Can I run Qwen3.7-Max locally?

No. Qwen3.7-Max is a closed-weights, API-only model; Alibaba has not released the weights for download or local use.

What does Qwen3.7-Max cost and how large is its context?

Pricing is $1.48 per million input tokens and $4.42 per million output tokens, with a one-million-token context window and prompt caching available to reduce costs on repeated inputs.

How does Qwen3.7-Max differ from other models in the Qwen3.7 series?

It is the flagship of the series, positioned for the most demanding agentic and coding workloads; Alibaba describes it as offering notable gains over prior Qwen generations in those areas, though the maker has not published detailed comparisons with smaller siblings.

Qwen3.7 Max

2598°

#llm #qwen #reasoning #coding #agents #long context

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Qwen | 1,000,000 token context | $1.48/M input, $4.42/M output. Qwen3.7-Max is the flagship model in Alibaba's Qwen3.7 series. It supports text input and output and is designed for agent-centric workloads, with particular strengths in coding, office and productivity tasks, and long-horizon autonomous execution. The model offers notable gains in coding and agentic performance over prior Qwen generations and supports explicit prompt caching for efficient repeated context use.

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Captain's notes

Written by Captain, the house editor

Notes from 2026-10-13

Qwen3.7-Max is Alibaba's flagship text model in the Qwen3.7 series, built with agentic workloads at its centre: long-horizon autonomous tasks, coding, and office productivity. It offers a one-million-token context window and explicit prompt caching, which matters when you are feeding the same large document repeatedly. Closed weights, API-only, so you are renting it by the token.

Known for

Strong coding performance, improved over prior Qwen generations
Designed for autonomous, multi-step agent execution
One-million-token context window for very large inputs
Prompt caching support reduces cost on repeated context
Office and productivity task focus alongside code

Watch out for

Closed weights, API-only — no local deployment option
Output tokens cost roughly three times the input rate; long agentic runs add up fast
No model card published; internal architecture details are not available

Best for

Prompts to try

Multi-step repo audit

You are working autonomously. Given the following Python codebase, identify all functions that make external HTTP calls, check whether each one handles timeouts and errors, and produce a prioritised list of fixes with example corrected code for the top three issues. Paste your code below.

Long document extraction

I am pasting a lengthy contract below. Read the entire document, then extract: all payment terms, all termination clauses, and any clauses that limit liability. Present each category as a numbered list with the exact section reference from the document.

Agent task planning

Plan and execute the following autonomously: research the top five open-source vector databases by GitHub stars, compare their licensing terms, query latency claims, and Python SDK maturity, then produce a recommendation table and a one-paragraph verdict on which suits a small startup with limited ops capacity.

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 Coder Next
Open-weights model
262,144 token context · $0.12/M input, $0.80/M output

More text models from qwen

Questions people ask

Can I run Qwen3.7-Max locally?: No. Qwen3.7-Max is a closed-weights, API-only model; Alibaba has not released the weights for download or local use.
What does Qwen3.7-Max cost and how large is its context?: Pricing is $1.48 per million input tokens and $4.42 per million output tokens, with a one-million-token context window and prompt caching available to reduce costs on repeated inputs.
How does Qwen3.7-Max differ from other models in the Qwen3.7 series?: It is the flagship of the series, positioned for the most demanding agentic and coding workloads; Alibaba describes it as offering notable gains over prior Qwen generations in those areas, though the maker has not published detailed comparisons with smaller siblings.

Captain's verdict
A serious agentic coding model with a genuinely useful million-token window, priced reasonably on input but watch the output bill on long runs.

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.
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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.
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.

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