Beyond ChatGPT: Top Open-Source LLMs You Should Know

In just a few years, Large Language Models (LLMs) have evolved from task-specific NLP tools to powerful, general-purpose systems, such as ChatGPT, that can outperform the USMLE passing score by over 20 points.

While proprietary leaders such as ChatGPT, Gemini, and Claude dominate the spotlight, they come with major limitations like closed architectures, high costs, and opaque training processes. These constraints have sparked a surge of interest in open-source alternatives.

The turning point arrived in 2023 with Meta’s release of LLaMA, which ignited a wave of community-driven innovation and proved that open models could rival GPT-4-level performance.

By 2024, model families like GPT, LLaMA, and PaLM had diversified the ecosystem. Today, open-source architectures deliver impressive results through transparency, fine-tuning flexibility, and collective optimization. Models such as Mistral’s Mixture-of-Experts have shown that smaller systems can achieve remarkable efficiency and impact.

For data professionals, open models offer practical advantages such as customization, reproducibility, and cost-effective deployment across domains like coding, research, and healthcare.

As the field shifts from massive, centralized LLMs to specialized, multimodal, and efficient designs, open innovation is becoming the cornerstone of progress.

This article explores the leading open-source LLMs of 2025 and beyond, their strengths, and how they’re redefining the AI landscape.

The Rise of Open-Source Powerhouses: Key LLMs in 2025

Currently, there are several LLMs out there, each pushing the boundaries of what open-source AI can achieve.

These models are no longer just research experiments; they are shaping industries, powering real-world applications, and setting new benchmarks in performance and accessibility.

Below is a closer look at the top open-source LLMs dominating the landscape in 2025.

1. Llama 3.x and Llama 4 (Meta AI)

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The Meta’s Llama series is by far the strongest candidate for open source LLMs and is considered to hold the strongest commitment to open science in AI. After the success of Llama 2 in 2023, Meta advanced the field with Llama 3.x in 2024.

They scaled its training dataset to 15 trillion tokens (seven times larger than Llama 2) and expanded the context window from 4K to 128K tokens for handling complex, long-form tasks.

The flagship Llama 3.1 405B, a 405 billion parameter model released in July 2024, became the largest open-source LLM at the time of its release, and quickly gained an edge over its rivals like GPT-4 and Claude 3.5 on reasoning benchmarks such as ARC (96.9) and GSM8K (96.8).

The key features of Llama 3.x included enhanced reasoning, improved instruction following, and stronger multilingual support, fixing early limitations where most of the training data was in English.

It also placed heavy emphasis on safety with tools like Llama Guard 3, Code Shield, and Prompt Guard, making it more reliable for enterprise use cases, with Meta noting that (Introducing Meta Llama 3).

As Meta notes,

We’re committed to the continued growth and development of an open AI ecosystem for releasing our models responsibly.

The next key model is Llama 4, which was released in 2025. With this model, Meta shifted toward multimodality (text + image) and Mixture-of-Experts (MoE) architectures.

Early models like Llama 4 Scout (109B, 17B active with context windows reaching 10 million tokens) and Llama 4 Maverick (400B, 17B active) balanced efficiency with scale.

The upcoming model, Behemoth (2T parameters, 288B active), aims to push towards trillion-scale open-source AI and seems to outperform competitors like GPT-4.5 and Gemini 2.0 Pro on STEM benchmarks (such as MATH-500 and GPQA).

The applications of these models are diverse and range from chatbots, legal and medical summarization, to advanced code generation, research, and finetuned domain-specific solutions in industries from healthcare to finance.

Key advantage of Lama is that it is developer-friendly, scalable, and backed by a vibrant community that continues to enrich the open-source LLM ecosystem.

2. Mistral AI Models

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Founded in 2023, Mistral AI rapidly became Europe’s most valuable AI startup. It is built around efficiency and performance-per-parameter. Its first release, Mistral 7B, showed that small models could rival much larger systems.

The real breakthrough, however, came with Mixtral 8x7B (a sparse Mixture-of-Experts (MoE) model), where only two of eight experts activate per query, delivering high-quality results at a fraction of the computational cost. This made Mistral models uniquely deployable even on consumer GPUs, which made it possible to expand its access beyond elite labs.

