Can I run Grok 4.6 locally?

SpaceXAI | 500,000 token context | $2.00/M input, $6.00/M output. Grok 4.6 is a model from SpaceXAI with frontier performance on coding, knowledge work, and STEM. It is succeeded by [Grok 4.7](/x-ai/grok-4.7).

Guides for this model

Grok 4.7 vs GPT-6.1 Sol: same input price, different bills

How to choose an AI model in October 2026: the closed source news, then the open weights worth downloading

Open weights vs open source vs API-only: what you are actually allowed to do

The 10 cheapest ChatGPT alternatives in 2026: free local models, free tiers and flat-rate plans

All guides

Captain's notes

Written by Captain, the house editor

Notes from 2026-10-13

Grok 4.6 is a closed-weights frontier model from xAI built for coding, STEM, and knowledge-intensive work, with a 500,000-token context window and vision capability. It sits one generation behind the current Grok 4.7, which shares the same input price. Worth considering if you need a large context and strong technical performance at a mid-tier API cost.

Known for

Strong performance on coding and STEM tasks
500,000-token context window handles very long documents
Vision support alongside text reasoning
Competitive input price at $2.00 per million tokens

Watch out for

Closed weights only — no local deployment, no offline use
Superseded by Grok 4.7, which you should probably use instead
Output costs $6.00 per million tokens; long generations add up

Best for

Prompts to try

Debug a tricky function

Here is a Python function that is supposed to parse nested JSON from an API response and return a flat dictionary, but it silently drops keys when a value is null. Find the bug, explain why it happens, and give me a corrected version with a brief test case.

Reason through a STEM problem

A satellite in a circular orbit at 400 km altitude fires its thrusters to raise apogee to 1,200 km. Walk me through the delta-v calculation for the Hohmann transfer, state every assumption you make, and flag where real-world factors would change the answer.

Analyse a long document

I am pasting the full text of a technical specification below. Identify every requirement that creates a dependency on an external system, list them in a table with the section number and a one-line summary of the dependency, and flag any that appear contradictory.

Compare with

Grok 4.7
API-only model
500,000 token context · $2.00/M input, $6.00/M output
Grok 4.5
API-only model
500,000 token context · $2.00/M input, $6.00/M output
Grok Build 0.1
API-only model
256,000 token context · $1.00/M input, $2.00/M output
Grok 4.20 Multi-Agent
API-only model
2,000,000 token context · $1.25/M input, $2.50/M output

More text models from x-ai

Questions people ask

Can I run Grok 4.6 locally?: No. Grok 4.6 is API-only; xAI has not released open weights, so local deployment is not possible.
What does Grok 4.6 cost and how large is its context?: It costs $2.00 per million input tokens and $6.00 per million output tokens, with a 500,000-token context window.
How does Grok 4.6 differ from Grok 4.7?: Grok 4.7 is the newer generation and supersedes 4.6; both share the same $2.00 input price, so there is little reason to choose 4.6 for new projects.

Captain's verdict
A capable frontier model that has already been lapped by its successor — use Grok 4.7 unless you have a specific reason not to.

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.

Ranking badge

Updated Oct 22, 2026

Made this model? Show this badge on your page.

Paste it into your Hugging Face card or Civitai description.

Change colors / personalize / get code

Also ranked: #64 Hottest Text / LLM model · #64 Hottest Text / LLM text model

Not this creator?

Get your own badge

Grok 4.6 Popularity Trend

Heat score since 12 Aug 2026

Heat score

8,316°

This week

▲

10.3%

8,302° then, 8,316° now

This month

▲

68.9%

8,244° then, 8,316° now

Past year

n/a

Wasn't released/tracked

7,000°

Floor

The scale runs from 0° to 9,999°. Everything below 7,000° is kept for models no source reports on yet, which carry a stand-in number there so lists stay complete. 7,000° is where measured data begins: a model with real data but almost no downloads sits at the floor, and the measurements fill the degrees above it. The floor says nothing about the model; only the degrees above it do, and they are what separates one model from another.

+1,316°

OpenRouter tokens

242,941,655,085 tokens this model processed through OpenRouter in the last 30 days, prompts and answers together. Every 10,000,000 tokens count as one download in the download term, 24,294 in all; the last day's tokens join the adoption rate. Source: OpenRouter (openrouter.ai/rankings).

Show detailed breakdown

Every number on this site can be checked. This model's heat score is 8,316°, and here it is built one ingredient at a time, with the running total after each: the floor, the degrees each source adds, the bonus for the kind of model and what the age of its weights takes away. The colors are the bands of the chart above. The glossary at the end defines every term.

1
Start at the floor.
Why 7,000° and not zero: the scale runs from 0° to 9,999°, and the part below 7,000° is reserved for models no source reports on yet, which carry a stand-in number there so the lists stay complete and never outrank a model with data. 7,000° is where measured data begins. A model with real data but almost no downloads sits here; everything that follows is measurement, and the room above the floor holds ten tenfold steps in downloads at 300° each. The floor is the same for every model, so it decides nothing; the degrees above it do.
7,000°
running total
2
Add the downloads.
24,294 download-equivalents in all, from OpenRouter tokens. Ten times the downloads adds the same 300°, so size matters but never runs away with the score.
+1,316°
8,316°
running total
=
The heat score.
Where this model stands on every list of this site. Nothing else adjusts it.
8,316°

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 this model's popularity has moved against the rest of the catalog: its growth in downloads, likes and usage over each period, measured against the typical model's growth over the same period, with its heat score at the time. Above the typical growth reads as a rise, below it as a fall. The past is reconstructed from today's totals, the model's first day and its measured download speed where two readings exist; a model that did not exist at the start of a period shows n/a.

Similar Models

AI Models for Image, Video & Text Generation

Grok 4.6 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.

More Text / LLM models llm x-ai vision reasoning coding long context research

Browse by Model Family