Llama-3.3-70B-Instruct-Turbo

Llama 3.3 70B Instruct

Meta's text-only workhorse. Mature, widely deployed, and defined by two constraints worth knowing before you build.

Knowledge Ends December 2023

The constraint that decides how you use this model for anything factual.

The pre-training data has a cutoff of December 2023.

That is a long way back. Library versions, API surfaces, product names, regulations, prices, people in roles, and entire companies have changed since — and the model will answer about all of them with complete confidence.

It is also older than the model's own release date. Llama 3.3 shipped in December 2024, a year after its knowledge ends. That gap existed on day one and has only widened.

What follows is not "avoid this model." It is "ground it."

Retrieval is the fix. Documents in the prompt move the model from recalling to reading, and reading is where a seventy-billion-parameter model performs closest to a much larger one. With 128K of context available, supplying what it needs to know is straightforward.

And tell it the date. A model with a fixed cutoff and no sense of today reasons about "recently" and "currently" against the wrong anchor. One computed line in the system prompt removes a category of quiet error.

Eight Languages, and Arabic Is Not One

Stated plainly on Meta's card:

Supported languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.

Eight, and the list is worth reading for what is absent.

No Arabic. No Chinese. No Japanese. No Korean. No Russian.

This matters more than a count suggests if your users write in a language outside the eight. The model will produce text in other languages — it has seen them — but Meta do not support that, which means the quality was not measured and the behaviour was not tuned.

Note the reversal against Meta's own later generation. Llama 4 lists twelve languages with Arabic first. The newer family covers what this one does not.

So the decision is straightforward. If your traffic is in the eight, this model is a mature, well-understood choice. If it is not, the newer Llama generation or a model from a family built for wider coverage is the better fit — and there are several in this catalogue with a hundred languages or more.

What the Post-Training Was

Meta describe it precisely, and the numbers are specific.

Pre-training: ~15 trillion tokens from publicly available sources.

Fine-tuning data: publicly available instruction datasets, plus over 25 million synthetically generated examples.

Method: supervised fine-tuning, then reinforcement learning with human feedback — aligned for helpfulness and safety.

Twenty-five million synthetic examples is the number worth pausing on. That is a scale of generated training data that only works if the generation and filtering are good — a bad synthetic pipeline at that volume teaches a model its own errors, at scale.

Meta note elsewhere that they developed LLM-based classifiers to filter and curate high-quality prompts and responses during the data mix — which is the part that makes twenty-five million viable rather than harmful.

And the method is worth noting as a marker of its generation. SFT plus RLHF was the standard pipeline of late 2024. Models released since have moved toward direct preference methods and large-scale reinforcement learning on verifiable tasks. Neither approach is simply better; they produce differently-shaped models, and this one is shaped by the older, well-understood one.

Specifications

Grouped-query attention is listed for improved inference scalability — the mechanism that keeps the key-value cache manageable across a 128K window on a seventy-billion-parameter model.

Ready for commercial use, subject to the licence.

Provider suffixes such as -Turbo are hosting conventions rather than Meta's naming, usually encoding a quantisation or a serving tier.

Capabilities

Text only. This is the generation before Meta's models became multimodal — Llama 4 introduced native vision through early fusion, and this one has none.

⚠️ Read the Licence

The Llama 3.3 Community License Agreement — a custom commercial licence, not Apache or MIT.

Meta's Llama licences carry conditions that permissive ones do not, and the terms are specific to this version rather than inherited from earlier releases. Read it against your deployment before you build on it.

Meta ship safety tooling alongside the model — Llama Guard for filtering input prompts and output responses, plus Prompt Guard and Code Shield where relevant.

Their own framing of the safety approach is three-part: enable developers to deploy safe experiences, protect developers against adversarial users, and provide protections against misuse.

Which places the first of those on you. The tooling exists; deploying it is a decision.

It Answers Directly

No thinking mode, no effort parameter, no reasoning_content field.

max_tokens covers the answer alone — nothing shares it. On a reasoning model part of that budget goes to a trace you did not ask for; here every token is answer.

Latency tracks input and output length, not how hard the model judged the question. On an interactive endpoint that predictability is frequently worth more than depth.

And on a 128K window with no reasoning overhead, long-input analysis is straightforward to budget — which is one of the practical advantages of a model from this generation.

