Qwen

Qwen — Alibaba's model family — huge open-weight range, closed flagship. Qwen is the most prolific open-weight family in the category, spanning tiny edge models to mixture-of-experts systems, with the top-end Max tier held back as API-only. Served through Alibaba Cloud Model Studio, which brings real regional coverage including Singapore and Sydney, and an OpenAI-compatible endpoint that makes evaluation cheap. Built by Alibaba Cloud. Founded 2009. Open source under Apache-2.0 (most open models).

We track it in 1 comparison — LLM APIs — so every claim below is a cell in a table you can open and check rather than an impression. Across those rosters it sits against 17 other tools, and what follows is where it visibly separates from them.

Where it gives ground.

• Context window — 262,144 tokens, 6th of 10. Anthropic records 1,000,000 tokens.

None of these disqualify it on their own. They are the fields to check against your own requirements before you commit, because they are the ones where a competitor genuinely does better.

Provenance. 20 of 27 tracked fields carry a value for Qwen, and 2 of those cite a document you can open. Last verified 2026-01-15. Every figure keeps its own provenance — measured by us, claimed by the vendor, inferred, or community-reported — and we would rather print a dash than a guess.

Its nearest neighbour in our data is Amazon Bedrock. Qwen is ahead on OpenAI-compat API (Yes against No) and Trains on your data (Partial against No). Amazon Bedrock takes Computer use (Yes against No) and Context window (1,000,000 tokens against 262,144 tokens). That pattern repeats across the rest of the roster — see Qwen alternatives for the other rivals, each compared the same way.

Company

Alibaba Cloud

Founded

2009

Licence

Apache-2.0 (most open models) (open source)

Fields we track

20 of 27

Last verified

2026-01-15

The best open-weight range in the category — there is a Qwen model at nearly every size and modality, mostly Apache 2.0. The hosted Max tier is a reasonable mid-price flagship but rarely the reason to be here; most teams use the open weights through a Western host and treat Model Studio as optional.

$/M output

$6 /M tok4th of 8

Inferredverified 2026-01-15

$/M input

$1.2 /M tok4th of 8

Inferredverified 2026-01-15

Context window

262,144 tokens6th of 10

Inferredverified 2026-01-15

Tool use

●Yes1st of 18

Inferredverified 2026-01-15

Pinnable versions

●Yes1st of 18

Inferredverified 2026-01-15

OpenAI-compat API

●Yes1st of 18

Vendor-claimedverified 2026-01-15source

Batch discount

50 %1st of 10

Inferredverified 2026-01-15

Computer use

○No5th of 18

Inferredverified 2026-01-15

Qwen is the right call in these situations, each one drawn from a field we actually record:

• Finding an open-weight model at a specific size or modality.
• Fine-tuning where a permissive licence and a model-size ladder both matter.
• Asia-Pacific deployments already on Alibaba Cloud.

Reach for something else when any of the following is a requirement rather than a nice-to-have:

• Context window. Qwen records 262,144 tokens, 6th of 10 in the LLM APIs roster. Anthropic records 1,000,000 tokens on the same field. If that is a hard requirement rather than a preference, start elsewhere.

We publish this block because a comparison that only lists what a tool is good at is marketing. Every figure above sits on the same page as its source, and the field definitions are on the category tables if you want to check how we measured them.

What we don't know. 7 of the 27 fields we track for Qwen are still blank: Max output, TTFT p50, Output tok/s, $/M cache read, Cache TTL, Notice period and Zero retention. Those render as dashes rather than as zeroes or assumptions, because an empty cell and a bad cell are not the same thing and only one of them is honest. If you know any of these figures and can point at a document, tell us.