Can I run Qwen3.8 27B locally?

Yes, the weights are open and published on Hugging Face at Qwen/Qwen3.8-27B, compatible with vLLM, SGLang, and Hugging Face Transformers. At 27B parameters you will need substantial VRAM; check the VRAM guide linked below before downloading.

What does it cost and how long is the context?

Via API the model is $0.04 per million input tokens and $2.30 per million output tokens, with a one-million-token context window. Running it locally has no per-token cost beyond your own hardware.

What makes this different from earlier Qwen models?

Qwen3.8 is described as the most capable generation in the Qwen open-model family to date, with particular improvements to agent execution, vision-language understanding including video, and the new flexible thinking control system that lets you tune reasoning depth per request.

▲

36.4%

this week

Qwen3.8 27B

8839°

#llm #qwen #vision #video #reasoning #coding

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Qwen | 1,000,000 token context | $0.24/M input, $2.20/M output. Qwen3.8 27B is an open-weight dense vision-language model from Qwen. It is suited for coding, professional workflows, research, multimodal interaction, and long-running agent tasks, with flexible thinking that can be enabled or disabled.

Guides for this model

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All guides

Captain's notes

Written by Captain, the house editor

Notes from 2026-10-13

Qwen3.8 27B is an open-weight dense vision-language model from Qwen, handling text, images, and video within a context window that stretches to one million tokens. It sits in a practical size class for self-hosting while carrying genuine reasoning depth, with thinking mode on by default and tunable per request. It suits developers and researchers who want a capable multimodal agent runner they can actually deploy themselves.

Known for

Flexible thinking control: enable, disable, or tune reasoning depth per request.
Native image and video understanding, including documents and STEM diagrams.
Strong autonomous planning for long-horizon, multi-step agent tasks.
One-million-token context available via the hosted API.
Open weights compatible with vLLM, SGLang, and Hugging Face Transformers.

Watch out for

Native context is 262,144 tokens; the full 1M window requires the hosted Qwen Cloud service, not self-hosted.
Output costs $2.30 per million tokens via API, which adds up fast on long agentic runs.
At 27B parameters, local deployment needs meaningful VRAM; check your hardware before downloading.

Best for

Prompts to try

Debug a failing agent loop

I have a Python agent that calls a search tool, parses the result, and decides whether to call again. After two iterations it always stops, even when the task is unfinished. Here is the loop code: [paste your code]. Identify why it exits early and rewrite the loop so it continues until the task is genuinely complete or a hard limit of ten calls is reached.

Analyse a diagram or chart

I am attaching a technical diagram. Describe what it shows, identify any relationships or flows between components, flag anything that looks inconsistent or unusual, and suggest two questions a domain expert should ask about it.

Plan a multi-step research task

I need to understand the current state of solid-state battery commercialisation: who the leading manufacturers are, what the main technical bottlenecks remain, and which bottlenecks are closest to being solved. Break this into a step-by-step research plan, then work through each step and give me a concise synthesis at the end.

Compare with

Qwen3.7 Flash
API-only model
1,000,000 token context · $0.03/M input, $0.13/M output
Qwen3.8 Max (0902)
API-only model
1,000,000 token context · $2.00/M input, $6.00/M output
Qwen3.8 Omni Flash
API-only model
1,000,000 token context · $0.15/M input, $0.47/M output
Qwen3.5 Plus 2026-02-15
API-only model
1,000,000 token context · $0.26/M input, $1.56/M output

More text models from qwen

Questions people ask

Can I run Qwen3.8 27B locally?: Yes, the weights are open and published on Hugging Face at Qwen/Qwen3.8-27B, compatible with vLLM, SGLang, and Hugging Face Transformers. At 27B parameters you will need substantial VRAM; check the VRAM guide linked below before downloading.
What does it cost and how long is the context?: Via API the model is $0.04 per million input tokens and $2.30 per million output tokens, with a one-million-token context window. Running it locally has no per-token cost beyond your own hardware.
What makes this different from earlier Qwen models?: Qwen3.8 is described as the most capable generation in the Qwen open-model family to date, with particular improvements to agent execution, vision-language understanding including video, and the new flexible thinking control system that lets you tune reasoning depth per request.

Captain's verdict
A serious open-weight multimodal agent model at a size you can actually host, with thinking you can dial up or down.

Model card on Hugging Face

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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Also ranked: #44 Hottest Text / LLM model · #44 Hottest Text / LLM text model

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Qwen3.8 27B Popularity Trend

Heat score since 14 Aug 2026

Heat score

8,839°

This week

▲

36.4%

8,798° then, 8,839° now

This month

▲

426%

8,620° then, 8,839° 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.

+1,553°

OpenRouter tokens

1,505,340,752,896 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, 150,534 in all; the last day's tokens join the adoption rate. Source: OpenRouter (openrouter.ai/rankings).

+286°

Adoption rate

About 6,446 downloads a day, measured between two readings of the sources, never guessed from a lifetime average. Speed weighs a quarter of the download term, so a fresh surge shows without letting a one-day spike outrank years of use.

Show detailed breakdown

Every number on this site can be checked. This model's heat score is 8,839°, 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.
150,534 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.
+1,553°
8,553°
running total
3
Add the adoption rate.
About 6,446 downloads a day right now, measured between two readings, at a quarter of the weight.
+286°
8,839°
running total
=
The heat score.
Where this model stands on every list of this site. Nothing else adjusts it.
8,839°

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.

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Qwen3.8 27B 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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