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Free AI Visibility AuditQwen3 Max is Qwen's flagship text model, released in September 2025 as an updated build on the Qwen3 series. It is a large general-purpose model aimed at reasoning, instruction following and multilingual work, with a context window long enough to hold substantial document sets in a single call.
QwenVerified 25/09/2026Released 23/09/202501 / 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
$0.78
field median $0.43
Output / 1M tokens
$3.90
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 Max is a text-only large language model built for general reasoning, following complex instructions and answering across many languages. Qwen positions it as a step up on long-tail knowledge coverage, the kind of question that sits outside the common cases most models handle well. It does not take images or audio, so anything involving visual input needs a different model in the stack.
Where it sits in its line, and when a sibling is the better pick.
Qwen3 Max first appeared on 23 September 2025 as the top of the Qwen3 line, superseding the January 2025 release it was built from. It is the right pick when a task needs the strongest Qwen reasoning available, particularly across multiple languages or on obscure subject matter. For short, high-volume, low-difficulty calls, a smaller Qwen model will do the same job for less.
The API, the apps it powers, and what its limits let you do.
Qwen3 Max is reached through the Qwen API and is the model behind Qwen's own assistant surfaces. The context window takes a large corpus of text in one pass, a full contract set, a documentation tree or a long conversation history, and the output ceiling is generous enough for long-form drafting and detailed structured responses in a single response rather than a stitched sequence.
What changes because this exists, or why it does not.
Qwen3 Max makes long-document reasoning and multilingual work practical in one model rather than a routing layer between several. The improvement Qwen claims on long-tail knowledge matters for teams working outside English-language, mainstream-topic territory, where general models tend to thin out and hedge.
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 | $0.78 | per 1M tokens | 2026-09-24 | Check |
| Output | $3.90 | per 1M tokens | 2026-09-24 | Check |
Qwen3 Max sits in the mid range on cost, well below the flagship Western frontier models while charging more than the small efficiency models built for bulk work. For most teams it is affordable enough to run on real workloads rather than reserve for difficult cases, though the long context means a careless prompt can still get expensive.
| Workload | Input / month | Output / month | Cost |
|---|---|---|---|
| A small product team | 20M tokens | 5M tokens | $35.10 |
| A busy support assistant | 200M tokens | 40M tokens | $312.00 |
| A document pipeline | 1000M tokens | 100M tokens | $1,170.00 |
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 | 65,536 tokens |
| Modalities | text |
| Released | 23/09/2025 |
| Status | Current |
| Catalogue identifier | qwen/qwen3-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.
Nothing else in this line yet.
A line is worked out from the naming and the release dates across every model page we hold. It fills in as the provider ships successors, or as we pick up the models that came before this one.
How we decide what counts as evidence10 / 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 your buyers sit outside the English-speaking market, Qwen3 Max is a surface worth checking, because it is answering vendor questions for audiences that ChatGPT and Perplexity reach less reliably. The claimed gain on long-tail knowledge means niche category language and less common product terms are more likely to be recognised, which raises the value of clear, consistent naming in your own content. Test your brand prompts in Qwen directly, results there often diverge from what you see on Western engines.
Buyers meet Qwen3 Max in Qwen's consumer chat assistant, where it answers product and vendor questions directly, and through the API where teams build it into their own search and support tools. Its reach is strongest in Chinese-language and wider Asian markets, so it is the model shaping AI answers for a large audience that Western-built assistants cover less well.
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.