The Mistral-Large-2407 (123B parameters) is another key model that established itself as one of the most capable open models and achieved 84% on MMLU, excelling in math, reasoning, and code generation while maintaining its efficiency.

It introduced a 128K token context window for long conversations and native function calling with JSON outputs. These features enabled agentic applications.

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As far as benchmarks are concerned, it ranked second only to GPT-4o in HumanEval for coding accuracy and showed strong performance in multilingual reasoning tasks.

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For software development, Codestral (22B) is offered by Mistral as its specialist coding model, which supports 80+ programming languages. It outperformed larger models in fill-in-the-middle tasks, achieving the highest performance in Python, Bash, Java, and PHP.

Beyond text, Pixtral 12B marked Mistral’s entry into multimodality. Combining a 12B multimodal decoder with a 400M vision encoder, it supports text+image reasoning with a 128K context window.

On benchmarks like MathVista, DocQA, and VQAv2, Pixtral outperformed Llama-3.2 11B and Qwen2-VL 7B, making it one of the most competitive open multimodal models.

The success of Mistral models is because they deliver high performance at lower compute costs, making them practical across research, industry, and consumer applications. With open weights, strong community adoption, and integration on IBM WatsonX and Amazon Bedrock, they represent one of the most flexible and widely adopted open-source LLM families of 2025.

3. Deepseek Models

Perhaps the most disruptive and talked-about LLM model in recent times is DeepSeek. Founded in Hangzhou in 2023, DeepSeek AI quickly shook up the LLM market by prioritizing reasoning-first models that rival proprietary giants like OpenAI.

Its breakthrough came in January 2025 with the release of DeepSeek-R1, which is a reasoning-focused model built at a cost of under $6 million, which may look a lot to you, but to put it in context, is just a fraction of what competitors spend.

Within days, the DeepSeek app overtook ChatGPT on Apple’s App Store, which caused a sell-off that wiped $600 billion from Nvidia’s market cap, with the Nasdaq dropping 3.4%. This “Sputnik moment” for AI signaled that open-source challengers could match or even surpass proprietary models.

The next key DeepSeek you need to know about is its flagship V3 model, which uses a Mixture-of-Experts architecture with 671B parameters (37B active per query), supporting 128K token contexts and innovations like Multi-Head Latent Attention and Multi-Token Prediction for efficiency.

The R1 series is particularly great as it’s better refined (with reinforcement learning), and excels at step-by-step reasoning (particularly in math, coding, and logic).

Let’s understand this with some benchmarks.

These models power diverse applications ranging from scientific research, technical documentation, to data analysis and education. In China, hospitals employ DeepSeek for medical imaging diagnostics, while automakers like BYD integrate it into ADAS systems for safer driving.

Its coding-focused spin-offs (e.g., DeepSeek-Coder and Coder-V2) support 388 programming languages, matching GPT-4 Turbo in enterprise code tasks.

To understand DeepSeek’s rise, you need to focus on its cost efficiency, reasoning transparency, and open access, and with top-tier reasoning performance, affordable inference (as low as $0.55 per million tokens input), and rapid adoption, DeepSeek today represents a turning point in open AI innovation.

4. Google’s Gemma 2.x

Google’s Gemma 2.x represents its open-source push in the LLM ecosystem, derived from the Gemini research line. Released in 9B and 27B parameter versions, with both base and instruction-tuned models, Gemma 2.x was trained on 13 trillion tokens of web data, code, and math, nearly doubling the training corpus of its predecessor.

These models have a heavy emphasis on efficiency, accessibility, and transparency, which is achieved by offering open weights under a permissive license. All of this has enabled finetuning and commercial deployment.

Positioned as a lighter version of Google’s proprietary Gemini models, Gemma 2.x is designed for developers who are seeking powerful reasoning in a deployable and resource-conscious form.

Let’s now look at some key technical features of Gemma 2.x. Firstly, it integrates several architectural innovations, which include Sliding window attention (which balances local and global attention, thereby enabling long-context comprehension of up to 8K tokens) and logit soft-capping (which stabilizes training).

