Is Llama 3.1 8B Instruct open source?

Meta publishes the weights for Llama 3.1 8B Instruct, so you can download it and run it on your own infrastructure rather than calling a hosted API. That is the main reason teams with data-residency constraints choose it.

What is Llama 3.1 8B Instruct good at?

High-volume text work: classification, extraction, summarising, rewriting and routing. It is fast and cheap enough to run on every record in a pipeline, which is a different job from the deep reasoning tasks you would give a larger model.

Can Llama 3.1 8B Instruct handle images?

No. It is a text-only model, so it takes text in and produces text out. Documents need to be converted to text before they reach it.

When should I use a larger Llama 3.1 model instead?

When the task needs sustained reasoning, careful judgement or reliable accuracy on hard problems. A common pattern is to run the 8B size for the bulk of the traffic and escalate the small share of difficult cases to a bigger model.

Meta model°

Llama 3.1 8B Instruct

Llama 3.1 8B Instruct is Meta's small open-weight instruction-tuned language model, the lightest member of the Llama 3.1 family, built for fast text tasks at very low cost.

MetaVerified 24/09/2026Released 23/07/2024

01 / decision

Decision snapshot

Each figure sits next to the middle of the field, so it can be read as dear or cheap, wide or narrow, rather than floating on its own. Compared against the 231 models we track, not against an absolute standard.

Capability

Not measured

No published benchmark scores yet

Input / 1M tokens

$0.05

field median $0.43

Output / 1M tokens

$0.08

field median $1.80

Context

131K

field median 500K

02 / overview

What Llama 3.1 8B Instruct is, and when to reach for it

Four questions, answered separately, because somebody arrives at one of them rather than at the top of the page.

What it is

The job it was built for, and the job it is not for.

Llama 3.1 8B Instruct is a text-only, instruction-tuned model, meaning it takes a prompt and follows it rather than simply continuing text. It is built for high-volume work where speed and cost matter more than depth: classification, extraction, summarising, routing, rewriting, and other jobs that run thousands of times a day. It is not the model to reach for when a task needs long chains of reasoning, and it does not handle images or audio.

When it arrived, and when to use it

Where it sits in its line, and when a sibling is the better pick.

Llama 3.1 8B Instruct arrived in July 2024, released alongside the larger sizes in Meta's Llama 3.1 class. Within that family it is the entry point: the right pick when you are running a task at scale, when latency is the constraint, or when you want to self-host on modest hardware. It is the wrong pick for the hardest reasoning problems, where a larger Llama 3.1 size or a frontier model earns its higher cost.

How you reach it

The API, the apps it powers, and what its limits let you do.

Llama 3.1 8B Instruct is served through most hosted inference APIs and can also be run on your own infrastructure, since Meta publishes the weights. Its context window stretches to well over a hundred thousand tokens, so a long document, a full support thread or a large batch of records can go in one call and come back summarised or structured. Output is text only, and the generous output limit means it can also produce long-form results rather than short snippets.

Why it matters

What changes because this exists, or why it does not.

Llama 3.1 8B Instruct made it practical to put a language model in the middle of a pipeline rather than at the end of a conversation. Work that was previously too expensive to run on every record, tagging every ticket, summarising every call, normalising every product feed, becomes routine at this price point. The open weights also mean a team can run it inside its own boundary, which settles a lot of data-residency arguments before they start.

Superseded by Llama 4 Maverick. See the whole line.

03 / evidence

How much of this is verified

Split by category, so a strong number never hides a thin evidence base. Verified means we read it on the benchmark's own published results; a provider's claim about its own model is shown and labelled rather than dropped.

No published benchmark scores for this model yet.

We publish a score only where we can link the result to where it was published. Until a benchmark result for this model exists in a source we read, this section stays empty rather than being filled with a provider's marketing figure.

How we decide what counts as evidence

04 / ledger

Benchmark ledger

Every published row, grouped by category, each compared with the best published score on the same benchmark. 'Is 64% good' is a question nobody can answer; '26 points behind the leader' is one anybody can.

Nothing in the ledger yet.

Each row here carries a score, the benchmark it came from, what the leading model scored on the same test, and a link to the published result. Rows appear as results are published and read.

