Short answer — there’s no single “best” AI model; the best choice depends on task (chat, long-context analysis, code, images, embeddings, on-device use), budget, and whether you need open-source vs hosted. Below are the leading models (by category) and quick guidance for which to pick.

Top models by category (as of Sep 28, 2025)

• Large general-purpose chat / reasoning

  • OpenAI: GPT-4o family — strong at general reasoning, tool integration and multimodal inputs; widely used in production (via OpenAI/Microsoft partners). (general industry context).
  • Anthropic: Claude 3 family (Opus / Sonnet / Haiku) — top-tier reasoning and long-context capabilities; widely deployed in enterprise products. (Anthropic.com)
  • Google: Gemini 1.5 Pro — strong multimodal and audio/video reasoning, developer features like JSON mode and system instructions. (developers.googleblog.com)

• Open / research-friendly LLMs

  • Meta: Llama 3 family (8B, 70B, 405B / later 3.3 variants) — very competitive open models with strong performance/cost trade-offs; useful when you need local hosting or custom fine‑tuning. (techcrunch.com)
  • Mistral / Falcon families — popular open alternatives (Mistral for compact performance; Falcon for larger open-weight options).

• Long-context / document work

  • Claude and some variants of GPT/Gemini emphasise very large context windows; pick the model that offers the token window you need (100k+ tokens for very long documents). Anthropic and OpenAI have focused heavily here. (Anthropic.com)

• Code generation and developer tooling

  • GPT-family (OpenAI) and Anthropic’s Claude variants and specialized models from GitHub Copilot/other vendors are strong for code tasks. Microsoft/Anthropic integrations have expanded enterprise tooling. (theverge.com)

• Multimodal (images, audio, video)

  • Google Gemini 1.5 Pro and the multimodal variants from OpenAI (GPT-4o multimodal lines) are leading for integrated audio+video+text workflows. (developers.googleblog.com)

• Embeddings and retrieval

  • Choose the embedding model that matches your retrieval latency/accuracy needs — Google and OpenAI publish newer embedding models; many open models also exist via Hugging Face.

How to pick the “best” for you

• If you want the absolute best hosted reasoning/chat experience and don’t need open weights: try the latest GPT (OpenAI) or Claude (Anthropic); compare cost and safety/guardrails. (Anthropic.com)
• If you need open weights or on-premises deployment: Llama 3 family, Mistral, Falcon. (techcrunch.com)
• If you need multimodal (speech + video + images) or structured outputs (JSON, function-calling): Gemini 1.5 Pro and recent OpenAI models offer explicit features for that. (developers.googleblog.com)
• If you need very long-document context (books, large codebases): pick models advertising large token windows (e.g., Claude/other “200k+ token” offerings). (Anthropic.com)

Practical tips

• Benchmark with your own prompts/data — published leaderboard scores don’t always reflect real-world behaviour on your tasks.
• Check licensing and hosting: open models give flexibility but require infra; hosted APIs are easier but cost and data residency matter (important for UK/EU customers).
• Watch deprecation and update notices: providers retire older models and release newer families — keep your code/config up to date. (docs.Anthropic.com)

If you want, I can:

• Recommend 2–3 specific models (hosted or open) tailored to your task (chat, code, image, long-doc retrieval), or
• Run a short benchmark plan you can use to compare model outputs on your own prompts.

Which would you like?