Alibaba's Qwen team says Qwen4 is coming, opens Qwen4-Max preview

Alibaba’s Qwen team said on November 11, 2026 that Qwen4 is coming, and it has put the flagship Qwen4-Max into preview on Alibaba’s Token Plan. The team says the new generation brings “deeper knowledge,” “richer world understanding” and “stronger coding and agentic capabilities,” and is built for complex coding, knowledge work, front-end builds and professional software workflows.

The post confirms a model family Alibaba first showed on stage in September. It does not include a model card, benchmark results, prices or a date for the rest of the family.

What the Qwen team announced on November 11

The announcement is a short post on the official Qwen account on X. It makes four claims about Qwen4: deeper knowledge, richer world understanding, stronger coding and stronger agentic abilities. “Agentic” here means a model that plans and carries out multi-step tasks, often by calling tools, rather than answering a single prompt. The post sets out the target workloads as “complex coding and knowledge work to front-end builds and professional software workflows”.

The practical news is access. The post says “Qwen4-Max is now in preview on Alibaba’s Token Plan,” and that early access to the new model is also available on QwenWork, Qoder, Qwen APP and QwenStudio. The team closes with an invitation to developers: “Put it to the test on a real problem. Show us what you build.”

This follows a shorter teaser five days earlier. On November 6, the Qwen account posted “The becoming begins.” A reply to a post quoting that teaser says it was posted at 7:00 PM and showed an image of a flock of birds with the line 北冥有鱼 其名为_. Medium’s Data Science in Your Pocket described that November 6 post as a “stealth” confirmation with “no livestreamed keynote, no benchmark wall, no pricing slide”.

Where Qwen4-Max can be tried, and what is still missing

Based on the Qwen post, there are five ways to try the new model today: the Token Plan preview of Qwen4-Max, plus early access through QwenWork, Qoder, Qwen APP and QwenStudio. The post does not give a public API model identifier, rate limits, regions or a general-availability date.

Independent trackers have nothing to measure yet. BenchLM’s comparison page, updated November 11, shows Qwen4 Max with no public benchmark score, says its context window and release date are “Not sourced,” and lists no comparable API price. Because of that, BenchLM declines to name a winner against Qwen3.7 Plus on coding, agentic work or long documents.

Medium’s write-up lists the same gaps for the whole family: no release date, no benchmarks for the final Qwen4 checkpoints, no pricing or rate limits for any tier, and an unknown licence mix. It warns that any score circulating before launch “traces back to the Flash-Next preview or the Kalmia canary, not the shipping family”. Kie.ai’s analysis, written in October, makes the same point about Qwen4-Max: no first-party model card, benchmark, context window or price existed for any Qwen4 tier.

How Qwen4 got here: two months of stage slides and code

Qwen4 has been signalled publicly since September. Alibaba’s coverage trail runs as follows:

• September 22, Apsara Conference, Hangzhou. Qwen LLM lead Liu Dayiheng presented the Qwen4 family, positioned Qwen4-Max as the flagship, and said Qwen4 was already in training on a next-generation architecture. Medium reports he said it would arrive “very soon”. TestingCatalog reported that Max, Flash, Plus and 27B models had been announced.
• October 22. An unreleased model codenamed “Kalmia” began appearing in anonymous arena battles, and testers said its reasoning matched the known Qwen4 architecture preview, according to Medium.
• October 28. Qwen Code, Alibaba’s coding tool, merged 1-million-token context sizing for every qwen4 model identifier, according to Medium. One developer on X also noted that Qwen Code lists Qwen4 with a 1M context.
• October 29, Apsara Amsterdam. A slide read “Qwen4 Series Coming Soon” and named all four tiers. Alibaba Cloud CTO Feifei Li reportedly told attendees the launch was “next week”.
• November 6. The “The becoming begins.” teaser on the Qwen account.
• November 11. The “Qwen4 is coming” post and the Qwen4-Max preview.

The open-source inference software was also preparing ahead of time. Medium reports that llama.cpp merged support for a “qwen4exp” architecture in late August. It says, separately and without giving a date, that vLLM and Hugging Face Transformers carried qwen4 code paths and tests “well before any weights carried the name”.

Not every detail on this trail is official. Kie.ai notes that the four tier names came from a captured stage slide and do not appear in Alibaba’s published September 22 press release, which says only that Qwen4 “is currently in training”. Kie.ai’s piece also cited one community estimate pointing to early 2027 for the release. The November 11 preview has overtaken that estimate, at least for the Max tier.

What Qwen3.8-Flash-Next suggests about Qwen4’s design

Alibaba has not published a Qwen4 architecture. The best available guide is Qwen3.8-Flash-Next, which Alibaba open-sourced on August 28, 2026. Qwen’s own materials call it an experimental preview of the architecture that will underpin Qwen4. Medium says the model card uses the same description.

