Can I run Qwen3.8 Max Prime locally?

No. The weights are not public and there is no Hugging Face repository; it is available only via API.

What does it cost and how big is the context?

Pricing is $4.00 per million input tokens and $12.00 per million output tokens, with a one-million-token context window.

How does it differ from Qwen3.8 Max?

It is a higher-throughput variant of Qwen3.8 Max served as a separate SKU; capabilities are matched, but the Prime tier costs more and is intended for workloads where throughput or latency matters.

Qwen3.8 Max Prime

1868°

#llm #qwen #vision #video #reasoning #long context

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Qwen | 1,000,000 token context | $4.00/M input, $12.00/M output. Qwen3.8 Max Prime is a higher-throughput variant of Qwen3.8 Max from Alibaba's Qwen team, served as a separate SKU at a higher price point. It accepts text, image, and video input and returns text, with a 1M-token context window and reasoning enabled by default. Tool calling, structured outputs, and configurable reasoning effort are supported, matching Qwen3.8 Max.

Guides for this model

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

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

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

Qwen3.8 Max Prime is a higher-throughput variant of Qwen3.8 Max, built by Alibaba's Qwen team and served as a separate API SKU. It brings the same 1M-token context, reasoning, vision, and video input as its sibling, but at a higher price in exchange for better throughput. It suits teams running latency-sensitive workloads who need the full Max feature set without queuing.

Known for

One-million-token context window for very long documents or codebases
Accepts text, image, and video input in a single model
Reasoning enabled by default, with configurable effort
Tool calling and structured outputs supported out of the box

Watch out for

Closed weights, API-only: no local deployment option.
At $4.00/M input and $12.00/M output, it costs more than Qwen3.8 Max for the same capabilities.
The throughput advantage over the base Max SKU is not quantified by the maker.

Best for

Prompts to try

Reason through a tricky plan

A small logistics company has three vans, seven daily delivery routes, and two drivers who cannot work the same shift. One van needs servicing on Tuesday. Lay out a weekly schedule that covers all routes, respects the constraint, and flags any days where capacity is tight.

Analyse a video or image

Attach a screenshot or short video clip of a dashboard, chart, or scene. Describe what you see in detail, identify any anomalies or points of interest, and suggest one follow-up question worth investigating.

Extract structure from a long doc

Here is a lengthy contract or report. Pull out every obligation, deadline, and named party into a structured table with columns: Party, Obligation, Deadline, Consequence of breach. Flag any clauses that seem ambiguous or contradictory.

Compare with

Qwen3.8 Flash
Open-weights model
1,000,000 token context · $0.15/M input, $0.47/M output
Qwen3.8 27B
Open-weights model
1,000,000 token context · $0.24/M input, $2.20/M output
Qwen3.6 35B A3B
Open-weights model
262,144 token context · $0.15/M input, $1.00/M output
Qwen3.5-9B
Open-weights model
262,144 token context · $0.10/M input, $0.15/M output

More text models from qwen

Questions people ask

Can I run Qwen3.8 Max Prime locally?: No. The weights are not public and there is no Hugging Face repository; it is available only via API.
What does it cost and how big is the context?: Pricing is $4.00 per million input tokens and $12.00 per million output tokens, with a one-million-token context window.
How does it differ from Qwen3.8 Max?: It is a higher-throughput variant of Qwen3.8 Max served as a separate SKU; capabilities are matched, but the Prime tier costs more and is intended for workloads where throughput or latency matters.

Captain's verdict
Pay the premium only if throughput is genuinely the bottleneck; otherwise Qwen3.8 Max does the same job for less.

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

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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.
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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.
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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.
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Typical model: The middle of the catalog: half the models with a figure grew faster over the period, half slower.

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