The 9B version uses knowledge distillation from larger teacher models, while the 27B version was trained from scratch to boost performance across reasoning and summarization tasks. Talking about benchmarks, the 27B achieved 74 on GSM8K (math word problems) and 71.4 on ARC-c, which is better than models like Qwen-1.5 (32B).

The crucial thing to know about Gemma 2.x is that it is lightweight and hardware-friendly. Let’s consider a 27B instruction-tuned model, which can run in 4-bit quantization on 18GB consumer GPUs, making it practical for on-device and edge computing scenarios.

Its strengths go beyond this and extend to semantic search, question answering, instruction following, and multimodal tasks, making it a great model for applications like interactive storytelling, hybrid creative workflows (text + images), and educational tutoring.

Gemma 2.x Flash has gained traction due to efficiency, openness, and Google’s ecosystem integration. With seamless deployment options through Hugging Face and Google Cloud, it delivers enterprise-level reliability while staying accessible for individual developers.

Thus, Gemma can strike a delicate balance between cutting-edge research and practical usability.

5. Falcon Models

Now, let’s look at a relatively less talked-about LLM: the Falcon models. Developed by the Technology Innovation Institute (TII) in the UAE, these models have become some of the most impactful open-source LLMs lately.

The initial models were Falcon 7B and 40B, which eventually led up to the massive Falcon 180B, and more recently, the Falcon 3 series, including Falcon 3–7B Instruct. These models showcase TII’s focus on high-performance models with large parameters and efficient, lightweight architectures.

Thanks to licensing under Apache 2.0, Falcon can have broad adoption and can be easily used in commercial applications because it is free from restrictive commercial constraints.

The key features of the Falcon family of LLMs are its scale and technical innovation. Falcon 40B, for instance, was trained on one trillion (1000B) tokens of the RefinedWeb dataset, while Falcon 180B pushed this further with 3.5 trillion tokens, making it the largest popular model with openly documented pretraining run and openly accessible weights.

Architectural advances like rotary positional embeddings, FlashAttention, and multi-query attention have improved both scalability and inference speed of the Falcon models.

Apart from these models, the Falcon 3–7B Instruct variant emphasizes efficiency by supporting long-context windows up to 131K tokens, integrating grouped query attention (for reduced memory use), and can be quantized for deployment in low-resource environments.

Another key model is Falcon 180B, which is available for one-click deployment on AWS SageMaker JumpStart, through which enterprises can easily and reliably scale production workloads and simplify operations.

By combining massive-scale models with efficient instruction versions, Falcon has managed to strike a delicate balance between cutting-edge performance and practical accessibility, thereby cementing its place as a cornerstone in the open-source LLM ecosystem.

And the hype around Falcon’s and its popularity is backed by results, too. For instance, these models consistently topped the OpenLLM Leaderboard and surpassed their peers (e.g., LLaMA, StableLM, MPT, RedPajama, etc).

Another reason for their popularity is their wide range of real-world use cases that span from enterprise solutions and content generation to language translation and advanced scientific research.

6. Other Notable LLMs

Major Open-Source LLMs:

i. BLOOM 2 (BigScience): Open-Access and Ethical Multilingual AI

The discussion on top open source LLMs cannot be complete without talking about BLOOM 2. It represents the next phase of the BigScience initiative, which was created by a collaboration of over 1,200 researchers worldwide (39 countries to be precise).

Unlike proprietary models, BLOOM places heavy emphasis on open access, with its full model weights made freely available through Hugging Face. This indicates the strong commitment the project has towards transparency and inclusivity (Getting Started with Bloom).

With 176 billion parameters, BLOOM was trained on the ROOTS corpus, covering 46 natural languages and 13 programming languages, making it one of the most diverse multilingual models available.

Due to such training, the model is able to perform numerous tasks like translation, summarization, question answering, and even code generation.

The project has also explored architectural innovations like ALiBi positional embeddings, embedding layer norm, and robust finetuning strategies to enhance zero-shot performance.

