By the year 2025, large language models (LLMs) have become a major factor in the transformation of the information retrieval field. These models now underpin a wide array of advanced applications, including semantic search, document ranking, question answering, and AI-driven knowledge discovery. However, the rapidly expanding ecosystem of LLMs, spanning fully open-source frameworks, commercial solutions, and hybrid approaches, presents significant challenges in determining the most suitable models for developing intelligent retrieval systems.
This extensive guide thoroughly examines the top large language models for 2025, particularly from the standpoint of their use in enterprise search and information retrieval. It offers insightful guidance on navigating important factors including model performance, transparency, scalability, and integration and is aimed at technical teams, machine learning engineers, and leaders of AI products.
This blog post is a useful resource for understanding the current state of LLM, whether your objective is to build a customized retrieval-augmented generation (RAG) pipeline, improve the functionality of a search engine, or improve access to information inside complicated document collections.
Understanding LLM Licensing Models
Based on their level of openness, large language models can be broadly divided into three groups: commercial, partially open, and open source. A genuinely open source AI model must include all the parts needed to replicate and alter the system, according to the Open Source Initiative (OSI). This includes the full source code used for training and inference, the learned model parameters (such weights and optimizer states), and comprehensive documentation of the training data (including provenance, selection criteria, preparation, and access). These components must all be made accessible under licenses that have been authorized by OSI and allow for unlimited use, modification, and redistribution.
On the other hand, partially open models, also known as hybrid or restricted-access models, may make parts of their components publicly available, like model weights or inference code, but exclude other important components, like training datasets or the entire training pipeline. Despite being advertised as open, these models frequently include restrictive licenses that restrict commercial use or forbid modification. As a result, they do not meet the OSI’s open-source AI requirements and could be problematic for companies that need complete reproducibility or transparency.
Conversely, commercial models are completely closed systems. There is no insight into model weights, architecture specifics, or training data; access is only possible via proprietary APIs. Although these models often provide cutting-edge performance and easy integration, they are not transparent enough for scientific review, auditing, or fine-tuning, and they frequently include vendor dependencies and usage fees.
| Criteria | Truly Open-Source | Partially Open-Source (Open-Weight) | Closed-Source (Proprietary) |
|---|---|---|---|
| Model Weights | ✅ Available | ✅ Available | ❌ Not available |
| Training Data | ✅ Fully disclosed | ❌ Not disclosed | ❌ Not disclosed |
| Training Code | ✅ Public | ❌ Partially or not disclosed | ❌ Not available |
| Inference Code | ✅ Available | ✅ Available | ❌ Not available |
| Reproducibility | ✅ Fully reproducible | ❌ Not reproducible | ❌ Not reproducible |
| Commercial Use Allowed | ✅ Yes | ❌ Usually restricted | ❌ Restricted |
| Notable Examples | OLMo, K2 | LLaMA 2, Mistral, Gemma, Falcon | GPT, Claude, Gemini, Command+ |
Open Source Large Language Models
Only a handful of language models released to date truly adhere to the open source AI philosophy. Let’s see a short list of what is available at the moment:
OLMo
The Allen Institute for AI (AI2) created OLMo (Open Language Model), a huge language model that is completely open source and intended to promote repeatability and transparency in AI research. OLMo offers complete access to its training data, code, model weights, and assessment tools under the permissive Apache 2.0 license.
K2
K2 is a 65-billion-parameter large language model developed collaboratively by LLM360, Petuum, and the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI). Released under the Apache 2.0 license, K2 exemplifies a fully open source approach by providing comprehensive access to its training data, codebase, model weights, and intermediate checkpoints, thereby ensuring full reproducibility and transparency in its development process.
| Model | Company | License | What’s Shared | How Open? |
|---|---|---|---|---|
| OLMo | AI2 (Allen Institute for AI) | Apache 2.0 | Code, data (fully described), training pipeline, weights, documentation | ✅ Fully open source, OSI-compliant |
| K2 | LLM360 | Apache 2.0 | Code, training and validation data (referenced), weights, blog reports | ✅ Fully open source, OSI-compliant |
Domain Specific Open Source Language Models
Molformer
Developed by IBM, MolFormer is a domain-specific large language model tailored for computational chemistry and molecular representation tasks. It stands out as one of the rare fully open-source AI systems that adheres to OSI standards, offering full transparency and reusability.
BioGPT
BioGPT, developed by Microsoft, is a large language model pre-trained specifically on extensive biomedical literature. It provides access to all components necessary to inspect, modify, and reuse the model under an OSI-approved license.
