
Large Language Models (LLMs) are a groundbreaking development in the field of artificial intelligence. These powerful AI systems, trained on vast amounts of text data, have the ability to understand, generate, and interact with human language with remarkable accuracy and fluency.
LLMs are revolutionizing various domains, from content creation and language translation to code generation and sentiment analysis.
The importance of open-source LLMs in the AI landscape cannot be overstated. Open-source models democratize access to cutting-edge language technologies, fostering innovation, collaboration, and transparency within the AI community. By making the underlying architecture and training data publicly available, open-source LLMs enable researchers and developers to study, modify, and build upon these models, leading to rapid advancements and diverse applications.
What are Large Language Models (LLMs)?

Large Language Models are a type of artificial intelligence algorithm that utilizes deep learning techniques and massive datasets to understand, summarize, generate, and predict human language. LLMs are trained on enormous corpora of text data, often comprising billions of words, allowing them to capture intricate patterns, semantics, and contextual relationships within the language.
Open-source LLMs differ from proprietary models in several key aspects. While proprietary LLMs, such as those developed by major tech companies, offer impressive performance, they often come with limitations in terms of control, customization, and transparency.
Open-source models, on the other hand, provide users with full access to the underlying architecture, weights, and training data, enabling fine-tuning, modification, and deployment without reliance on external APIs or services. This flexibility and transparency make open-source LLMs a compelling choice for researchers, developers, and organizations seeking to harness the power of language AI while maintaining control over their implementations.
Explore the Top 10 Open-Source Language Models of 2025
Model Name | Main Feature |
---|---|
Mixtral-8x7b-Instruct-v0.1 | Sparse mixture of experts (SMoE) architecture with 8 experts per MLP, enabling 6x faster inference than Llama 2 70B |
Tulu-2-DPO-70B | Trained on a mix of public, synthetic and human datasets using Direct Preference Optimization (DPO) |
GPT-NeoX-20B | 20B parameter autoregressive model trained on the Pile dataset, strong few-shot reasoning capabilities |
LLaMA 2 | Improved instruction following, longer context length, and open-source release from Meta AI |
OPT-175B | Large open-source model from Meta AI trained on publicly available data, strong zero-shot performance |
Falcon 40B | Instruct-tuned dense model with strong instruction following and reasoning abilities |
XGen-7B | Efficient model that matches GPT-3 Curie performance with 10x fewer parameters |
Vicuna 13-B | Open-source chatbot trained via RLHF on user-shared conversations, strong conversational and instruction following abilities |
BLOOM | 176B parameter open multilingual model supporting 46 natural languages and 13 programming languages |
BERT | Pioneering bidirectional Transformer model that set a new standard for language understanding tasks when open-sourced |
1. Mixtral-8x7b-Instruct-v0.1
Mixtral 8x7B, developed by Mistral AI, is a cutting-edge open-source large language model (LLM) that outperforms industry giants like Llama 2 70B and GPT-3.5. Leveraging a sparse mixture of experts (SMoE) architecture, Mixtral 8x7B boasts 46.7B parameters while only utilizing 12.9B per token, ensuring unparalleled efficiency.
Licensed under the permissive Apache 2.0, this multilingual powerhouse excels in code generation, handles 32k token contexts, and seamlessly switches between English, French, Italian, German, and Spanish. With its instruction-tuned variant achieving an impressive 8.3 score on MT-Bench, Mixtral 8x7B sets a new standard for open-source LLMs, democratizing access to state-of-the-art language AI technology.
Key Features of Mixtral 8x7B:
- Multilingual support for English, French, Italian, German, and Spanish.
- Strong performance in code generation tasks.
- Designed for instruction-following and open-ended generation.
- Licensed under Apache 2.0 for open-source use.
- Seamless integration with OpenAI APIs and AWS ecosystem.
Ideal Use Cases:
Mixtral-8x7b-Instruct-v0.1 is well-suited for a wide range of natural language processing tasks that demand high performance, efficiency, and multilingual support. Its instruction-following capabilities make it ideal for open-ended question answering, task automation, and conversational AI applications.
Performance Benchmarks:
While comprehensive benchmarks are still emerging, initial evaluations suggest that Mixtral-8x7b-Instruct-v0.1 delivers competitive performance on various NLP tasks compared to GPT-3.5-turbo. For instance, on the GSM-8K 5-shot benchmark, it achieved 53.6% accuracy, slightly outperforming GPT-3.5-turbo at 52.2%. On the MT Bench for instruction models, it scored 8.30, on par with GPT-3.5-turbo's 8.32.
