Updated
LLMs are the general-purpose models behind chat, coding, research, and agents. The real decision isn’t which is smartest - it’s matching capability, price, context, and access, on a leaderboard that reshuffles monthly. We ranked the 15 that matter most.
Best LLMs
#ModelBest for
How to Choose
When choosing between these models, consider:- Access: First decide whether you want the model in an app, through an API, or running locally, because that choice drives cost, privacy, latency, and setup work more than small capability gaps do. Among the main picks, Gemma 4 31B is the cleanest run-it-yourself option, and only on a high-end machine. gpt-oss-120b and Llama 4 Scout are worth checking as open-weight alternatives, but they are not stronger overall recommendations here. The other highlighted open-weight models are “open” but need self-hosting infrastructure, so in practice you’re calling a hosted API just like a proprietary one.
- Quality: Our score is a single 0-100 number that blends two respected public signals - the Artificial Analysis Intelligence Index, which measures reasoning and task benchmarks, and Arena Text Overall, which measures head-to-head human preference. It’s a good broad gauge of current capability, but it won’t predict every prompt, so use it to build a shortlist and then test the top two or three on your own work.
- Price: We compare blended cost per million tokens, which folds input and output into one number for an apples-to-apples view. Prices here span more than a hundredfold, so once two models both clear your quality bar, cost usually decides.
- Context window: This is how much text the model can weigh at once. A 1M-token window comfortably holds a large codebase or a stack of documents; the 256k-500k models are fine for most single-document and chat work but can force you to chunk very long inputs. Match the window to your longest realistic input, not the biggest number on the page.
Other Models We Considered
Frequently Asked Questions
What is the best LLM right now?
What is the best LLM right now?
Claude Fable 5, on raw capability. It tops our combined score and pulls ahead most on hard, long-horizon work. But it’s the priciest option here, and for a lot of tasks you won’t notice the gap over Claude Opus 4.8, GPT-5.6 Sol, or Claude Sonnet 5 - each a fraction of the cost.
What is the best LLM for most people?
What is the best LLM for most people?
Claude Sonnet 5. It gives you most of the top tier’s quality - strong reasoning, clean writing, a 1M-token context - at a mainstream price, and it stays fast in interactive use. Gemini 3.1 Pro and GPT-5.5 are the close alternatives worth comparing.
What is the best open-weight LLM?
What is the best open-weight LLM?
GLM-5.2 is the strongest open-weight pick here, close to solid midrange proprietary models while costing less. Just remember “open weight” means you can host it or use a provider, not that you’ll run it on a laptop - it needs real self-hosting infrastructure. For lower cost, DeepSeek V4 Pro is the next step down.
What is the best LLM you can run locally?
What is the best LLM you can run locally?
Among the main picks, Gemma 4 31B is the cleanest local choice, and only on a high-end machine with a strong GPU or ample memory. You trade capability for offline use, privacy, and zero per-token cost. gpt-oss-120b and Llama 4 Scout are also worth checking as open-weight alternatives, but the bigger open models here need self-hosting infrastructure rather than a normal machine.
Is Claude better than ChatGPT?
Is Claude better than ChatGPT?
Those are apps, not models, and the honest answer depends on which model you run inside them. Claude Fable 5 leads our score, but GPT-5.6 Sol is right behind and strong across broad tasks. Pick by the specific model and your workload, not the brand, and test both on your own prompts.
Are LLM benchmarks reliable for real-world use?
Are LLM benchmarks reliable for real-world use?
They’re a good starting filter, not a verdict. Our score blends reasoning benchmarks with head-to-head human preference, which captures broad capability well but can’t predict how a model handles your exact prompts, domain, or tools. Use the score to shortlist, then test the top two or three on your real work.
Should you choose by score, price, context window, or access?
Should you choose by score, price, context window, or access?
Access first: app, API, or local changes cost, privacy, and setup more than small score gaps. Then take the cheapest model that clears your quality bar - prices here vary more than a hundredfold. Treat context window as a gate, matching it to your longest realistic input.