Meta Llama
Meta Llama is open-weight Llama models, hosted almost everywhere but Meta. We compare it in LLM APIs. It is one of only 4 of 17 tools in its roster with AU region. The trade-off is being marked No on Effort control, where Anthropic records Yes.
What is Meta Llama?
- Meta Llama, Open-weight Llama models, hosted almost everywhere but Meta.
- - Meta's contribution is weights, not an endpoint
- Downloadable under a community licence with usage conditions, served by every router and cloud here
- Meta's own hosted API exists but was never the primary channel
- The reference case for portability: if one 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.
- 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.
- 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.
- A first place here means first of everything we list.
Where it gives ground.
- 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 Image input (Yes against Partial) and Open weights (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.
At a glance
- Company
- Meta
- Founded
- 2004
- Licence
- Llama Community License (open source)
- Fields we track
- 19 of 27
- Last verified
- 2026-01-15
Meta Llama in LLM APIs
Ranked against 18 tools across 27 sourced fields. Open the full LLM APIs table.A model family, not a serious first-party endpoint; its value is continuity insurance, a model that cannot be deprecated.
Where it lands in this roster
- 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
When to use Meta Llama
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.
- 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;.
When not to use Meta Llama
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. - Audio in/out. Meta Llama records No on the LLM APIs table. OpenAI
records Partial 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.
Options compared alongside Meta Llama
Everything below shares at least one comparison category with Meta Llama, ordered by how much overlap there is. For the reasoning on each, which fields it wins, which it loses, see Meta Llama alternatives.
Sources and gaps
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.
- www.llama.com/models/llama-4/Context window, Image input · Vendor-claimed · verified 2026-01-15
- www.llama.com/llama-downloads/Open weights · Vendor-claimed · verified 2026-01-15