Can I run Meta Llama 3.1 70B Instruct Turbo locally?

Not this variant — the open weights are not attached to this listing, so you access it through an API only.

What does it cost and how long is the context?

Pricing is $0.40 per million tokens for both input and output, with a context window of 131,072 tokens.

How does the 70B compare to the 8B and 405B in the same family?

The 70B sits between the lightweight 8B and the top-end 405B; it offers meaningfully more capability than the 8B at a fraction of the 405B's cost, making it the practical middle choice for most production workloads.

Meta Llama 3.1 70B Instruct Turbo

3948°

#llm #meta-llama #chat #writing #summarization #long context

Open DeepInfra

meta-llama | 131,072 token context | $0.40/M input, $0.40/M output. Meta developed and released the Meta Llama 3.1 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8B, 70B and 405B sizes

Guides for this model

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All guides

Captain's notes

Written by Captain, the house editor

Notes from 2026-10-13

Meta Llama 3.1 70B Instruct Turbo is a 70-billion-parameter instruction-tuned model from Meta, served via API at a flat $0.40 per million tokens in and out. It sits in the middle of the Llama 3.1 family — more capable than the 8B, cheaper than the 405B — with a 131,072-token context window. It suits developers and teams who want solid general-purpose performance without paying frontier prices.

Known for

Competitive instruction-following at a mid-tier price point
131K token context handles long documents and extended conversations
Part of a three-size family: step up or down without changing your stack
Turbo serving means faster throughput than the standard 70B endpoint

Watch out for

Weights are not publicly available here; you cannot run this version locally.
At 70B it will lag behind the 405B on hard reasoning and complex coding tasks.
Meta has not published the licence terms for this specific served variant.

Best for

Prompts to try

Summarise a long document

Here is a lengthy report. Read it carefully, then give me a structured summary: three sentences on the main argument, a bullet list of the five most important findings, and one sentence on what is missing or unresolved. [Paste your document below this line.]

Draft a clear explanation

Explain how transformer attention mechanisms work to someone who understands basic matrix multiplication but has never studied machine learning. Use a concrete analogy, keep it under 300 words, and end with one sentence on why it matters.

Rewrite for a different audience

Take the following technical changelog and rewrite it as a plain-English email to non-technical stakeholders. Keep every meaningful change, cut all jargon, and flag anything that requires action from the reader. [Paste changelog below.]

Compare with

Llama 3.1 70B Instruct
Open-weights model
131,072 token context · $0.40/M input, $0.40/M output
GPT-4o (2024-11-20)
API-only model
128,000 token context · $2.50/M input, $10.00/M output
GPT-5 Image Mini
API-only model
400,000 token context · $2.50/M input, $2.00/M output
GPT-3.5 Turbo 16k
API-only model
16,385 token context · $3.00/M input, $4.00/M output

More text models from meta-llama

Questions people ask

Can I run Meta Llama 3.1 70B Instruct Turbo locally?: Not this variant — the open weights are not attached to this listing, so you access it through an API only.
What does it cost and how long is the context?: Pricing is $0.40 per million tokens for both input and output, with a context window of 131,072 tokens.
How does the 70B compare to the 8B and 405B in the same family?: The 70B sits between the lightweight 8B and the top-end 405B; it offers meaningfully more capability than the 8B at a fraction of the 405B's cost, making it the practical middle choice for most production workloads.

Captain's verdict
A sensible mid-range workhorse: enough context and capability for most production tasks, at a price that does not require a budget meeting.

Captain writes these notes from the model's own description and model card. Prices and context sizes are the provider's at the time of writing and can change.

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Updated Oct 22, 2026

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How this score adds up

This model has no popularity data from any source yet, so it carries a stand-in number below 7,000° that keeps the lists complete. It never ranks above a model with data. The breakdown appears here as soon as a source reports it.

Glossary: the terms used here

Heat score: One number for how much a model is used and wanted right now, from 0° to 9,999°. Every list on this site that says hottest is sorted by it and nothing else.
Floor: The 7,000° every model with real data starts from. The scale runs from 0° to 9,999°; the part below the floor is reserved for stand-ins (models without data), so that a model with data always ranks above one without. The floor is the same for everyone and decides nothing; the degrees above it are the measurement.
Download-equivalents: Everything the score treats as a download, added up: downloads themselves, each torrent seeder as 1,000, each AI Horde image and each run on a generation site as one.
Log scale: Counting by tens, not by ones: going from 1,000 to 10,000 downloads adds the same degrees as going from 10,000 to 100,000. It keeps giants from crowding out everything else.
Likes: Thumbs up and likes from every source, worth half a download each on the log scale.
Adoption rate: How many downloads a day a model gets right now, from the difference between two readings of a source. A quarter of the weight of the download term.
Measured and estimated: Measured means two readings of a source exist and the difference is real. Estimated means only today's total is known and the past is spread over the model's life; the site says which one it is showing.
Seeder: Someone who keeps a copy of the weights online for others to download over a torrent, or a hosted mirror doing the same. Each one counts as 1,000 downloads.
AI Horde: A volunteer network that generates images for free on donated computers. The number of images it has made with a model is public, and each counts as one download.
Generation sites: Sites that host their own copies of a model and let people generate with it, such as PixAI, SeaArt and Yodayo. Their downloads count like Civitai's and each generation run as one download.
OpenRouter tokens: A text model served through an API, such as Claude or GPT, has no weights to download, so its usage is measured in tokens: the pieces of text it read and wrote through OpenRouter, a gateway many apps use, over the last 30 days. Every 10,000,000 tokens count as one download. Source: OpenRouter (openrouter.ai/rankings), CC BY 4.0.
Full model (checkpoint): The weights themselves, the file that generates on its own. It gets a bonus because add-ons depend on it.
LoRA and embedding: Small add-on files that steer someone else's weights toward a style, a character or a concept. They cannot run alone.
Family and weights: The base a model is built on, such as SD 1.5, SDXL, Flux or Wan. Every model inherits the release date of its family's weights.
Age: Degrees taken off for how long ago the family's weights came out: 150° a year, up to 750°. It keeps old downloads that had years to pile up from freezing the top of the list.
Family boost: A small, bounded nudge for LoRAs of a family this site is pushing forward right now, always listed in the breakdown.
Stand-in score: A stable number below the floor given to a model no source reports on yet, so lists stay complete. It never ranks above a model with data.
New! (grace period): A model less than 7 days old shows New! instead of a score and holds no place in the rankings. Its score is computed from day one, but a model one day old grows faster than anything, so without the wait the week's #1 would always be whatever came out yesterday rather than what held up for a week.
Trend: How a model's growth over a period compares with the typical model's growth over the same period. Faster than typical reads as a rise, slower as a fall.
Typical model: The middle of the catalog: half the models with a figure grew faster over the period, half slower.

How to install

How to Install Ollama and Run a Local AI (Windows, Mac, Linux)

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Meta Llama 3.1 70B Instruct Turbo is a text model for the Text / LLM family listed on ModelTrends.ai, a read-only catalog of open source AI image, video and text models. Compare it with other Text / LLM models and check its heat score to see how it is trending.

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