Llama 3 in 2026: what actually shipped, and what did not

The actual Llama release timeline

Here is every Llama release that bears on this question, in order. Note that the four releases people might call "Llama 3" all landed in 2024, and the only thing after them is the Llama 4 generation in 2025.

Why people keep searching for a 2026 Llama 3

There are a few honest reasons this question shows up so often. Llama versioning moved fast: four point releases in 2024 alone, then a jump to a different major number in 2025. If you onboarded onto Llama 3.1 or 3.3 in 2024 and only checked back in 2026, it is reasonable to assume there must be a newer Llama 3 by now. There is not. The line stopped at 3.3, and the next step was Llama 4.

The other reason is that "Llama 3" became a generic shorthand for "the open Meta model" the way people say "Kleenex" for tissue. So a search for the latest Llama 3 news in 2026 usually means one of two things: either you want the newest model overall (that is Llama 4 Scout or Maverick), or you want the most recent 3.x specifically (that is Llama 3.3 70B from December 2024). Both are answerable, neither is a 2026 release.

If you landed here trying to decide what to actually pull and run, the practical answer is below. The version you download matters less than where the model lives and what drives it.

The part no Llama roundup covers: running it as an agent on your Mac

Every other guide on this stops at specs and download links. The question that actually changes your day is different: once you have a Llama model, how do you put it behind a real agent loop, locally, on a Mac, without losing your work every time you restart?

That is the thing fazm is built around. fazm is a native macOS app that wraps Claude Code (via the Agent Client Protocol package @agentclientprotocol/claude-agent-acp) and Codex (codex-acp) so you get the same agent loop in a window instead of a terminal. The detail that matters for Llama: fazm exposes a custom API endpoint setting. Point it at a local inference server behind an Anthropic-compatible gateway and the agent drives Llama instead of Anthropic's models. Nothing about the loop changes.

# 1. Serve a Llama model locally (example: Ollama)
ollama pull llama3.3:70b
ollama serve            # exposes http://localhost:11434

# 2. Put an Anthropic-compatible gateway in front (LiteLLM)
litellm --model ollama/llama3.3:70b --port 4000
# -> http://localhost:4000 now speaks the Anthropic messages API

# 3. In fazm, set the custom API endpoint for the chat:
#    Base URL: http://localhost:4000
#    Model:    llama3.3:70b
# The Claude Code / Codex agent loop now drives Llama,
# while fazm keeps the session, the fork button, and full context.

The gateway exists because the agent harness expects an Anthropic-shaped API while Ollama and vLLM speak their own. LiteLLM translates between them, which is what lets the unchanged Claude Code loop talk to Llama.

Raw terminal Llama agent vs the same loop in fazm

You can run a Llama-backed agent from a bare terminal today. The model works. What breaks is everything around it: close the window or restart the Mac and the conversation is gone, branching an experiment means a session-id dance, and long runs quietly compact context to stay under a limit, dropping decisions you made earlier.

What you need to drive Llama from fazm

Nothing here is fazm-specific except the last step. If you already run a local Llama, you are most of the way there.