Partially Open Source Large Language Models
Although more and more big language models have been published under the terms “open” or “open source” in recent years, the majority do not satisfy the standards set by the Open Source Initiative (OSI). Usually, these partially open-source models (also known as open-weight models) make their model weights and inference code available, but they leave out crucial components like the entire training dataset, preprocessing scripts, training setups, and intermediate checkpoints. The models cannot be replicated, audited, or significantly altered without this information, which runs counter to the fundamental principles of open-source software development.
This selective release strategy often serves commercial or strategic purposes.
Organizations can maintain control over the model’s deployment and subsequent use while gaining the recognition and goodwill that come with open-source branding by publishing model weights under non-commercial or research-only licenses. Full transparency is further hindered by the fact that training datasets are frequently either proprietary, sourced from copyrighted sources, or generated under dubious legal circumstances. Therefore, even though these models seem more open than completely closed commercial systems, they nonetheless restrict scientific reproducibility, collaboration, and scrutiny.
Llama
LLaMA (Large Language Model Meta AI) is Meta‘s flagship family of large language models, first introduced in 2023 and now in its fourth generation. LLaMA represents a high-performance, partially open model that shares weights and some tools but falls far short of open-source standards. It should be understood as a commercial research release—not an open model in the OSI-compliant sense.
Deepseek
DeepSeek models represent a technically impressive and cost-efficient alternative to established LLMs, but their openness is mostly superficial. The lack of dataset transparency, training code, and clear reproducibility mechanisms places them firmly in the category of open-weight but not open-source models.
Mistral
Mistral, a French AI startup, is a technically refined and efficient open-weight model family that has made significant contributions to democratizing access to performant LLMs in Europe. However, the lack of training data, code, and full transparency places Mistral in the category of partially open-source models—available to use and fine-tune, but not fully inspect or reproduce.
Qwen
Qwen, developed by Alibaba Cloud, offers a technically competent and versatile model with strong multilingual and task-oriented features. However, due to the absence of source code and training data, Qwen should be considered a partially open model—publicly accessible for use and fine-tuning, but not open source by OSI standards or reproducibility best practices.
Gemma
Gemma, released by Google, positions itself as a lightweight, efficient alternative to the company’s flagship Gemini models. Gemma provides open weights and solid usability, particularly for lightweight or resource-constrained applications. However, due to the lack of data transparency, training code, and open licensing, it falls under the category of partially open-source models, rather than a truly open or reproducible system.
Falcon
Falcon, developed by the Technology Innovation Institute (TII) in the UAE, delivers strong performance and public weights, but the combination of a restrictive custom license and incomplete training disclosure makes it a partially open-source LLM rather than a fully open model.
| Model | Company | License | What’s Shared | How Open? |
|---|---|---|---|---|
| LLaMA | Meta AI | Custom (LLaMA Community License) | Pretrained weights, some inference code, vague dataset references | 🔒 Not OSI-compliant. Example of open-washing—minimal transparency. |
| DeepSeek | DeepSeek | MIT & custom licenses | Weights, limited code, benchmarks | ⚠️ Partial. Lacks full data/code. Initial open claims now toned down. |
| Mistral | Mistral AI | Apache 2.0 & custom | Weights, minimal inference code | ⚠️ Partial. Good accessibility, but training data/code not shared. |
| Qwen | Alibaba Cloud | Apache 2.0 / Custom (varies) | Pretrained weights | ⚠️ Partial. Lacks transparency on data and full codebase. |
| Gemma | Custom license | Weights, limited documentation | ⚠️ Partial. No full training code, vague on data sources. | |
| Falcon | TII (UAE) | Custom license | Weights, some training data | ⚠️ Partial. Not OSI-compliant despite open-weight release. |
Domain Specific partially open source Language Models
Nucleotide Transformers
Developed by InstaDeep Research in collaboration with NVIDIA and Technical University of Munich (TUM), the Nucleotide Transformers are specialized models designed for DNA sequence analysis. This model offers open access to weights and inference tools but with restrictions on commercial use, placing them in a partially open-source category.
BioMedLM
BioMedLM, developed by the Stanford Center for Research on Foundation Models (CRFM) in collaboration with MosaicML, is a domain-specific large language model trained exclusively on biomedical abstracts and papers, particularly from The Pile. While BioMedLM is accessible and useful for research, it does not qualify as a fully open source model by OSI standards, due to its restrictive license and lack of full reproducibility materials.
MedAlpaca
MedAlpaca, developed by researchers affiliated with the Stanford Center for Research on Foundation Models (CRFM) and MosaicML, is a specialized family of large language models focused on the medical domain.
Although the fine-tuning work itself is open source, the resulting model remains dependent on a non-open source foundation (Meta’s LLaMA), which disqualifies it from being a truly open source model by OSI standards.
