Meta-Llama-3.1-70B-Instruct-Turbo

Llama 3.1 70B Instruct

The release that brought long context to Meta's open models — and the base a great many other models were built on.

Sixteen Times, In One Step

The change Llama 3.1 introduced, and it reshaped what open models could do.

From eight thousand tokens to a hundred and thirty-one thousand. Not an incremental widening — a sixteen-fold jump, delivered across every size in the release at once.

What that changed in practice. At 8,192 tokens, an open model served a conversation or a document. At 131,072, it serves a codebase, a corpus, or a session that runs for hours.

Grouped-query attention is what made it affordable, and Meta name it as the reason: an efficient representation that helps with longer contexts by keeping the key-value cache manageable as input grows.

And the whole family moved together — 8B, 70B, and 405B, each in base and instruct form, all at 128K and all across the same eight languages.

A great deal of what followed was built on this. Fine-tunes, merges, adapters, and specialised models across the open ecosystem took Llama 3.1 70B as their base, which is why the name still appears under models that no longer look much like it.

Its Successor Is the Same Model, Post-Trained Better

The comparison worth making first, because it is unusually clean.

Every specification is identical. Same architecture, same window, same language coverage, same knowledge, same cost profile.

The difference is entirely in post-training, and Llama 3.3's card describes what that produced: a model optimised for multilingual dialogue that outperforms many available open source and closed chat models on common benchmarks.

Which makes the decision simple in a way it rarely is. There is no trade to weigh — no capability you give up, no extra footprint, no different licence shape. If both are available to you, the newer one is the newer one.

Two reasons to stay here anyway, and they are real.

A fine-tune or adapter built on this base. LoRA adapters and fine-tunes are trained against specific weights; they do not transfer to a differently post-trained model.

A production path already validated against it. Behaviour differences between post-training runs are real, and a prompt suite tuned against this model is a prompt suite that needs re-validating against another.

Specifications

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

Training cost, for scale: the family consumed 39.3 million GPU hours in total — 7.0 million for the 70B alone, against 1.46 million for the 8B and 30.84 million for the 405B.

Post-training used publicly available instruction datasets plus over 25 million synthetically generated examples, with supervised fine-tuning followed by reinforcement learning from human feedback. Meta developed LLM-based classifiers to filter and curate the data mix — which is what makes twenty-five million synthetic examples viable rather than harmful.

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

Capabilities

Text only. Meta's models became multimodal two generations later; this one has no vision.

⚠️ Knowledge Ends December 2023

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

December 2023 is nearly three years before now. 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 was already seven months old at release. Llama 3.1 shipped in July 2024 against knowledge ending the previous December, and that gap has only widened.

Ground it. Retrieval moves the model from recalling to reading, and reading is where a seventy-billion-parameter model performs closest to a much larger one. With 131,072 tokens of context available, supplying what it needs to know is a budget question rather than a constraint.

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 removes a category of quiet error.

⚠️ Eight Languages, and Arabic Is Not One

English, German, French, Italian, Portuguese, Hindi, Spanish, Thai.

Read the list for what is absent. No Arabic. No Chinese. No Japanese. No Korean. No Russian.

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.

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

If your traffic is outside the eight, that is a reason to look at a different model rather than a detail to work around — and there are models in this catalogue supporting a hundred languages or more.

⚠️ Read the Licence

The Llama 3.1 Community License — a custom commercial licence, not Apache or MIT.

Meta's Llama licences carry conditions permissive ones do not, including an attribution requirement and a monthly-active-user threshold above which separate terms apply. The terms are specific to this version rather than inherited from earlier releases.

Meta shipped safety tooling alongside this release — Llama Guard 3, fine-tuned on Llama 3.1 8B, and Prompt Guard, both released with the family.

Deploying them is a decision, and on a public-facing path it is the right one.

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.

