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The field guide to large language models

Compare 55 models from 17 providers on price, context and licensing. Run the numbers with free calculators. Learn how it all works in plain English.

Prices and specs checked against provider documentation on October 10, 2026. No provider pays for placement.

Interactive demo: how a language model picks its next word

I need a language model for

Next-token candidatesProbability
1.0

This is how an LLM writes: it scores every possible next token, then samples one. Raise the temperature and the odds flatten; drop it to zero and the top token always wins. Pick a token to jump to the right page.

What the leading models cost

List prices per million tokens, and what a 30,000-conversation support bot would cost each month on each model.

Price your own workload
ModelWeightsContextInput / 1MOutput / 1MSupport bot, per month*
GPT-6.1 SolOpenAIClosed1.05M$2$10$240
Claude Sonnet 5.5AnthropicClosed1M$2$10$240
Claude Opus 5.5AnthropicClosed1M$4$20$480
Grok 4.7xAIClosed500K$2$6$192
DeepSeek-V4.1-FlashDeepSeekOpen weights1M$0.30$1.20$32
GPT-6 AstraOpenAIClosed1.05M$10$50$1,200
Gemini 3.8 FlashGoogleClosed1M$0.75$3.75$90
Claude Fable 5.1AnthropicClosed1M$10$50$1,200
GLM-5.3Z.ai (Zhipu AI)Open weights1M$1.40$4.40$137
Qwen3.8-2.4T-A95BAlibaba (Qwen)Open weights256K$2$6$192

*30,000 conversations of 2,000 input and 400 output tokens at list price. Frontier and advanced models, newest first.

Building with LLMs? Get a vendor-neutral plan.

Tell us what you're building. Within 2 business days you get a model shortlist, an architecture outline and a cost estimate, plus optional introductions to vetted implementation partners.

  • Free and vendor-neutral: no model provider pays for placement.
  • Your details go only to partners you agree to.

Takes about 2 minutes. No sales calls unless you ask for one.

Understand what you're buying

14 plain-English guides on how LLMs work, what they cost and where they fail, each checked against primary sources.

All guides
FundamentalsWhat is a large language model?A large language model is a neural network trained on enormous amounts of text to predict what comes next, which turns out to be enough to draft, summarize, translate and write code. This guide explains what LLMs are, how they're trained, the kinds you'll meet, the major families as of October 2026 and the limits to plan around.Beginner10 min readChoosing & buyingHow to choose an LLM for your projectA practical framework for choosing a large language model: define the job, weigh ten criteria in a scoring matrix, and run a quick evaluation on your own data. Includes starting points for five common use cases as of October 2026 and a procurement checklist.Intermediate10 min readChoosing & buyingLLM API pricing explainedLLM APIs bill per token, but output, reasoning, caching, batching and long-context tiers can change a bill several-fold. This guide explains each pricing component, shows real prices as of October 2026 and works through three cost examples step by step.Beginner11 min readRunning & deployingHow to run LLMs locallyA practical guide to running open-weight language models on your own computer, from memory math and quantization formats to tools and model picks. It includes an Ollama quick start, hardware tiers as of October 2026, performance tips and fixes for common errors.Intermediate11 min readBuildingRetrieval-augmented generation (RAG) explainedA practical guide to retrieval-augmented generation, covering each stage of the pipeline from parsing and chunking to hybrid search, reranking and cited answers. It also explains how to evaluate RAG quality, when long context or fine-tuning fits better and how to secure the system.Intermediate10 min readBuildingPrompt engineering: a practical guide with examplesA practical guide to writing prompts that work: the five parts of a prompt, core principles, eight techniques with before-and-after examples, and how reasoning models change the rules. Includes a testing workflow and a reusable template.Beginner11 min read

Term of the week

Speculative decoding

A speed-up where a small model drafts several tokens and the large model checks them all at once.

Browse all 169 terms

Watch and learn

Hand-picked explainers from people who build these systems, each with a short summary so you know what you're getting.

All 26 videos

[1hr Talk] Intro to Large Language Models

Andrej Karpathy59:48BeginnerWatch on YouTube

A one-hour tour of what a large language model is, how one gets trained, and where it still falls short, presented without heavy math. It suits newcomers who want a complete mental model before reading about any single technique.

Large Language Models explained briefly

3Blue1Brown07:57BeginnerWatch on YouTube

A short, animated primer on how a language model predicts the next word, how training tunes billions of parameters, and what makes transformers different. It works as a first stop for beginners and as a quick refresher for everyone else.

Deep Dive into LLMs like ChatGPT

Andrej KarpathyIntermediateWatch on YouTube

A multi-hour walkthrough of the full pipeline behind assistants like ChatGPT, from gathering web text and tokenizing it to pretraining, supervised fine-tuning and reinforcement learning. It rewards technical readers and curious builders who want a durable picture of how modern models are made and why they behave as they do.

What happened lately

The most recent milestones from our timeline of 65 events, from the 2017 Transformer paper to this month. Every entry links to a primary source.

See the full timeline

  1. Oct 7, 2026
    GPT-6 rolls out in ChatGPT

    OpenAI began rolling GPT-6 out across ChatGPT, including the free tier, and added Intelligent UI, which can answer with charts, forms and small interactive tools as well as text.

  2. Sep 3, 2026
    GPT-6 Astra released

    OpenAI opened its GPT-6 generation with Astra, which it said met its 'Critical' cybersecurity threshold, so it shipped with safeguards that refuse advanced cyber tasks such as proof-of-concept exploits.

  3. Aug 2026
    Qwen3.8 open weights released

    Alibaba's Qwen3.8 open-weight releases included a 27B Apache 2.0 model and a 2.4-trillion-parameter mixture-of-experts flagship (95B active) under a separate custom license.

  4. Jul 27, 2026
    EU AI Act high-risk deadlines delayed

    The Digital Omnibus amendments pushed high-risk AI obligations to December 2027 and August 2028, days before the Act's general application date of August 2, 2026.

  5. Jun 9, 2026
    Claude Fable 5 and Mythos 5 released

    Anthropic made a Mythos-class model generally available as Fable 5, routing some cyber, bio and distillation queries to Opus 4.8, while the less-restricted Mythos 5 went to vetted partners.

  6. May 19, 2026
    Gemini 3.5 Flash released

    Unveiled at Google I/O, Gemini 3.5 Flash opened Google's 3.5 generation and was generally available in the Gemini API on day one; a larger 3.5 Pro was promised later.

Frequently asked questions

What is LLModel.com?

LLModel.com is an independent reference for large language models. It tracks the models from major providers and open-weight labs with prices, context windows and licenses checked against provider documentation, and adds free calculators, comparisons, plain-English guides, a glossary and curated videos.

How do you keep model prices up to date?

Every model entry lists the official pages we checked and the date we checked them. We re-verify prices and specs against provider documentation on a regular schedule and when providers announce changes. Prices still change often, so confirm on the provider's pricing page before you buy.

Which LLM should I use?

It depends on the task, your budget, how much text you send, and whether data can leave your network. Start with the five-question model picker or a shortlist by job, then test the top two or three models on a sample of your own work and compare cost per correct answer.

Are the tools free?

Yes. The calculators, comparison tool, token counter and prompt builder are free and need no account. They run in your browser, so the text and numbers you enter aren't sent to us.

How does LLModel make money?

Through display advertising, clearly labeled sponsorships, optional introductions to implementation partners when you ask for help, and reader support. No provider pays to change a ranking, a score or an editorial verdict. Our editorial policy explains the rules.