BLOOM’s popularity is also due to its development following an Ethical Charter to mitigate bias, promote inclusivity, and encourage responsible AI use. This ethical grounding, in addition to global collaboration, has made BLOOM 2 a landmark in responsible, open-source AI research.

ii. Phi-3 / Phi-4 (Microsoft): Compact Models with High Impact

The next key model among the top open source LLMs is the “tiny but mighty” Microsoft’s Phi series, which demonstrates how small language models (SLMs) can deliver enterprise-grade performance while remaining resource-efficient.

Introduced at Microsoft Build 2024, the Phi-3 family includes Phi-3-mini (3.8B parameters), Phi-3-small (7B), Phi-3-medium (14B), and the multimodal Phi-3-vision, capable of processing both text and images with context windows up to 128K tokens.

These models are built on curated datasets that combine high-quality public content, synthetic data, and educational material, with Phi-3 excelling in reasoning, coding, math, and language understanding, often outperforming or coming very close to larger models like GPT-3.5.

Phi-3-vision, which, as the name suggests, is a visual model, extends this capability to OCR, Table Parsing, Reading Comprehension on Scanned Documents, Image captioning, chart interpretation, multimodal tasks, etc, establishing its versatility.

In addition, training enhancements such as Direct Preference Optimization (DPO), Supervised finetuning (SFT), etc, further ensure outputs are safe and reliable.

The latest Phi-4 series has expanded this foundation with advanced reasoning capabilities and multimodal processing across text, vision, and speech, optimized for edge deployments and environments with limited computing power and network access.

iii. Qwen2 / Qwen2.5 (Alibaba)

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Alibaba’s Qwen2 and Qwen2.5 models are the last models in this list of open source LLMs. These models have highlighted the company’s growing influence in the open-source LLM ecosystem.

Qwen2 introduced models ranging from 0.5B to 72B parameters, trained on over 27 languages, with strong improvements in coding and mathematics.

Even the smaller 1.5B model has demonstrated strong reasoning in math and logic, while the larger Qwen2 72B outperformed Meta’s Llama 3 70B and Mixtral 8x22B across multiple benchmarks such as MMLU, TheoremQA, and HumanEval.

Qwen2.5 built on this foundation with the release of Qwen2.5-Max, a Mixture-of-Experts (MoE) architecture trained on more than 20 trillion tokens, allowing for sparse activation of “experts,” enabling efficiency while scaling parameter size.

In addition to this, due to the 32K token context window, Qwen2.5-Max excels in long-form reasoning, large document analysis, and advanced coding. Benchmarks also have revealed that it surpasses DeepSeek V3, GPT-4o, and Claude-3.5-Sonnet in areas like LiveBench and Arena-Hard, and leads in GPQA-Diamond for complex reasoning tasks.

Now it’s important to note that while some of Qwen2.5’s top-tier variants remain proprietary, Alibaba has released open-weight versions like Qwen2.5–72B-Instruct, which topped the OpenCompass leaderboard.

In total, Alibaba has made over 100 Qwen2.5 multimodal models openly available, strengthening its commitment to accessible AI development. These models cover text, image, and even video generation, offering robust multilingual support across 29+ languages.

Thus, by creating a balance of competitive performance and open-source accessibility, Qwen has successfully become one of the most versatile families of models in the world of open-source LLMs.

Key Trends Shaping Open-Source LLMs in 2025

Before concluding the discussion on leading open-source LLMs, it’s important to look at the key trends shaping the direction of the landscape in 2025.

  • Efficiency and Accessibility

In 2025, efficiency is at the core of open-source LLM innovation, and the consensus is forming around a future driven by smaller, yet smarter models designed to run efficiently on consumer GPUs and even edge devices.

This democratization is extremely critical as it will make advanced AI available beyond high-end data centers, thereby reducing energy demands while at the same time enabling real-time, low-latency applications and providing a better ROI to businesses, which is important given 51% see positive ROI when using open source tools.

Techniques like the Stable and Transferable Mixture-of-Experts (ST-MoE) optimize efficiency by activating only a subset of experts for each input, thereby reducing FLOPs and computational costs without sacrificing model quality.

Additionally, quantization and pruning are widely adopted to shrink model sizes while maintaining output quality, extending deployment to resource-constrained environments like mobile devices and IoT systems.

