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WHich is the first LLM and the best 10 LLMs

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How much the models agreed on this answer

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Models agree on

✓The term 'LLM' relates to Large Language Models in AI, not just legal licensure.
✓GPT-1 (OpenAI) is frequently cited as a 'first' or foundational modern LLM, often connected to the transformer architecture.
✓BERT (Google) is recognized as a pioneering transformer-based model, fundamentally important for NLP, especially for language understanding.
✓GPT-4 / GPT-4o, Claude 3/3.5, and Gemini 1.5 Pro are consistently listed among the top-performing, proprietary LLMs for general capabilities, reasoning, and multimodal features.
✓LLaMA (Meta) and Mistral AI's models (e.g., Mixtral 8x7B, Mistral 7B) are recognized as leading open-source or open-weight models, praised for their performance and efficiency.
✓'Best' is context-dependent, varying by use case, cost, and open-source vs. proprietary needs.

Points of disagreement

~**Identity of the 'First LLM'**: While GPT-1 is widely mentioned as a foundational modern LLM, some explicitly credit GPT-2 (2019) as the first 'large language model' to demonstrably scale benefits, and others focus on BERT (2018) as a pivotal transformer-based model, implying it as the first 'modern LLM' in a broader sense.
~**Ranking and Specific Models in Top 10**: The exact composition and ranking of the 'best 10 LLMs' vary significantly between models due to different evaluation criteria, currency of information (e.g., 'as of October 2023' vs. 'as of mid-2025'), and inclusion of proprietary vs. open-source models. For instance, some lists heavily feature historically significant but older models (BERT, T5, RoBERTa), while others focus almost exclusively on very recent (2024) state-of-the-art models like GPT-4o and Claude 3.5 Sonnet.
~**Specific characteristics and parameter counts**: While general capabilities align for top models (reasoning, multimodal), the detailed parameter counts and specific architectural highlights are mentioned with varying levels of detail and sometimes different emphasis (e.g., 'effective parameters' for MoE models).

Pinpointing the 'first' Large Language Model (LLM) is nuanced, as the definition has evolved. While some might cite early statistical language models, the consensus for a 'modern' LLM with transformer architecture and large-scale pre-training points to models from 2018-2019. Often, GPT-1 (2018/2019) by OpenAI is credited as the foundational model for the transformer-based GPT series, showcasing the potential for human-like text generation. However, BERT (2018) by Google is also frequently highlighted as a pioneering transformer-based model that introduced the pre-train-then-fine-tune paradigm, impactful for language understanding, not generation. Some also assert GPT-2 (2019) as the first explicitly labeled "large language model" that clearly demonstrated the benefits of scaling.

As for the 'best' LLMs, the landscape is incredibly dynamic, with models constantly evolving. Performance is highly dependent on the specific use case, and the definition of 'best' varies between proprietary and open-source offerings, and whether focusing on general capability, efficiency, or specialized tasks.

Here's an integrated list of top LLMs, blending perspectives and including key differentiators:

Top LLMs (as of late 2023 - mid 2024, reflecting constant evolution):

·GPT-4 / GPT-4o (OpenAI): Widely regarded as a leader in general intelligence, reasoning, and instruction following. GPT-4o, an omni-modal model, especially excels in human evaluations and can process text, vision, and audio. GPT models are proprietary.
·Claude 3/3.5 (Anthropic): Known for strong performance in complex reasoning, coding, long context windows (up to 200K tokens), and safety alignment (Constitutional AI). Claude 3.5 Sonnet shows impressive results in coding and grad-school level tasks. Proprietary.
·Gemini 1.5 Pro / Flash (Google DeepMind): Noteworthy for massive context windows (up to 1M tokens), strong multimodal capabilities (text, image, audio), and efficient Mixture-of-Experts (MoE) architecture. Excels in multimodal reasoning and fast inference. Proprietary.
·LLaMA 2 / 3 (Meta): A leading open-source family of models. LLaMA 3, a significant improvement over LLaMA 2, demonstrates strong performance (e.g., MMLU, GSM8K) and is widely adopted for research and fine-tuning due to its accessibility and competitive benchmarks.
·Mixtral 8x7B (Mistral AI): An open-weight Mixture-of-Experts (MoE) model. It offers exceptional performance for its active parameter count (39B active from 141B total), making it highly efficient, competitive with larger dense models, and strong in reasoning and coding.
·Mistral Large / 7B-Instruct (Mistral AI): Mistral Large is competitive with leading proprietary models, while Mistral 7B-Instruct is highly optimized for efficiency, making it excellent for on-device or low-cost inference with high per-parameter performance. Both offer open weights (Mistral Large being a commercial offering).
·DBRX (Databricks): An enterprise-focused MoE model (132B total, 36B active) optimized for business use cases, demonstrating strong performance in SQL generation and coding. Proprietary.
·Qwen 1.5 / 2 (Alibaba Cloud): Open-source, multimodal (text/image), and excels in multilingual benchmarks, especially for Chinese and mixed-language tasks.
·Cohere Command R+ (Cohere): Enterprise-focused, optimized for retrieval-augmented generation (RAG) and low-latency chat, with a strong emphasis on factuality, making it ideal for business applications. Proprietary.
·Phi-3.5 (Microsoft): A small yet powerful open-weight model (3.8B parameters), distilled from larger models and optimized for edge devices, achieving impressive MMLU scores for its size.

Other historically significant or specialized LLMs include BERT (Google) and RoBERTa (Facebook AI), which revolutionized language understanding; T5 (Google), known for its text-to-text framework; and XLNet (Google AI) for its unique training approach. Megatron-LM (NVIDIA/Microsoft) and BLOOM (BigScience) are also notable for their scale and open-science contributions respectively.

The 'best' choice invariably depends on factors like required performance ceiling, cost, deployment environment (cloud vs. on-device), need for open-source flexibility, and specific task requirements (e.g., code generation, creative writing, factual retrieval, multilingual support, or multimodal processing). Benchmarks like LMSYS Chatbot Arena and Hugging Face Open LLM Leaderboard offer up-to-date performance comparisons.

WHich is the first LLM and the best 10 LLMs

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