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2. Tulu-2-DPO-70B
Tulu-2-DPO-70B, developed by AllenAI, stands as the flagship model in the cutting-edge Tulu V2 series of open-source large language models (LLMs). Boasting 70 billion parameters, this powerhouse is a fine-tuned version of the renowned Llama 2, meticulously trained using Direct Preference Optimization (DPO) on a diverse mix of publicly available, synthetic, and human-curated datasets.
Licensed under AI2's ImpACT Low-risk license, this model sets a new standard for open-source language AI, offering unparalleled performance, alignment, and adaptability for a wide range of natural language processing tasks.
Key Features of Tulu-2-DPO-70B:
- Matches or exceeds GPT-3.5-turbo-0301 performance on several benchmarks.
- Trained to follow instructions and align with desired tones.
- Supports English language.
- Released with checkpoints, data, training and evaluation code.
- Quantized versions available for more efficient inference.
Ideal Use Cases:
Tulu-2-DPO-70B is well-suited for open-ended generation tasks that require high-quality instruction following and sentiment control. Its strong performance on benchmarks like MT-Bench and AlpacaEval suggest it can handle a wide variety of language tasks including summarization, question answering, and open-ended dialogue. As one of the largest open models with DPO training, it provides a powerful foundation for applications that require GPT-3.5 level language understanding and generation but cannot use proprietary models. However, developers should be cautious about potential misuse as the model has not been fully aligned for safety.
Performance Benchmarks:
On the MT-Bench benchmark, Tulu-2-DPO-70B achieves a score of 7.89, the highest among open models at the time of release. It also reaches a 95.1% win rate on the AlpacaEval benchmark, significantly outperforming GPT-3.5-turbo-0314 (89.4%) and coming close to GPT-4.
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3. GPT-NeoX-20B
GPT-NeoX-20B, developed by the EleutherAI collective, stands as a pioneering open-source large language model (LLM) with 20 billion parameters. Trained on the Pile dataset using sparse transformer architectures, this model delivers exceptional performance across a wide range of natural language processing tasks. GPT-NeoX-20B excels in content generation, question answering, and code understanding, making it an ideal choice for medium to large businesses with advanced AI needs.
Licensed under the permissive Apache 2.0 license, this model democratizes access to cutting-edge language AI capabilities, fostering innovation and transparency within the open-source community. With its impressive performance and scalability, GPT-NeoX-20B paves the way for the future of open-source LLMs.
Key Features of GPT-NeoX-20B:
- Uses rotary positional embeddings instead of learned embeddings.
- Computes attention and feed-forward layers in parallel for faster inference.
- Dense architecture with no sparse layers.
- Open-source model weights and code available on GitHub.
Ideal use cases:
GPT-NeoX-20B is well-suited for applications requiring strong language understanding, reasoning, and knowledge capabilities, such as question-answering systems, code generation, scientific writing assistance, and solving complex mathematical problems. Its open-source nature also makes it valuable for researchers exploring large language model safety, interpretability, and customization.
Performance benchmarks:
On popular NLP benchmarks like LAMBADA and WinoGrande, GPT-NeoX-20B performs comparably to GPT-3's Curie model. However, it excels on knowledge-intensive tasks like the MATH dataset, outperforming even GPT-3 175B. Its one-shot performance on HendrycksTest also demonstrates strong reasoning abilities.
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4. LLaMA 2
Llama 2, Meta AI‘s groundbreaking open-source large language model (LLM), is revolutionizing the AI landscape in 2025. As a successor to the original Llama model, Llama 2 boasts enhanced capabilities, improved safety measures, and unparalleled accessibility. With model sizes ranging from 7 billion to 70 billion parameters, Llama 2 caters to a wide array of applications while delivering top-notch performance across benchmarks in reasoning, coding, and general knowledge. What sets Llama 2 apart is its open-source nature, enabling researchers and businesses to leverage its power for both research and commercial purposes. Dive in to explore how Llama 2 is democratizing access to cutting-edge AI and paving the way for a new era of innovation.
Key Features of Llama 2:
- Optimized for dialogue use cases through supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF).
- Available in sizes from 7B to 70B parameters to suit varied computational needs.
- Incorporates ethical and safety considerations in training data and human evaluations.
- Open-source and free for commercial use (with some restrictions for very large companies).
- Outperforms other open-source chat models on most benchmarks.
Ideal use cases:
Llama 2 is a highly versatile foundational language model suited for a wide range of natural language tasks. Its dialogue optimization makes it ideal for building conversational AI assistants, chatbots, and interactive characters. Llama 2 can power engaging and informative customer support, educational tools, creative writing aids, and even interactive entertainment. Its strong reasoning and coding abilities also enable applications like knowledge retrieval, document analysis, code generation, and task automation.
