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Free AI Visibility AuditQwen3.6 27B is a dense 27-billion-parameter model from the Qwen team at Alibaba that accepts text, image and video input and returns text. It was first seen in April 2026 and sits in the mid-size band of the Qwen line, where the cost per call is low enough to run high-volume work.
QwenVerified 25/09/2026Released 27/04/202601 / decision
Each figure sits next to the middle of the field, so it can be read as dear or cheap, wide or narrow, rather than floating on its own. Compared against the 231 models we track, not against an absolute standard.
Capability
Not measured
No published benchmark scores yet
Input / 1M tokens
$0.32
field median $0.42
Output / 1M tokens
$2.70
field median $1.81
Context
262K
field median 500K
02 / overview
Four questions, answered separately, because somebody arrives at one of them rather than at the top of the page.
The job it was built for, and the job it is not for.
Qwen3.6 27B is a general-purpose multimodal language model built to read text, images and video and answer in text. The job it was made for is bulk understanding work, summarising, extracting, classifying and answering over mixed material, rather than frontier reasoning. It is not an image or video generator, and it is not the model to reach for when you need the largest model in a family to carry a hard problem alone.
Where it sits in its line, and when a sibling is the better pick.
Qwen3.6 27B arrived in April 2026 as the dense 27-billion-parameter member of the Qwen3.6 family. It is the right pick when the work is routine but the volume is large, or when the inputs include screenshots and video clips that a text-only model cannot see. It is the wrong pick for problems where you would otherwise choose the biggest model in the range and accept the cost, because a smaller dense model will not close that gap.
The API, the apps it powers, and what its limits let you do.
Qwen3.6 27B is reached through an API, taking text, image and video in and returning text out. Its context window runs to a quarter of a million tokens and its maximum output is effectively the same size, so a long document set, a transcript with frames attached, or a whole codebase section can go in and come back out rewritten in one pass rather than being chunked. That symmetry between input and output length is the practical detail: whole-document rewrites and long structured extractions do not need stitching.
What changes because this exists, or why it does not.
Qwen3.6 27B makes two things cheaper at once that used to be separate decisions: reading video and images, and reading a very long context. A team that previously ran a transcription step, then a text model, then a separate vision call can collapse that into one request. Nothing here is new in kind, the change is that the price point makes it reasonable to run across an entire content library rather than a sample.
Follows Qwen3.5 Plus 2026-04-20. Superseded by Qwen3.6 35B A3B. See the whole line.
03 / evidence
Split by category, so a strong number never hides a thin evidence base. Verified means we read it on the benchmark's own published results; a provider's claim about its own model is shown and labelled rather than dropped.
No published benchmark scores for this model yet.
We publish a score only where we can link the result to where it was published. Until a benchmark result for this model exists in a source we read, this section stays empty rather than being filled with a provider's marketing figure.
How we decide what counts as evidence04 / ledger
Every published row, grouped by category, each compared with the best published score on the same benchmark. 'Is 64% good' is a question nobody can answer; '26 points behind the leader' is one anybody can.
Nothing in the ledger yet.
Each row here carries a score, the benchmark it came from, what the leading model scored on the same test, and a link to the published result. Rows appear as results are published and read.
How we decide what counts as evidence05 / capability
Where this model is strong, and against how many peers. Ranks are against models with evidence in that category, not against everything we track: ranking against models nobody tested would rank who published, not who is better.
No category scores to shape yet.
A category score is the weighted mean of the benchmarks published for it. With no published rows there is nothing to average, and an empty chart drawn at zero would say something false.
How we decide what counts as evidence06 / cost
List API rates as last read from the provider, with the source on every row, plus every change we have recorded since we started tracking it.
| Charge | Price | Unit | Read on | Source |
|---|---|---|---|---|
| Input | $0.32 | per 1M tokens | 2026-09-24 | Check |
| Output | $2.70 | per 1M tokens | 2026-09-24 | Check |
Qwen3.6 27B is priced to be run in bulk, with input costing a small fraction of what frontier models charge and output staying modest. Against the rest of the field it sits firmly in the cheap, high-throughput tier, which is what makes whole-library passes and per-page processing affordable rather than something you budget for.
| Workload | Input / month | Output / month | Cost |
|---|---|---|---|
| A small product team | 20M tokens | 5M tokens | $19.90 |
| A busy support assistant | 200M tokens | 40M tokens | $172.00 |
| A document pipeline | 1000M tokens | 100M tokens | $590.00 |
List API rates, no caching and no batch discount, which both providers offer and which change the answer a great deal. Treat these as the ceiling, not the bill.
07 / specs
As listed by the provider's own catalogue and re-read every few hours. Anything absent is absent there too.
| Context window | 262,144 tokens |
|---|---|
| Maximum output | 262,140 tokens |
| Modalities | text, image, video |
| Released | 27/04/2026 |
| Status | Current |
| Catalogue identifier | qwen/qwen3.6-27b |
08 / lineage
What this model replaced, what replaced it, and what else its provider has in the field.
Also from Qwen
09 / line
Every model in this family in release order, so a page from eight months ago says in one glance that two newer ones exist.
Came before
← Qwen3.5 Plus 2026-04-20Came after
Qwen3.6 35B A3B →Ordered by release date and worked out from the naming, so a new member slots in as soon as its page exists. A retired model keeps its page and its place in the line.
10 / notes
What this model changes for a brand trying to be cited in AI answers, and every change we have logged since it launched.
What it changes for you
Models at this price get used for the unglamorous, high-volume layer of AI search, the crawling, summarising and classifying of pages before anything reaches a user. That means your product pages are increasingly being read, and judged, by a mid-size model working at speed rather than a frontier one reasoning carefully, so clear claims, plain structure and facts stated on the page matter more than nuance. If your positioning only makes sense to a careful reader, it will not survive the summarisation step.
Most buyers meet Qwen3.6 27B through the API rather than a branded consumer app, either direct from Alibaba's cloud or through the model catalogues and open routers that carry Qwen releases. It also turns up inside other people's products, as the model quietly running a support assistant, a document tool or a search feature that the buyer never sees named. In practice that means it shapes answers a buyer reads without ever appearing in the interface.
Nothing published here yet. Changes appear within hours of a provider announcing them.
11 / questions
The things people ask about this model, answered from what is on this page rather than from anywhere else.
Models change what gets cited. We measure whether AI answers name your brand or your competitors across every assistant, and show you what to change.