Large language models list 2026: the major LLMs with their data
A side-by-side look at the major large language models available in mid-2026, comparing developer, parameters, context window and what each one is best known for.
The context
Why “LLM comparison 2026” is trending
New flagship releases from OpenAI, Anthropic, Google and xAI have landed within months of each other in 2026, alongside fast-improving open-weight models from Meta, Mistral, DeepSeek and Alibaba. That pace is why people keep searching for a straight comparison table rather than another explainer. An LLM is a neural network trained on massive text corpora to predict and generate language, and the practical differences between them (how many parameters they run, how much text they can read at once, whether they can be self-hosted) directly shape which one fits a given use case.
Two specs matter most when comparing models. Parameters are the internal weights learned during training; higher counts generally mean more capacity, but several 2026 models use mixture-of-experts (MoE) architectures that activate only a fraction of total parameters per query, cutting compute cost without sacrificing scale. The context window is how much text a model can consider at once, from prompt to output, and ranges from roughly 128k tokens up to several million in the longest-context systems.
| Model | Developer | Parameters | Context window | Notable for |
|---|---|---|---|---|
| GPT-5.5 | OpenAI | not disclosed | not disclosed | ChatGPT flagship (released April 2026), broadest app/plugin ecosystem, ChatGPT has over 900 million weekly users |
| Claude Opus 4.8 | Anthropic | not disclosed | about 200k tokens | Rated strongest for writing, powers much of the coding-tool ecosystem (Cursor, Windsurf, Claude Code); tiers: Opus / Sonnet / Haiku |
| Gemini 3.1 Pro | Google DeepMind | not disclosed | about 1M tokens | Long-context and reasoning leader, multimodal, integrated into Search, Workspace and Android |
| Llama 4 (Scout / Maverick) | Meta | Open weights: 109B and 400B total (17B active parameters each, mixture-of-experts) | up to about 10M tokens (Scout) | The leading open-weight family, free to download and fine-tune |
| Mistral Large 2 | Mistral AI | 123B (public) | about 128k tokens | Europe’s flagship lab (France), strong multilingual performance |
| Grok 4.3 | xAI | not disclosed | about 1M tokens | Deep X (Twitter) integration, real-time answers, native video input |
| DeepSeek-V4 (preview) | DeepSeek | V4-Pro about 1.6T parameters (public) | about 1M tokens | Chinese open-weight lab (MIT license), very low API prices; R1 reasoning model made it famous in January 2025 |
| Qwen3 | Alibaba | Open weights: up to 235B total (22B active, mixture-of-experts) | up to about 128k tokens | Open weights, very strong multilingual coverage |
Open-weight models (Llama, DeepSeek, Qwen, and some Mistral releases) can be downloaded and run on private servers, which appeals to teams that want cost control or data privacy. Closed models (GPT, Claude, Gemini, Grok) only run on their makers’ infrastructure but tend to ship the newest capabilities first. By mid-2026, four closed frontier systems trade the overall lead depending on the task: Claude Opus 4.8, GPT-5.5, Gemini 3.1 Pro and Grok 4, with open-weight Chinese labs DeepSeek and Qwen close behind at a fraction of the price. For a task-by-task breakdown of which model actually wins where, see which AI model is best in 2026.
Whichever model is chosen, the same limits still apply across the board: hallucination (confident but incorrect output), inherited training bias, and a fixed knowledge cutoff date beyond which the model has no built-in awareness of events.