The best large language models (LLMs) in 2026
Zapier evaluates leading large language models (LLMs) for 2026, highlighting proprietary, open, and open-source options while emphasizing practical use cases and AutomationBench evaluations.
Large language models (LLMs) power most AI tools today, including chatbots like ChatGPT and Claude, code generators like Codex, and features such as Google’s AI answers and Apple Intelligence. These models have evolved from research projects in the late 2010s to widely used tools, with advancements like reasoning models, large multimodal models (LMMs) handling images and audio, and agentic models capable of tool use and coding. The rapid pace of development makes keeping track of the landscape challenging, as new models emerge and others become outdated within days.
Zapier’s list focuses on significant, practical models rather than those excelling in benchmarks alone, prioritizing tools users can actually access. The company’s AutomationBench evaluates how well models handle complex business workflows, offering a data-driven approach to selecting the right AI model. Open and open-source models like gpt-oss-20b, Gemma 4, and DeepSeek V4 allow users to run them locally or via APIs, while proprietary models such as GPT-6 and Claude Fable 5.1 remain popular but are controlled by private companies with undisclosed details.
The distinction between open and open-source models lies in licensing: open-source licenses are highly permissive, requiring derivative works to also be open-source, while open licenses like Google’s Gemma 4 include usage restrictions, such as prohibiting criminal facilitation. Western companies predominantly develop proprietary models, whereas many open models originate from Chinese tech firms, reflecting a growing divide in AI development. This shift underscores the global nature of AI innovation and the varying regulatory approaches across regions.
Early LLMs struggled to maintain coherence beyond a few sentences, but modern models like GPT-6 can generate coherent text spanning tens of thousands of words or write code while considering entire codebases. Training data for these models typically includes vast repositories like the public internet, published books, and synthetic AI outputs, enabling them to produce authoritative-sounding text across diverse subjects. The underlying technology relies on neural networks that model relationships between tokens—fractions of words—using high-dimensional vectors to generate contextually appropriate responses.