Performance benchmarks:
Llama 2 demonstrates leading performance among open-source language models across various benchmarks. The 70B parameter model is competitive with models like GPT-3.5 on knowledge-intensive tasks, reaching 85% on the TriviaQA dataset. On reasoning challenges like BoolQ, Llama 2 shows major gains, with the 70B model hitting 80.2% accuracy. Even the smaller 7B model outperforms others in its size class. Llama 2 also exhibits strong few-shot learning, nearly doubling the scores of 7B models on tasks like coding and logic. While not surpassing the latest proprietary models, Llama 2 sets a new bar for open-source language model performance.
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5. OPT-175B
OPT-175B, developed by Meta AI, is a groundbreaking open-source large language model (LLM) that pushes the boundaries of what's possible in natural language processing. As an open-source alternative to OpenAI's GPT-3, OPT-175B boasts an impressive 175 billion parameters, putting it on par with the top performing models of its time. What sets OPT-175B apart is its commitment to transparency and collaboration. By making the model weights and code freely available, Meta AI has empowered researchers and developers worldwide to explore, fine-tune, and build upon this powerful tool.
This open approach fosters innovation and accelerates progress in natural language processing applications. With capabilities spanning text generation, question answering, summarization and more, OPT-175B has proven its versatility across a wide range of tasks. Its strong performance on benchmarks showcases the immense potential of open-source language models.
Key Features of OPT-175B:
- High zero-shot performance across many NLP tasks.
- Supports English, Chinese, Arabic, Spanish, Russian, and 58 other languages.
- Available model weights, code, and training data released openly.
- Efficient decoder-only transformer architecture.
- Ability to be fine-tuned on custom datasets.
Ideal Use Cases:
OPT-175B excels in general language tasks like text generation, summarization, question answering, translation, and analysis across many domains and languages. Its versatility makes it suitable for research, content creation, chatbots, language learning, and multilingual applications.
Performance Benchmarks:
On the LAMBADA language modelling benchmark, OPT-175B achieved 76.2% accuracy, outperforming GPT-3's 76.0%. On the TriviaQA reading comprehension task, it scored 80.5 F1, comparable to GPT-3's 80.6 F1. Its strong zero-shot abilities enable high performance without task-specific fine-tuning.
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6. Falcon 40B
Falcon 40B, developed by the Technology Innovation Institute (TII), stands as the epitome of open-source large language models (LLMs). Boasting an impressive 40 billion parameters, this causal decoder-only model delivers exceptional performance across a wide range of natural language processing tasks. Trained on a meticulously curated 1 trillion token dataset, Falcon 40B excels in areas such as text generation, question answering, and code understanding.
Its innovative architecture, featuring multi-query attention and FlashAttention, optimizes inference scalability and computational efficiency. Licensed under the permissive Apache 2.0 license, Falcon 40B democratizes access to cutting-edge language AI capabilities, fostering innovation and transparency within the open-source community.
Key Features of Falcon 40B:
- Efficient training using less compute than GPT-3 or Chinchilla.
- Strong few-shot learning capabilities on complex tasks.
- Supports code generation, question answering, analysis, and more.
- Available in 40B and 180B versions with the larger model being state-of-the-art.
Ideal Use Cases:
Falcon 40B shines in applications requiring strong language understanding, reasoning, and precise execution of instructions. Some ideal use cases include code generation and assistance, question answering systems, analysis and writing assistants, and multi-task AI agents for complex scenarios.
Performance Benchmarks:
On the InstructGPT benchmark, Falcon 40B achieves state-of-the-art results, outperforming GPT-3 and other large models. It also demonstrates superior few-shot learning compared to models like GPT-3 and PaLM. The 180B version sets new records on various benchmarks like TruthfulQA and StrategyQA.
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7. XGen-7B
XGen-7B, developed by Salesforce AI Research, is a pioneering open-source large language model (LLM) boasting 7 billion parameters. Trained on an unprecedented 1.5 trillion tokens, this model excels at long sequence modeling with an impressive 8K token context window. XGen-7B outperforms industry giants like LLaMA and GPT-3 across diverse benchmarks, including code generation, question answering, and text summarization.
Licensed under the permissive Apache 2.0 license, this multilingual powerhouse democratizes access to cutting-edge language AI capabilities. With its unparalleled performance, scalability, and open-source nature, XGen-7B sets a new standard for open-source LLMs, fostering innovation and transparency within the AI community.
Key Features of XGen-7B:
- Trained on 1.5 trillion tokens of diverse data.
- Instruction-tuned for better task comprehension.
- Dense attention for modelling long sequences.
