Is it good enough?

Capabilities & modalities

Llama is a strong generalist: reasoning, summarisation, instruction-following, multilingual chat, and capable coding. The dense 3.1/3.3 line is text-only; the Llama 4 generation is natively multimodal, accepting images alongside text. The key usage caveat is legal, not technical: the multimodal Llama models are not licensed to EU-domiciled entities, so EU adopters should restrict themselves to the text-only checkpoints. Across the family, capability scales with size - the 8B is an excellent lightweight worker, while 70B/405B and the large MoE models close in on frontier quality for open weights.

Context window & long-context behaviour

Llama 3.1 and 3.3 provide a 128K-token context window. Llama 4 Scout extends this dramatically to a very long (multi-hundred-K+) window via its mixture-of-experts architecture. As with all long-context models, effective recall is strongest near the prompt boundaries and softens in the middle of very long inputs, so structure retrieval and place the most important material deliberately rather than relying on uniform recall.

Prompt format & chat template

Llama Instruct models use the Llama 3 chat template, built from special tokens that delimit roles and turns (a begin-of-text marker, <|start_header_id|>role<|end_header_id|> headers, and an <|eot_id|> end-of-turn token). Do not hand-assemble these strings - apply the tokenizer's built-in chat template so the exact tokens and system/user/assistant structure are correct:

from transformers import AutoTokenizer
tok = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
msgs = [{"role": "system", "content": "You are helpful."},
        {"role": "user", "content": "Hello!"}]
prompt = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)

Template mismatches are one of the most common causes of degraded output - see the Support troubleshooting notes.

Multilingual coverage

Meta officially supports eight core languages - English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai - for the Llama 3.1 line. The model has broader informal competence in other languages from its pretraining, but only the eight are officially validated and covered by the intended-use terms; production use outside them should be evaluated for the specific language.

Function / tool calling

From Llama 3.1 onward, tool/function calling is a documented, first-party capability. The model cards describe both built-in tool formats and custom (JSON) tool calls, and the tokenizer's chat template supports passing tool/function definitions. Serving stacks (vLLM, TGI, Ollama) surface this through OpenAI-compatible tools/tool_calls fields. Tool-calling reliability still varies with model size - validate outputs and treat any tool-triggered content as untrusted.