Meta Llama

Meta Llama — Open-weight Llama models, hosted almost everywhere but Meta. Meta's contribution to this category is weights, not an endpoint. Llama models are downloadable under a community licence with usage conditions, and are served by every router and cloud on this page. Meta's own hosted API exists but has never been the primary distribution channel, which makes Llama the reference case for portability: if a host retires it, three others still serve it. Built by Meta. Founded 2004. Open source under Llama Community License.

We track it in 1 comparison — LLM APIs — so every claim below is a cell in a table you can open and check rather than an impression. Across those rosters it sits against 17 other tools, and what follows is where it visibly separates from them.

Where it wins.

• Context window — 1,000,000 tokens, tied for best across 4 of 10 tools.
• AU region — Yes, which only 4 of 17 tools here manage.

Each of those is ranked against the whole roster on its category page, not against a hand-picked subset, so a first place here means first of everything we list.

Where it gives ground.

• Effort control — No. Anthropic records Yes.

None of these disqualify it on their own. They are the fields to check against your own requirements before you commit, because they are the ones where a competitor genuinely does better.

Provenance. 19 of 27 tracked fields carry a value for Meta Llama, and 3 of those cite a document you can open. Last verified 2026-01-15. Every figure keeps its own provenance — measured by us, claimed by the vendor, inferred, or community-reported — and we would rather print a dash than a guess.

Its nearest neighbour in our data is Qwen. Meta Llama is ahead on Context window (1,000,000 tokens against 262,144 tokens) and Image input (Yes against Partial). Qwen takes Trains on your data (Partial against No) and Effort control (Partial against No). That pattern repeats across the rest of the roster — see Meta Llama alternatives for the other rivals, each compared the same way.

Company

Meta

Founded

2004

Licence

Llama Community License (open source)

Fields we track

19 of 27

Last verified

2026-01-15

Listed here as a model family rather than a serious first-party endpoint — nobody should be calling Meta's API when Groq, Together, Fireworks and Bedrock all serve the same weights better. Its real value is as the category's continuity insurance: a capable model that literally cannot be deprecated.

Context window

1,000,000 tokens1st of 10

Vendor-claimedverified 2026-01-15source

AU region

●Yes1st of 17

Inferredverified 2026-01-15

Pinnable versions

●Yes1st of 18

Inferredverified 2026-01-15

Trains on your data

○No7th of 18

Inferredverified 2026-01-15

Effort control

○No12th of 18

Inferredverified 2026-01-15

OpenAI-compat API

●Yes1st of 18

Inferredverified 2026-01-15

MCP support

○No6th of 18

Inferredverified 2026-01-15

Image input

●Yes1st of 18

Vendor-claimedverified 2026-01-15source

Meta Llama is the right call in these situations, each one drawn from a field we actually record:

• Workloads that must survive any single vendor disappearing.
• On-premise or air-gapped deployments with a recognisable licence.
• Fine-tuning on your own data without a vendor in the loop.
• Context window is your binding constraint. Meta Llama records 1,000,000 tokens, the best figure in the LLM APIs roster. We define that field as maximum INPUT tokens the flagship model accepts in a single request, at the standard (non-surcharged) tier.
• AU region is your binding constraint. Meta Llama records Yes, which only 4 of the 17 tools in the LLM APIs roster do. We define that field as whether inference can be pinned to Australian infrastructure;.

Reach for something else when any of the following is a requirement rather than a nice-to-have:

• Effort control. Meta Llama records No on the LLM APIs table. Anthropic records Yes on the same field. If that is a hard requirement rather than a preference, start elsewhere.

We publish this block because a comparison that only lists what a tool is good at is marketing. Every figure above sits on the same page as its source, and the field definitions are on the category tables if you want to check how we measured them.

What we don't know. 8 of the 27 fields we track for Meta Llama are still blank: Max output, TTFT p50, Output tok/s, $/M input, $/M output, $/M cache read, Batch discount and Notice period. Those render as dashes rather than as zeroes or assumptions, because an empty cell and a bad cell are not the same thing and only one of them is honest. If you know any of these figures and can point at a document, tell us.