- Open-sourced under Apache 2.0 license.
- Available in 4K and 8K versions.
Ideal Use Cases:
XGen-7B shines in applications that involve long-form text understanding and generation due to its extended context window. It excels at summarizing lengthy documents, conversations, or scripts. It can comprehend and answer questions based on long contexts from diverse domains. XGen-7B is also well-suited for open-ended dialogue, creative writing tasks requiring coherence over many tokens, and analyzing long sequences like protein structures.
Performance Benchmarks:
In evaluations by Salesforce, XGen-7B's instruction-tuned 8K version achieved state-of-the-art results on AMI meeting summarization, ForeverDreaming dialogue, and TVMegaSite screenplay tasks compared to other open-source LLMs. On long-form question-answering using Wikipedia data, it outperformed 2K baselines by a significant margin. For text summarization of meetings and government reports, XGen-7B was substantially better than existing models at capturing key information over extended contexts.
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8. Vicuna 13-B
Vicuna 13B, developed by LMSYS, is a pioneering 13 billion parameter open-source chatbot model that has revolutionized the field of large language models (LLMs). Fine-tuned on over 70,000 user-shared conversations from ShareGPT, this transformer-based model delivers exceptional performance across diverse natural language processing tasks. Vicuna 13B excels in areas such as content generation, question answering, and code understanding, making it a versatile choice for researchers, developers, and businesses alike.
With its impressive capabilities, open-source availability under the Llama 2 Community License, and commitment to transparency, Vicuna 13B democratizes access to cutting-edge language AI technology, fostering innovation and collaboration within the AI community.
Key Features of Vicuna 13-B:
- Strong conversational abilities and instruction following.
- Open-source and freely available.
- Supports multiple languages.
- Can be fine-tuned for specific tasks.
- Efficient inference through quantization.
Ideal Use Cases:
Vicuna 13-B excels in conversational AI applications like chatbots, virtual assistants, and customer support systems due to its strong language understanding and generation abilities honed through RLHF. It can also handle open-ended tasks like creative writing, code generation, and question-answering effectively.
Performance Benchmarks:
On popular NLP benchmarks like LAMBADA and HellaSwag, Vicuna 13-B achieves near human-level performance, outperforming models like GPT-3. It also shows strong few-shot learning capabilities, matching or exceeding larger models on tasks like translation and summarization after few examples.
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9. BLOOM
BLOOM, developed by BigScience, is a state-of-the-art open-source large language model (LLM) boasting 176 billion parameters. Trained on the ROOTS corpus, which encompasses 46 natural languages and 13 programming languages, BLOOM delivers exceptional multilingual performance across various natural language processing tasks. With its transformer-based architecture and ability to generate coherent text, BLOOM democratizes access to cutting-edge language AI technology.
Licensed under the Responsible AI License, this model fosters innovation, collaboration, and transparency within the AI community. BLOOM's impressive capabilities, coupled with its open-source nature, position it as a game-changer in the field of large language models, empowering researchers, developers, and organizations to harness the power of advanced language AI.
Key Features of BLOOM:
- Completely open-source model with code and checkpoints publicly released under the Responsible AI License.
- Developed collaboratively by over 1000 researchers from 70+ countries and 250+ institutions, led by Hugging Face.
- Supports zero-shot cross-lingual transfer and multilingual applications out-of-the-box.
- Decoder-only transformer architecture allows flexible text generation and completion.
- Smaller model variants like BLOOM-560m and BLOOM-1b7 enable wider access and usage.
Ideal Use Cases:
BLOOM is ideal for applications requiring open-source multilingual language understanding and generation. This includes cross-lingual information retrieval, document summarization, and conversational AI chatbots that need to engage users in their native languages. BLOOM's broad linguistic knowledge also makes it well-suited for creative writing assistance, language education tools, and low-resource machine translation. However, specialized monolingual models may be preferable for high-stakes English-only applications like medical Q&A.
Performance Benchmarks:
BLOOM achieves strong results on cross-lingual natural language inference (XNLI), question answering (XQuAD, MLQA), and paraphrasing (PAWS-X) tasks, often outperforming multilingual BERT-style models. It also demonstrates generative capabilities competitive with GPT-3 on datasets like LAMBADA and WikiText. However, scaling model size from 560M to 1B parameters does not consistently improve BLOOM's performance. BLOOM also generates significantly less toxic content than GPT models in prompted generation settings. Overall, BLOOM represents a milestone in open multilingual NLP technology.
