alignment, helpfulness, and multi-turn reasoning, while remaining fully open under the Apache 2.0 license.
Key features:
Ideal if you're building customer-facing virtual assistants, internal copilots or domain-specific chatbots and want a robust, open-source alternative with strong out-of-the-box alignment.
Here's a comparison table:
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Choosing the right open source LLM depends on more than just performance benchmarks. Use case, industry requirements and deployment environment all influence which model is the best fit. Below, we map top open source LLMs to practical applications across common business scenarios.
If you're building a customer support bot or an internal AI assistant, look for models trained on conversational datasets with high context windows.
For scalable content production, balance model size with deployment cost. Falcon offers superior depth, while Mistral delivers speed and agility.
Consider the programming language coverage, inference speed and model size based on your IDE or integration platform.
Open source models with permissive licences and transparent architectures are essential for compliance-heavy industries.
In academic or prototyping contexts, favour models with fast inference times and minimal system requirements.
Here's the open source LLMs decision matrix:
Selecting the right open source LLM is not just about performance—it’s about aligning the model's characteristics with your technical constraints, compliance needs, and intended use case. Whether you’re evaluating for scale, speed or specialisation, the following criteria will help you choose confidently.
For applications involving multi-turn dialogue, long documents or RAG pipelines, prioritise models with extended context windows and efficient attention mechanisms.
Always confirm whether your intended use, particularly in commercial products, is permitted under the model's licence terms.
Prioritise models with large, active communities if you want better documentation, model checkpoints and plugin support.
If customisation is critical, look for models with open weights, existing adapters and training examples available.
Estimate costs for inference at scale and validate whether the model architecture is supported by your stack (e.g. ONNX, Torch, TensorRT).
Once you’ve selected a model, the next step is operational deployment—turning theory into usable AI systems. Open source LLMs offer flexible deployment paths, but each comes with technical and architectural trade-offs, depending on your infrastructure and goals.
Tip: Use containerised LLM deployment with Docker and orchestration tools like Kubernetes or Ray Serve to scale flexibly across nodes.
Whether deploying on-premise or in the cloud, your AI architecture must support observability, compliance, and scale. Discover AI-driven trends in software architecture to ensure your setup aligns with best practices.
When deploying in production, adopt a zero-trust architecture, log model decisions, and build in observability from the outset.
Deploying an open source LLM involves more than downloading a model file. From initial selection to live inference, a clear implementation workflow ensures scalability, security and task alignment. Below is a streamlined, production-ready process to help guide your rollout.
Tip: Use model parallelism or tensor parallelism when deploying large models, such as Falcon 180B or LLaMA 3 (70B), on a distributed infrastructure.
Fine-tuning enhances relevance and mitigates risks such as hallucination or misalignment in high-stakes domains.
Open-source LLMs are already being deployed across various industries to power chatbots, automate compliance, and streamline internal operations. The following case studies demonstrate how teams are applying these models in production, proving their value beyond experimentation.
Organisation type: Enterprise fintech platform
Use case: Regulatory document summarisation and client query automation
Model used: LLaMA 3 (70B), fine-tuned for financial terminology
Deployment: On-premise using NVIDIA A100 clusters and LangChain integration
Outcome:
Why it worked: LLaMA 3 provided a high-context window and strong language reasoning capabilities, enabling the team to automate nuanced workflows without relying on external APIs.
Organisation type: Private healthcare provider
Use case: Summarising clinical notes and generating post-visit summaries
Model used: Mistral 7B, deployed using Hugging Face Transformers and QLoRA
Deployment: Hybrid setup with on-prem inference and cloud-based model monitoring
Outcome:
Why it worked: Mistral’s small size and strong performance enabled real-time inference with minimal latency, making it ideal for time-sensitive clinical environments.
Deploying an open source LLM is just the beginning. Sustained success depends on proactive monitoring, regular optimisation and aligning the model’s evolution with your business goals. Below are best practices to maintain performance, reliability and compliance over time.
LLMs evolve quickly—what’s efficient today may not meet demand six months from now. Build infrastructure that adapts, not just scales.
Long-term success depends on more than initial deployment—it's about continuous iteration, community engagement and internal capability building.
Open source LLMs are no longer experimental. They're ready for production. With models like LLaMA 4, Mistral Medium 3, and Mixtral, businesses now have the freedom to build powerful, cost-effective AI solutions without being locked into a single vendor.
Choosing the right model depends on your goals, constraints and infrastructure. But with the right strategy, open source can match or even exceed the performance of proprietary alternatives.
Ready to deploy your open source LLM? Contact us today to get expert guidance on your next AI project. Our team at Imaginary Cloud specialises in helping companies evaluate, fine-tune and scale AI solutions built on open models. Whether you're starting from scratch or optimising an existing deployment, we can help you move faster and smarter.
It depends on your needs. Proprietary models like GPT-4 remain the most capable overall, but open source alternatives such as Mixtral, LLaMA 4, and fine-tuned Mistral Medium 3 can outperform ChatGPT in specific tasks or offer greater customisability.
Hugging Face is the most comprehensive platform for discovering, testing, and deploying open source LLMs. It provides easy access to model cards, inference APIs, community benchmarks and datasets.
Yes, when deployed with proper evaluation and monitoring. Many open models are fine-tuned for safety and include transparency features that help reduce bias and hallucination. However, responsibility for safe deployment ultimately rests with the user.
No, not necessarily. Models like Phi-4 are optimised for CPU inference. Larger models, such as the Falcon-H1 or LLaMA 4, benefit from GPU acceleration, especially for low-latency applications.
For personal projects or experimentation, Phi-4 or Mistral Medium 3 are excellent choices. They are lightweight, easy to deploy locally, and open for commercial and non-commercial use.
As of 2025, Mistral Medium 3, OpenChat, and LLaMA 4 are leading choices for local deployment. They offer strong performance and can run on consumer-grade hardware with the right optimisations (e.g. quantisation, GGUF format, llama.cpp).
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