Using Llama 3.1 70B on DEVUP AI

Base URL: https://api.devupai.com/v1 · Model ID: meta-llama/Meta-Llama-3.1-70B-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/Meta-Llama-3.1-70B-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/Meta-Llama-3.1-70B-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/Meta-Llama-3.1-70B-Instruct-Turbo",
    "messages": [
      { "role": "user", "content": "Hello world!" }
    ],
    "max_tokens": 1024
  }'

Grounding Against December 2023

The configuration this model needs for anything factual.

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/Meta-Llama-3.1-70B-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.

And 131,072 tokens is what makes this practical. Supplying enough material that the model rarely needs its own knowledge is a budget question here, not a constraint — which is precisely what the sixteen-fold context increase bought.

Using the Window

The capability this release introduced, on the task that justifies it.

from pathlib import Path

REPO = Path("src")

sources = "\n\n".join(
    f"=== {path.relative_to(REPO.parent)} ===\n{path.read_text(encoding='utf-8')}"
    for path in sorted(REPO.rglob("*.py"))
)

response = client.chat.completions.create(
    model="meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo",
    messages=[
        {
            "role": "system",
            "content": (
                "You are auditing a codebase. Identify every path where a database write can occur "
                "outside a transaction. Name the file, the function, and the call chain that reaches "
                "it. Report nothing you cannot trace."
            ),
        },
        {"role": "user", "content": sources},
    ],
    max_tokens=16384,
)

print(f"input: {response.usage.prompt_tokens:,} of 131,072")

Asking for the call chain rather than the line is what uses a long window rather than a search. A write outside a transaction is easy to grep for; the path reaching it from three modules away needed the whole subsystem in one context.

This request would not have been possible on the previous generation. At 8,192 tokens the codebase does not fit, and the entire class of whole-corpus analysis did not exist for open models until this release.

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/Meta-Llama-3.1-70B-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 December 2023 cutoff. A model answering from memory when a tool was available is answering from information nearly three years old.

Validate arguments before dispatch. Tool calling on this generation is supported rather than specialised, and a malformed argument should fail in your validation rather than in your database layer.

The Base a Great Many Models Were Built On

Worth knowing, because it explains why this name keeps appearing.

Llama 3.1 70B became the default foundation for open fine-tunes through late 2024 and into 2025 — creative models, coding models, domain specialists, and merges across the ecosystem took it as their starting point.

Which produces a practical consequence. If you are running a fine-tune or adapter built on this base, it is bound to these weights. LoRA adapters are trained against specific parameters; they do not transfer to a differently post-trained model, however similar its specifications look.

So "just upgrade to the newer one" is not always available. Where a fine-tune is in the path, the base stays where it is until the fine-tune is retrained — which is a real cost rather than a formality.

Where It Fits

As a fine-tuning base, which is what a great deal of the open ecosystem used it for.

Behind an existing adapter or fine-tune built on these weights, where the base is not a free choice.

On a validated production path, where a prompt suite tuned against this model's behaviour is working and re-validating it has a cost.

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

Long-document analysis across 131,072 tokens with no reasoning overhead to budget.

Multilingual dialogue within the eight supported languages.

Not as a fresh choice where the newer model in this family is available — same size, same window, same cost, better post-training.

Not for current factual knowledge. December 2023.

Not for Arabic, Chinese, Japanese, Korean, or Russian.

Not for images. Text only.

Practical Notes

If you are choosing fresh and have the option, the newer model in this family is the same model with better post-training.

Stay here if a fine-tune, an adapter, or a validated prompt suite is bound to these weights.

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.

Instruct the model to prefer tools over memory, explicitly.

Require quoted evidence on document work.

Read the Llama 3.1 Community License — attribution and a user threshold both apply.

Deploy Llama Guard 3 or an equivalent on any public path.

Limitations

Its successor shares every specification and differs only in post-training. Unless a fine-tune or a validated path binds you here, that is the model to use.

Knowledge ends December 2023 — nearly three years before now, and seven months before this model was released.

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, with an attribution requirement and a monthly-active-user threshold.

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

A July 2024 model. Foundational, widely built upon, and three generations behind Meta's current family in modality, language coverage, and capability.

Confident answers with no visible reasoning. Ground factual work and require an explicit way for the model to say it does not know.