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Free AI Visibility AuditQwen3.7 Max is the flagship model in Alibaba's Qwen3.7 series, a text-in, text-out model built for agent-centric work such as coding, office and productivity tasks.
QwenVerified 25/09/2026Released 21/05/202601 / 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
$1.48
field median $0.43
Output / 1M tokens
$4.43
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.
Qwen3.7 Max is a text-only large language model positioned at the top of the Qwen3.7 line. It was built for agentic workloads, meaning long chains of tool calls and multi-step tasks rather than single-turn chat, with coding and office or productivity work called out as its strengths. It does not handle images, audio or video, so anything involving document scans, screenshots or speech needs a different model in the pipeline.
Where it sits in its line, and when a sibling is the better pick.
Qwen3.7 Max first appeared in May 2026 as the flagship of the Qwen3.7 series, which means it sits above the smaller and cheaper members of the same family. Reach for it when a task runs long, touches a lot of source material or chains several tools together and you want one model to hold the whole thing. For short, high-volume classification or extraction jobs, a smaller Qwen sibling will do the same work for less.
The API, the apps it powers, and what its limits let you do.
Qwen3.7 Max is reached through Alibaba's Qwen API and the aggregators that resell it, and it also backs the consumer-facing Qwen chat products. The context window runs to a million tokens, which is enough to hold an entire codebase, a quarter's worth of internal documents or a long agent trace without chunking, and the output ceiling is generous enough to write a full file or a long report in one pass. Everything in and out is text.
What changes because this exists, or why it does not.
The combination of a million-token window and a large output allowance means a coding agent can read a whole repository and then write substantial changes without the retrieval scaffolding teams normally have to build and maintain. That was awkward before at this price point. For teams already running Qwen models, it is the step up you take when a workflow outgrows the smaller variants rather than a change of direction.
Follows Qwen3.6 Max Preview. Superseded by Qwen3.7 Plus. 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 | $1.48 | per 1M tokens | 2026-09-24 | Check |
| Output | $4.43 | per 1M tokens | 2026-09-24 | Check |
Qwen3.7 Max is priced in the mid range for a flagship model, well below the Western frontier tier while sitting above the small open-weight options. Given the context window it carries, the cost of stuffing a very large document set into a single call is lower than most models that can hold that much.
| Workload | Input / month | Output / month | Cost |
|---|---|---|---|
| A small product team | 20M tokens | 5M tokens | $51.63 |
| A busy support assistant | 200M tokens | 40M tokens | $472.00 |
| A document pipeline | 1000M tokens | 100M tokens | $1,917.50 |
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 | 131,072 tokens |
| Modalities | text |
| Released | 21/05/2026 |
| Status | Current |
| Catalogue identifier | qwen/qwen3.7-max |
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 Max PreviewCame after
Qwen3.7 Plus →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
If any share of your buyers sits in markets where Qwen powers the default assistant, this model is now part of how your category gets summarised, and it reads plain text only. That puts weight on having your product facts in crawlable HTML rather than in screenshots, diagrams or video. The very large context window also means whole documentation sites can be pulled into a single answer, so gaps and contradictions across your pages are more likely to surface together.
Buyers meet Qwen3.7 Max most often inside the Qwen consumer chat apps and through Alibaba Cloud's model service, where it is the default flagship choice. Developers encounter it through the Qwen API directly or via model marketplaces that carry the series. Its reach is strongest in Chinese-language markets and among teams building on Alibaba Cloud, so it matters more for some buyer bases than others.
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.