BioMistral
BioMistral is a family of medical large language models developed through continued pretraining of Mistral 7B Instruct, an open-weight model from Mistral AI.
BioMistral is partially open. It extends an open-weight model and shares some resources under a permissive license, but the training data and process lack full transparency and reproducibility.
| Model | Company | License | What’s Shared | How Open? |
|---|---|---|---|---|
| Nucleotide Transformer | InstaDeep Research (with Nvidia & TUM) | CC BY-NC-SA 4.0 | Pretrained weights, inference code, usage instructions | ⚠️ Partially open. No commercial use allowed; not OSI-compliant. |
| BioMedLM | Stanford CRFM & MosaicML | BigScience RAIL License v1.0 | Weights, code, datasets | ⚠️ Partially open. RAIL licenses impose ethical restrictions; not OSI-compliant. |
| MedAlpaca | Stanford CRFM & MosaicML | GPL & Creative Commons | Weights, fine-tuning code, dataset | ⚠️ Relies on LLaMA weights (not open); open contributions are partial. |
| BioMistral | Mistral AI | Apache 2.0 | Weights, partial dataset details, benchmarks | ⚠️ Misuse of "open source" term. Relies on Mistral weights, not fully reproducible. |
Commercial Large Language Models
The AI market is dominated by commercial large language models (LLMs), which provide strong, highly optimized solutions for both consumer and business applications. These commercial LLMs, in contrast to open source models, are usually proprietary, with software-as-a-service platforms or APIs controlling access. Their developers make significant investments in training on large datasets and fine-tuning to provide cutting-edge performance, frequently incorporating sophisticated safety and compliance features. The underlying architectures, training data, and learning weights of these models are never completely released, which restricts openness and reproducibility even if they offer strong and scalable AI capabilities. Easy connection, dependable support, and continuous enhancements are advantageous to developers and businesses, but at the expense of less customization options and more usage costs.
OpenAI GPT models, "o" models and text-to-vector models
ChatGPT, the flagship product of OpenAI, offers an intuitive user interface for interacting with sophisticated multi-modal large language models (LLMs). GPT models, which concentrate on text completion and produce human-like language rapidly and efficiently, and reasoning models, which work more slowly but are excellent at solving complicated tasks by using multi-step chains of thinking, are the two primary model types that OpenAI offers. Furthermore, OpenAI creates strong text-to-vector models, albeit it seems that recent progress in this field has stagnated. Programmatic access to these models is made possible by OpenAI’s APIs, which also include sophisticated features like function calling (tool use) and structured output, enabling developers to easily incorporate LLM capabilities into their applications.
Google - Gemini
Google’s Gemini models initially faced mixed reception as early attempts to rival OpenAI. The latest iterations of Gemini concentrate on web and front-end code development, alongside enhanced reasoning capabilities. In addition to language understanding, Google is actively developing text-to-vector models within the Gemini ecosystem. Like OpenAI, Gemini offers APIs that facilitate programmatic interaction with their LLMs, including support for function calling (tool usage) and structured output, enabling developers to build sophisticated applications leveraging Gemini’s capabilities.
Cohere - Command
Cohere’s general-purpose large language models tend to be smaller and deliver lower quality compared to larger competitors. However, their primary focus lies in optimizing performance and cost-efficiency, with strong support for multiple languages. Text-to-vector embedding and reranking models remain central to Cohere’s offerings, with their latest embedding models supporting exceptionally long contexts of up to 128k tokens, with potential for future expansion. Additionally, Cohere leads the Aya project, an open science initiative aimed at promoting more inclusive and multilingual AI development.
Anthropic - Claude
Anthropic has developed a unified approach combining fast-responding and slow-thinking models tailored to different use cases, reflecting a growing industry trend. The Claude family of models offers strong performance comparable to competitors, with a particular emphasis on coding assistance.
Conclusion
Large language models currently come in a wide range of forms, including entirely commercial products, partially open ventures, and truly open source projects. Even though there are still not many totally open source models, they are essential for encouraging openness, cooperation, and creativity. By preserving private elements while sharing essential components, partially open models allow for more access without completely giving up control. Commercial models, on the other hand, are more prevalent in terms of scale, performance, and integration possibilities, but they frequently put economic interests before of transparency. Anyone involved in AI creation, deployment, or research must be aware of these differences since each one has special advantages and disadvantages that will influence how AI technology develops in the future.
To assist anyone searching for the finest large language model suited to their project’s requirements, we will update this guide frequently.
Need Help with this topic?
Need Help With This Topic?
If you’re struggling with large language models for your search system, don’t worry – we’re here to help!
Our team offers expert services and training to help you optimize search engine and get the most out of your system. Contact us today to learn more!