  • Specialization and Customization

General-purpose models are no longer sufficient for industries with unique needs. The trend toward domain-specific LLMs is accelerating, with models like BloombergGPT for finance, Med-PaLM for healthcare, and ChatLAW for legal contexts.

These models leverage domain-optimized data to deliver more accurate, context-aware results. At the same time, finetuning practices are maturing, allowing enterprises to customize open-source base models with proprietary datasets while ensuring data privacy concerns are managed.

This allows businesses to create tailored solutions that enhance compliance and performance, and empower businesses to treat LLMs not as generic assistants but as specialized partners for critical workflows.

These company and domain-specialized LLMs are key to making them disruptive across several applications.

  • Multimodality

Another defining trend is the rise of multimodal LLMs that go beyond text and process multiple modalities, including images, audio, and even video.

Unlike early multimodal systems that stitched together separate models, 2025 is witnessing natively multimodal architectures that are developed end-to-end, such as NExT-GPT, CoDi-2, ModaVerse, etc., that are capable of unified reasoning across multiple data types.

These capabilities are opening up new use cases (due to enhanced contextual understanding) such as analyzing medical images like X-rays, enhancing product searches for e-commerce, or interpreting complex video scenes for security.

As these models become more widely available in open-source form, they are set to redefine how humans interact with AI across diverse sensory channels.

  • Safety, Alignment, and Transparency

A major trend is the ethics of AI, with ethical AI moving from discussions in academic and intellectual circles to becoming a necessity for industry leaders and policymakers.

The EU AI Act of 2024 is already shaping this development by aiming to ensure individuals’ safety, security, and fundamental rights through the implementation of stricter safety, security, transparency, and fairness in AI systems.

The great thing, however, is that open-source LLMs are readily embracing responsible AI practices like reinforcement learning from human feedback (RLHF), fairness-aware training, and third-party audits.

The key to a successful shift is transparency, and with open access to training data, model weights, and code, ensuring that biases are identified and corrected publicly is becoming possible.

Thus, a combination of open source, open data, open access, and open science makes it possible to address the issue of safety transparency and explainability, making open source models a more trustworthy alternative to proprietary closed-source black-box models.

  • Autonomous Agents and Tool Use

LLMs are rapidly evolving into agentic systems that are capable of decision-making and independent action. Native function calling allows models to interact with external APIs, databases, and tools.

This bridges the gap between language processing and real-world execution. These agentic LLMs are already being deployed for automated workflows, research assistance, and customer support, and the trend of autonomous agents is set to become more popular, with analysts predicting that by 2028, nearly one-third of enterprise applications will embed agentic AI, with 15% of daily work decisions made autonomously.

  • Community-Driven Innovation and Collaboration

Lastly, the major trend is regarding the open-source community, which remains the backbone of LLM progress. Platforms like Hugging Face provide a great shared space for libraries, datasets, evaluation benchmarks, and model repositories.

This is not only fueling rapid iteration but is also conducive to collaborative efforts that eventually help in accelerating breakthroughs in alignment, efficiency, and evaluation at a scale no single company can match.

With numerous developers contributing globally to various open-source LLM projects, the progress of such projects is not just keeping pace but is outperforming closed systems in speed, adaptability, and, most importantly, inclusivity.

This collective innovation ensures that open LLMs are likely to remain both accessible and cutting-edge.

Concluding Thoughts

The landscape of open-source LLMs in 2025 is defined by innovation and democratization. Models like Mistral, Gemma, Falcon, BLOOM, Phi, and Qwen have showcased how efficiency, specialization, and multimodality are fueling adoption across industries, while smaller models, sparse expert architectures, and fine-tuning are driving accessibility, open-source AI to reach more users and domains.

Despite the progress, safety, alignment, and transparency remain essential aspects that need to be addressed while open frameworks continue to foster trust through scrutiny and collaboration. All in all, open-source LLMs deliver both performance and inclusivity and will continue to pave the way for a more transparent and democratized AI future.

For additional insights on the changing landscape of Data Science and AI, follow our official AnalytixLabs Blog.

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