Using Llama 3.3 70B on DEVUP AI

Base URL: https://api.devupai.com/v1 · Model ID: meta-llama/Llama-3.3-70B-Instruct-Turbo

Python

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["DEVUP_API_KEY"],
    base_url="https://api.devupai.com/v1",
)

response = client.chat.completions.create(
    model="meta-llama/Llama-3.3-70B-Instruct-Turbo",
    messages=[
        {"role": "user", "content": "Hello world!"}
    ],
    max_tokens=1024,
)

print(response.choices[0].message.content)

Node.js

import DevupAI from "devupai";

const client = new DevupAI({
  apiKey: process.env.DEVUP_API_KEY,
});

async function main() {
  const response = await client.chat.completions.create({
    model: "meta-llama/Llama-3.3-70B-Instruct-Turbo",
    messages: [{ role: "user", content: "Hello world!" }],
    max_tokens: 1024,
  });

  console.log(response.choices[0].message.content);
}

main();

cURL

curl -X POST "https://api.devupai.com/v1/chat/completions" \
  -H "Authorization: Bearer $DEVUP_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo",
    "messages": [
      { "role": "user", "content": "Hello world!" }
    ],
    "max_tokens": 1024
  }'

Grounding Against December 2023

The configuration this model needs for anything factual, and it takes four lines.

from datetime import date

GROUNDED = f"""Answer only from the material provided below. Quote the passage supporting each
statement. Where the material does not contain the answer, say so plainly and stop — do not fill the
gap from general knowledge.

Your training data ends in December 2023. The current date is {date.today().isoformat()}. Treat
anything you recall about software versions, APIs, prices, regulations, companies, or people in
roles as potentially out of date, and say so when you rely on it."""

response = client.chat.completions.create(
    model="meta-llama/Llama-3.3-70B-Instruct-Turbo",
    messages=[
        {"role": "system", "content": GROUNDED},
        {"role": "user", "content": f"{documents}\n\nQuestion: {question}"},
    ],
    max_tokens=8192,
    temperature=0.2,
)

The last clause is the one people skip. Telling the model to say so when it relies on training knowledge turns an invisible risk into a visible flag — you get "based on my training data, which may be outdated" rather than a confident assertion you have no way to spot.

The instruction against filling gaps is the other half. A model asked a question it cannot answer from the supplied material will produce a plausible answer from memory unless told not to — and on a December 2023 cutoff, that memory is nearly three years old.

And a 128K window is what makes this practical. Supplying enough material that the model rarely needs its own knowledge is a budget question here, not a constraint.

Long-Document Work

Where 128K and no reasoning overhead combine well.

from pathlib import Path

corpus = "\n\n---\n\n".join(
    f"### {path.name}\n{path.read_text(encoding='utf-8')}"
    for path in sorted(Path("agreements").glob("*.txt"))
)

response = client.chat.completions.create(
    model="meta-llama/Llama-3.3-70B-Instruct-Turbo",
    messages=[
        {
            "role": "system",
            "content": (
                "Review this set of agreements. Identify every obligation stated in one document "
                "that contradicts an obligation in another. Quote both clauses and name both files. "
                "Report nothing you cannot quote."
            ),
        },
        {"role": "user", "content": corpus},
    ],
    max_tokens=16384,
)

print(f"input: {response.usage.prompt_tokens:,}")

Cross-document contradiction is the task that justifies the window. A conflict between the third document and the eleventh is invisible to any pipeline that reads them one at a time.

Requiring a quote per finding is what makes the output checkable — and on a model with an old cutoff, it also constrains the model to the material rather than to memory.

Tool Calling

import json

TOOLS = [
    {
        "type": "function",
        "function": {
            "name": "lookup_order",
            "description": "Return order status, line items, and delivery events for an order ID.",
            "parameters": {
                "type": "object",
                "properties": {"order_id": {"type": "string"}},
                "required": ["order_id"],
            },
        },
    },
]


def lookup_order(order_id: str) -> dict:
    """Replace with your real data access layer."""
    raise NotImplementedError


HANDLERS = {"lookup_order": lookup_order}

thread = [
    {
        "role": "system",
        "content": (
            "You are a support agent. Look facts up with the tools provided rather than assuming "
            "them, and never rely on training knowledge for anything a tool can answer. If the "
            "request does not identify a specific order, ask before calling anything."
        ),
    },
    {"role": "user", "content": "My order shows delivered but nothing arrived."},
]

