Qwen

• Qwen, Alibaba's model family, huge open-weight range, closed flagship.
• - The most prolific open-weight family, from tiny edge models to mixture-of-experts systems
• Top-end Max tier held back as API-only
• Served through Alibaba Cloud Model Studio, with regional coverage including Singapore and Sydney
• OpenAI-compatible endpoint 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.
• 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.

• On the fields we score, Qwen does not separate from its roster in either direction.
• It is neither top nor bottom on anything weighted, and nothing it does is rare enough among its rivals to count as a differentiator.
• That is a real finding rather than a gap: it usually means a competent generalist, and the decision comes down to the qualitative fields further down this page.

• Provenance. 22 of 27 tracked fields carry a value for Qwen, and 10 of those cite a document you can open.
• Last verified 2026-07-30.
• 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 Schema output (Yes against Partial).
• 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

22 of 27

Last verified

2026-07-30

The best open-weight range, a model at nearly every size and modality; the hosted Max tier is rarely the reason to be here.

$/M output

$7.5/M tok5th of 9

Vendor-claimedverified 2026-07-30source

$/M input

$2.5/M tok6th of 9

Vendor-claimedverified 2026-07-30source

$/M cache read

$0.25/M tok4th of 8

Inferredverified 2026-07-30source

Tool use

●Yes1st of 18

Vendor-claimedverified 2026-07-30source

Pinnable versions

●Yes1st of 18

Inferredverified 2026-01-15

Context window

1,000,000tokens1st of 12

Vendor-claimedverified 2026-07-30source

OpenAI-compat API

●Yes1st of 18

Vendor-claimedverified 2026-07-30source

Batch discount

50%1st of 10

Vendor-claimedverified 2026-07-30source

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.

• Nothing in our data disqualifies Qwen on a field we score, it is not bottom of its roster on anything weighted.
• That does not make it universally correct.
• The honest failure modes for a tool in this position are commercial rather than technical: pricing that changes under you, a licence that changes under you, or a roadmap that drifts away from your use case.
• Check the timeline for changes we have logged, and Qwen alternatives for what teams move to when it stops fitting.

What we don't know.

• 5 of the 27 fields we track for Qwen are still blank: Max output, TTFT p50, Output tok/s, 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.