How we decide what counts as evidence

05 / capability

Capability shape

Where this model is strong, and against how many peers. Ranks are against models with evidence in that category, not against everything we track: ranking against models nobody tested would rank who published, not who is better.

No category scores to shape yet.

A category score is the weighted mean of the benchmarks published for it. With no published rows there is nothing to average, and an empty chart drawn at zero would say something false.

How we decide what counts as evidence

06 / cost

What it costs

List API rates as last read from the provider, with the source on every row, plus every change we have recorded since we started tracking it.

Llama 3.1 8B Instruct API pricingSurge45°
Charge	Price	Unit	Read on	Source
Input	$0.05	per 1M tokens	2026-09-24	Check
Output	$0.08	per 1M tokens	2026-09-24	Check

Llama 3.1 8B Instruct sits at the very bottom of the cost range, cheap enough that inference stops being a line item you plan around. Against mid-sized and frontier models it costs a small fraction per unit of work, which is why it tends to be used for the bulk jobs while a larger model handles the few calls that genuinely need it.

What a month costsSurge45°
Workload	Input / month	Output / month	Cost
A small product team	20M tokens	5M tokens	$1.40
A busy support assistant	200M tokens	40M tokens	$13.20
A document pipeline	1000M tokens	100M tokens	$58.00

List API rates, no caching and no batch discount, which both providers offer and which change the answer a great deal. Treat these as the ceiling, not the bill.

07 / specs

Specifications

As listed by the provider's own catalogue and re-read every few hours. Anything absent is absent there too.

SpecificationSurge45°
Context window	131,072 tokens
Maximum output	117,964 tokens
Modalities	text
Released	23/07/2024
Status	Current
Catalogue identifier	meta-llama/llama-3.1-8b-instruct

08 / lineage

Lineage

What this model replaced, what replaced it, and what else its provider has in the field.

Also from Meta

09 / line

The line

Every model in this family in release order, so a page from eight months ago says in one glance that two newer ones exist.

Came before

The first we track in this line.

Came after

Llama 4 Maverick →

01Llama 3.1 8B Instruct23/07/2024
02Llama 4 Maverick05/04/2025
03Llama 4 Scout05/04/2025

Ordered by release date and worked out from the naming, so a new member slots in as soon as its page exists. A retired model keeps its page and its place in the line.

10 / notes

Our notes

What this model changes for a brand trying to be cited in AI answers, and every change we have logged since it launched.

What it changes for you

Cheap small models like Llama 3.1 8B Instruct are what let AI features spread across a product rather than sit in one chat box, so more of your category's software now reads, summarises and classifies text by default. For a SaaS brand being cited in AI answers, the practical effect is volume: far more automated summarising and extraction is happening over your public pages, docs and comparison content. Write so that a fast, small model reading one page in isolation can still state plainly what you do and who you are for.

Where buyers meet this model

Buyers rarely meet Llama 3.1 8B Instruct by name. They meet it as the thing quietly powering a chat widget, an in-app summary, a search suggestion or a classification step inside a SaaS product, and as a cheap default option on hosted inference platforms and cloud model catalogues. Developers encounter it directly through those APIs or by pulling the weights and serving them themselves.

Change log

Nothing published here yet. Changes appear within hours of a provider announcing them.

11 / questions

Questions

The things people ask about this model, answered from what is on this page rather than from anywhere else.

Is Llama 3.1 8B Instruct open source?: Meta publishes the weights for Llama 3.1 8B Instruct, so you can download it and run it on your own infrastructure rather than calling a hosted API. That is the main reason teams with data-residency constraints choose it.
What is Llama 3.1 8B Instruct good at?: High-volume text work: classification, extraction, summarising, rewriting and routing. It is fast and cheap enough to run on every record in a pipeline, which is a different job from the deep reasoning tasks you would give a larger model.
Can Llama 3.1 8B Instruct handle images?: No. It is a text-only model, so it takes text in and produces text out. Documents need to be converted to text before they reach it.
When should I use a larger Llama 3.1 model instead?: When the task needs sustained reasoning, careful judgement or reliable accuracy on hard problems. A common pattern is to run the 8B size for the bulk of the traffic and escalate the small share of difficult cases to a bigger model.

Surge45°

Is Llama 3.1 8B Instruct recommending you?

Models change what gets cited. We measure whether AI answers name your brand or your competitors across every assistant, and show you what to change.

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