CEILING = 12

for step in range(CEILING):
    response = client.chat.completions.create(
        model="meta-llama/Llama-3.3-70B-Instruct-Turbo",
        messages=thread,
        tools=TOOLS,
        max_tokens=4096,
    )

    message = response.choices[0].message
    thread.append(message)

    if not message.tool_calls:
        print(message.content)
        break

    for call in message.tool_calls:
        handler = HANDLERS.get(call.function.name)
        if handler is None:
            outcome = {"error": "unknown tool", "name": call.function.name}
        else:
            try:
                outcome = handler(**json.loads(call.function.arguments or "{}"))
            except Exception as exc:
                outcome = {"error": type(exc).__name__, "detail": str(exc)}

        thread.append({"role": "tool", "tool_call_id": call.id, "content": json.dumps(outcome)})
else:
    print(f"Stopped at the {CEILING}-step ceiling.")

"Never rely on training knowledge for anything a tool can answer" carries unusual weight on a model with a December 2023 cutoff. A model that answers from memory when a tool was available is answering from information nearly three years old.

The clarification instruction is the other half. An ambiguous request produces a confident lookup of the wrong thing unless the model is told to ask.

Multilingual Work, Inside the Eight

SYSTEM = (
    "Respond in the same language as the user's message, using that language's own register and "
    "conventions rather than translating English phrasing."
)

Within the eight supported languages, that instruction is what separates a correct answer from a native-sounding one — a model can answer accurately in French while sounding like English rendered into French, and that difference is what a reader notices first.

Outside the eight, the honest answer is a different model. The model will produce text; Meta do not support it, which means the quality was not measured and the behaviour was not tuned. On a product serving Arabic, Chinese, or Russian speakers, that is a decision to make deliberately rather than to discover from complaints.

Why It Is Still Worth Serving

A fair question for a model from December 2024, and there are real answers.

Maturity. Two years of deployment means the failure modes are known, the quantisations are tested, the serving stacks handle it, and the community has answered most integration questions already.

Predictability. No reasoning mode, no adaptive behaviour, no mode switching. Latency tracks length, output is answer-only, and max_tokens means what it says. On a production path that is worth a great deal.

Breadth of deployment. Llama 3.3 70B is one of the most widely served open models in existence, which means tooling, fine-tunes, adapters, and documentation all assume it exists.

Cost. Seventy billion parameters with grouped-query attention, from a generation where efficiency had been worked out, is economical to serve relative to what it does.

And the honest counterweight. Newer models in this catalogue offer longer windows, more languages, vision, reasoning modes, and knowledge that does not stop in 2023. If any of those matter to your workload, they matter more than maturity does.

Where It Fits

Grounded question answering, where documents supply the knowledge and the cutoff stops mattering.

Long-document analysis across a 128K window with no reasoning overhead to budget.

Multilingual dialogue within the eight supported languages — which Meta name as the optimisation target.

Code generation, which is listed as an output modality rather than an incidental capability.

Tool-driven applications, with the caveat that the model should be told to prefer tools over memory.

Production paths that value predictability over capability at the margin.

Not for current factual knowledge. December 2023.

Not for Arabic, Chinese, Japanese, Korean, or Russian — those are outside the supported eight.

Not for images. Text only; Meta's multimodal models are the next generation.

Not for deep reasoning. No thinking mode.

Practical Notes

Ground anything factual. The cutoff is December 2023.

Inject the current date, and tell the model to flag when it relies on training knowledge.

Check whether your users' languages are among the eight before deploying.

Instruct the model to prefer tools over memory, explicitly.

Require quoted evidence on document work.

Size max_tokens to the answer — nothing else consumes it.

Read the Llama 3.3 Community License against your deployment.

Deploy Meta's safety tooling on any public path, or an equivalent.

Compare against a newer model if languages, vision, or current knowledge matter to your workload.

Limitations

Knowledge ends December 2023 — nearly three years before now, and a year before the model's own release.

Eight supported languages, without Arabic, Chinese, Japanese, Korean, or Russian.

Text only. No image, audio, or video input.

No reasoning mode. Multi-step logic and complex analysis belong on a model built for them.

A custom commercial licence, not Apache or MIT, with conditions specific to this version.

SFT plus RLHF post-training, which is the pipeline of its generation. Later models use different methods and are shaped differently as a result.

Seventy billion parameters at full precision is a multi-card deployment.

A December 2024 model. Mature and widely supported, and two generations behind Meta's current family in modality, language coverage, and context.

Confident answers with no visible reasoning. There is less signal about where the model was uncertain, which makes grounding and explicit permission to say "I don't know" more important rather than less.