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10. BERT
BERT (Bidirectional Encoder Representations from Transformers) is a pioneering open-source language model that has revolutionized natural language processing since its introduction by Google in 2018. As one of the most widely-used and influential LLMs, BERT's innovative bidirectional architecture allows it to understand the context and meaning of words by considering both the left and right context.
Pre-trained on massive amounts of text data, BERT achieves state-of-the-art performance across a wide range of NLP tasks, from sentiment analysis to question answering. Its open-source nature has spurred extensive research and industry adoption. In 2025, BERT remains a go-to foundation for building powerful NLP applications.
Key Features of BERT:
- Masked language modelling for better understanding of relationships between words.
- Pre-trained on massive text corpora like Wikipedia and books.
- Supports fine-tuning on various NLP tasks with just an additional output layer.
- Base (110M parameters) and large (340M parameters) model sizes.
Ideal Use Cases:
BERT excels at natural language understanding tasks that require capturing context and relationships like question answering, text summarization, sentiment analysis, named entity recognition and natural language inference across various domains.
Performance Benchmarks:
On the GLUE benchmark, BERT achieved 7.6% absolute improvement over previous state-of-the-art. On SQuAD v1.1 question answering, BERT hit 93.2% F1 score, exceeding the human baseline of 91.2%.
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How to Choose the Perfect Open-Source Large Language Model (LLM) for Your Needs
Choosing the right open-source large language model (LLM) is a magical blend of considering your specific use case, evaluating model performance, assessing computational resources, navigating licensing terms, and tapping into the power of community support.
To find your perfect LLM match, start by clearly defining your intended application – whether it's generating content, analysing sentiment, or powering a chatbot.
Next, dive into performance benchmarks to compare contenders on key metrics like accuracy, latency, and efficiency. Don't forget to factor in the computational resources you can dedicate, as larger models often require heftier hardware. Licensing is also crucial – make sure the model's terms align with your commercial goals.
Finally, look for an active community rallying behind the model, as their collective wisdom, continuous improvements, and troubleshooting support can supercharge your LLM journey.
Open-Source LLMs in 2025 – FAQs Decoded for Everyone
What are Open-Source LLMs?
Open-source large language models (LLMs) are powerful AI systems that can understand and generate human-like text. Unlike proprietary models, their source code and training data are publicly available, allowing developers to inspect, modify, and build upon them freely.
What are the benefits of Using Open-Source LLMs?
Some key benefits include enhanced data privacy and security, cost savings by avoiding licensing fees, reduced vendor lock-in, transparency for auditing and customization, community-driven improvements, and fostering innovation through open collaboration.
How Do I Choose the Right Open-Source LLM for My Use Case?
Consider factors like the specific task (content generation, question answering, etc.), model performance and size, computational resources available, licensing terms, and community support. Many open-source LLMs are tailored for different applications.
Can I Run Open-Source LLMs Locally or Do I Need Cloud Services?
While some smaller models can run locally on powerful hardware, the largest open-source LLMs often require substantial computational resources. Cloud services or high-performance infrastructure may be needed to train or deploy these models efficiently.
How Do I Get Started with Using Open-Source LLMs?
Begin by exploring online demos and playgrounds to interact with pre-trained models. Then, follow setup guides to install the required frameworks and run models locally. For deployment, you can use cloud platforms with APIs or self-hosted solutions.
Are Open-Source LLMs Free to Use for Commercial Purposes?
Most open-source LLMs use permissive licenses like MIT or Apache that allow commercial use. However, carefully review the specific terms for each model, as some may have restrictions on commercial applications or require attributions.
What are the Limitations or Risks of Using Open-Source LLMs?
Potential risks include biases or inaccuracies from training data, lack of robust security audits, high computational costs for large models, and the environmental impact of training and inference. Proper vetting and responsible practices are crucial.
Can I Fine-Tune or Customize Open-Source LLMs for My Needs?
Yes, a key advantage of open-source LLMs is the ability to fine-tune them on your own data or modify their architectures and training processes to better suit your specific requirements and use cases.
Recommended Readings:
Let's Wrap It Up
The world of open-source large language models is rapidly evolving, and the models we've explored in this article are at the forefront of this revolution. From LLaMA's groundbreaking advancements to Vicuna's impressive chatbot capabilities, these LLMs are pushing the boundaries of what's possible in natural language processing.
As we move forward, it's clear that open-source models will play a crucial role in shaping the future of AI. Their transparency, accessibility, and collaborative nature foster innovation and democratize access to cutting-edge technology.
So, whether you're a researcher, developer, or simply an AI enthusiast, now is the time to dive in and explore the vast potential of these top 10 open-source LLMs. Experiment with their capabilities, fine-tune them for your specific needs, and contribute to the ever-growing body of knowledge in this exciting field.