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Free AI Visibility AuditQwen3.6 Flash is the fast, low-cost model in Alibaba's Qwen 3.6 series, built for high-volume text, image and video input work at a very long context length.
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
$0.19
field median $0.43
Output / 1M tokens
$1.13
field median $1.81
Context
1,000K
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 Flash is a speed-and-cost tier language model that accepts text, images and video as input and returns text. It is built for work you run a lot of, classification, extraction, summarising long documents or media, and routine chat, rather than for the hardest reasoning jobs in a product. If a task needs the strongest answer rather than the cheapest one, this is not the tier to reach for.
Where it sits in its line, and when a sibling is the better pick.
Qwen3.6 Flash was first seen on 27 April 2026 as the efficiency tier of the Qwen 3.6 family. It is the right pick when volume, latency or cost dominate, and when the input is long or mixed media rather than intellectually hard. It is the wrong pick for deep multi-step reasoning or anything where a weak answer is expensive, where a heavier sibling in the series earns its keep.
The API, the apps it powers, and what its limits let you do.
Qwen3.6 Flash is reached through the Qwen API and the providers that resell the Qwen line. Its million token context means you can put whole document sets, long transcripts or video alongside your instructions in a single call instead of building a retrieval layer first, and its output ceiling is generous enough for long structured responses such as full reports or large JSON payloads. Video and image input arrive in the same request as text, so media review and the write-up that follows happen in one step.
What changes because this exists, or why it does not.
A million token window at a cheap tier changes what teams bother to engineer. Pipelines that previously needed chunking, embedding and reranking can often be replaced by one long call, and media understanding stops being a separate service you bolt on. The practical effect is that bulk work which was uneconomic at frontier prices becomes routine.
Follows Qwen3.6 35B A3B. Superseded by Qwen3.6 Max Preview. 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 | $0.19 | per 1M tokens | 2026-09-24 | Check |
| Output | $1.13 | per 1M tokens | 2026-09-24 | Check |
Qwen3.6 Flash sits at the cheap end of the market, with output costing several times input, as is normal, and tiered pricing applying as usage grows. Against frontier models it is an order of magnitude cheaper to run, which is the point of the tier: you choose it when you are making millions of calls, not thousands.
| Workload | Input / month | Output / month | Cost |
|---|---|---|---|
| A small product team | 20M tokens | 5M tokens | $9.38 |
| A busy support assistant | 200M tokens | 40M tokens | $82.50 |
| A document pipeline | 1000M tokens | 100M tokens | $300.00 |
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 | 1,000,000 tokens |
|---|---|
| Maximum output | 65,536 tokens |
| Modalities | text, image, video |
| Released | 27/04/2026 |
| Status | Current |
| Catalogue identifier | qwen/qwen3.6-flash |
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
← Qwen3.6 35B A3BCame after
Qwen3.6 Max Preview →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
Cheap long-context models mean more AI products can afford to read your entire site, docs and support content rather than a few retrieved snippets, so thin or inconsistent pages get seen in full. Make sure your product explanations, pricing logic and comparison content hold up when read end to end, not just in isolation. And because this tier reads images and video, the claims in your demos and screenshots are now part of what a model learns about you.
Most buyers meet Qwen3.6 Flash indirectly, through the Qwen API inside a product someone else built, or through Qwen's own chat surface and the model catalogues that carry the Qwen line. Because it is cheap and fast, it tends to be the model doing the unglamorous work behind a feature, the summarising, tagging and routing, rather than the one named on the pricing page.
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.