The best LLMs in 2026 come from a handful of families: OpenAI's GPT models (behind ChatGPT), Google's Gemini and Anthropic's Claude lead the proprietary side, and Meta's Llama, DeepSeek, Mistral and Alibaba's Qwen lead the open-weight side. No single model wins at everything, and the lead changes with each release. Pick by task: test two or three models on your own real work and keep the one that gets it right at a cost you can accept.
We've left out version numbers, benchmark scores and prices on purpose. They change every few months, and a ranking that's out of date is worse than none. Check each provider's own pages for current versions and pricing.
The main LLM families
| Family | Made by | How you get it |
|---|---|---|
| GPT | OpenAI | ChatGPT app and OpenAI API (proprietary) |
| Gemini | Gemini app, Google products and Google's API (proprietary) | |
| Claude | Anthropic | Claude app and Anthropic API (proprietary) |
| Llama | Meta | Downloadable open-weight models under Meta's license |
| DeepSeek | DeepSeek | Chat app, API, and open-weight releases |
| Mistral | Mistral AI | Chat app, API, and some open-weight releases |
| Qwen | Alibaba | Open-weight releases and Alibaba Cloud |
Each family comes in several sizes. The big model is the most capable; smaller ones are faster and much cheaper, and are often good enough for simple jobs.
Which LLM to use for what
| If you need | Look for |
|---|---|
| Everyday writing, email, summaries | Any leading assistant on its free or standard tier. Pick the one whose tone you like. |
| Hard math, logic or planning | A reasoning model or reasoning mode. Expect slower answers. |
| Coding | Try the top models on a real task from your codebase, not a toy example. |
| Long documents or large codebases | A model with a large context window, and check that it actually uses the whole document by asking about details near the end. |
| Sensitive data that must stay in-house | An open-weight model you host yourself, or a provider contract that covers data retention. |
| High volume at low cost | A smaller, cheaper model from the same family, tested against the large one on your task. |
How to test models yourself in an hour
- Write down five to ten real tasks you'd actually use the model for, with the answer you'd accept.
- Run the same prompts, word for word, through two or three models.
- Score each answer: was it correct, did it follow your instructions, and would you have to fix it?
- Ask at least one question where you know the facts cold. Watch for confident answers that are wrong.
- For API use, note how long each answer took and estimate the monthly cost at your volume.
Public leaderboards are a fair starting point for a shortlist, but your own tasks are the test that counts.
Open-weight or proprietary?
Proprietary models are the easiest to start with: no setup, regular updates, and usually the strongest results on the hardest tasks. The tradeoff is that your prompts go to the provider's servers and you depend on its pricing and terms.
Open-weight models can run on your own hardware, so your data never leaves your systems, and you can fine-tune them for your field. The tradeoff is the work and the hardware cost of hosting them yourself. Licenses differ between families, so read them before you build a product on one.
LLM questions people ask
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