Flash-Next is a mixture-of-experts (MoE) model. An MoE model contains many specialised sub-networks, called “experts,” and only a few of them run for each token, which keeps compute low compared with the model’s total size. These figures describe the preview checkpoint, not Qwen4-Max:

Qwen3.8-Flash-Next (Qwen4 architecture preview)	As reported
Main parameters	125B, with 6B active per token
Experts	512; community teardowns report top-10 routing
Memory layer	An extra 51B parameters of N-gram embeddings
Attention	Gated DeltaNet linear attention plus Qwen Sparse Attention
Layers	48: 36 Gated DeltaNet and 12 attention, per community teardowns
Context window	262K tokens native, extendable to 1M with YaRN scaling
Training cost	About one ninth of Qwen3.7-Plus, per Alibaba’s claim

The most unusual element is the N-gram embedding table, which Medium describes as “a giant lookup table that adds capacity without dense compute”. Kie.ai notes that community teardowns count the served checkpoint as 176B total parameters. The official card splits this into the 125B main model and the separate 51B embedding.

On local speed, community testers report 93 tokens per second on a 12GB RTX 5070, and 21 tokens per second at a 250K context window on a single 24GB RTX 4090, according to Medium. These are community reports, not Alibaba measurements. Kie.ai cautions that nobody outside Alibaba has confirmed that Flash-Next’s figures carry over to the Max tier. It says that claims such as “Qwen4-Max uses n-gram embeddings” are inferences from the preview, not confirmed features.

Four tiers, and why builders are watching the 27B

The planned line-up has stayed consistent across both Apsara events, according to Medium:

• Qwen4-Max: the flagship, positioned against the top closed models from other labs. It is the only tier in preview so far.
• Qwen4-Plus: the balanced commercial tier.
• Qwen4-Flash: the low-latency, high-throughput tier, which Medium says Flash-Next previews.
• Qwen4-27B: the open-weight tier for local workstations and private clusters, succeeding Qwen3.8-27B.

Most of the public reaction centres on the 27B tier. TestingCatalog wrote that it is “waiting for Qwen4-27B the most”. One developer said Qwen3.8 27B “is still the best local model at that size”. Licensing is unsettled, though. Medium notes that the Qwen3.8 generation shipped with mixed licences: Apache 2.0 for the 27B and a custom licence for the Max checkpoint. Kie.ai says nothing published confirms Apache 2.0 for Qwen4-27B, or whether it is dense or a small MoE. In a reply to the November 6 teaser, one user asked for a small MoE instead of a dense model, saying “inference is too slow on dense models”. Kie.ai also notes repeated requests for a small MoE in the r/LocalLLaMA discussion of the September keynote.

Larger models are on a later roadmap. Alibaba’s 5-to-10-trillion-parameter target applies to Qwen4.5 and Qwen5, not Qwen4.

What it means for builders

For teams already using Qwen’s closed models, the preview gives an early chance to test Alibaba’s coding and agent claims on real tasks, which is what the Qwen team is asking for. Without a model card or benchmarks, any comparison has to come from your own evaluations for now.

The current baseline is Qwen3.8-Max. Kie.ai reports that Qwen3.8-Max scores 34 on the Artificial Analysis Intelligence Index v5 and has a 1M-token context window. AIHubMix lists Qwen3.8 Max Preview with a 1,000,000-token context, a maximum output of 131,072 tokens, and text, image and video input. Its listing describes that model as having 2.4T parameters, with gains over Qwen 3.7 Max in coding and “Cowork” professional-productivity tasks. On LMArena ratings imported by AIHubMix, qwen3.8-max-preview shows 1484 overall and 1524 for coding. These are the numbers Qwen4-Max will be measured against once results exist.

For teams running models on their own hardware, the 1M-token context that Qwen Code reportedly assigns to Qwen4 identifiers, together with the Flash-Next speed reports on single consumer GPUs, explains the interest in the 27B. None of this is confirmed for any shipping Qwen4 model. One reply under the November 6 teaser said the poster had found “no official model card, API name, or release date”, and that is still true for the full family after November 11.

The main risks are planning risks. Prices, rate limits and licences are unknown for every tier, so production cost estimates and decisions about self-hosting under the 27B licence will have to wait.

What to watch next

• A model card or technical report for Qwen4-Max, confirming parameters, context window and modalities. None exists today.
• Independent scores. BenchLM had no public result for Qwen4 Max as of November 11. Medium reports that the Qwen4-linked “Kalmia” model was already appearing in anonymous arena battles in October, so named results could follow after launch.
• Release dates and prices for Plus, Flash and 27B, none of which Alibaba has published.
• The 27B licence and design: whether it keeps Apache 2.0 like Qwen3.8-27B, and whether it is dense or MoE.
• Whether Flash-Next’s design carries over, including the N-gram memory layer and hybrid attention, to the shipping models.