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Alibaba's Qwen3.8-Max: 2.4T-param MoE flagship that programs autonomously for days

On 2026-08-03 Alibaba Tongyi released Qwen3.8-Max: a 2.4T-param MoE flagship with 1M context (991K input / 131K output), native vision across plan-execute-verify, positioned to "autonomously program for over ten days delivering complete projects." Pricing: ¥12/M input, ¥36/M output, explicit cache hit ¥1 (1/12 of uncached). Three entry points: blog / Qianwen platform / Qwen Studio.

Published August 3, 20264 min read
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On August 3, Alibaba's Tongyi officially released Qwen3.8-Max. This isn't a routine iteration-a 2.4-trillion-parameter MoE flagship, context pushed to 1M, and the official positioning is blunt: "can autonomously program for over ten days delivering complete projects." The model is no longer asked "can you?" but "are you done?"

Flagship specs: 2.4T-param MoE, 1M context

Qwen3.8-Max is a Mixture-of-Experts (MoE) architecture, 2.4 trillion total parameters, flagship tier. MoE means the model is large, but each inference activates only a subset of expert networks, so actual compute is far smaller than the parameter count-the key to scaling a model big without blowing up inference cost.

The context window is 1M tokens. A single turn takes up to 991K of input and returns 131K of output; in thinking mode input still reaches 983K with a max thought chain of 262K. Toss in a novel plus its reference docs and it reads while it works. Native vision understanding is standard: image, text, and video in, text out. The official line is that vision spans the full "plan-execute-verify" loop, supporting deep semantic parsing of ultra-long documents and long videos-not a glued-on OCR, but something built to work.

Pricing: cache hits drop to about a tenth

Pricing on the Qianwen AI platform:

ItemPrice (per million tokens)
Input¥12
Output¥36
Input (auto cache hit)¥1.5
Explicit cache creation¥15
Explicit cache hit¥1

The story is in the cache. Explicit cache hit at ¥1 versus uncached input at ¥12 is a 12x gap. Long-horizon tasks have high prefix repetition, so caching the shared prefix drives real input cost down to about a tenth. Explicit cache creation at ¥15 is 25% pricier than plain input, but you pay it once; subsequent hits run at ¥1. This is the cost switch built for "long conversation, multi-round iteration" scenarios-and Qwen3.8-Max is positioned exactly for long-horizon tasks, so the two fit tightly.

From "answering" to "working": long-horizon autonomous tasks

What's worth watching isn't the spec sheet, it's the shift in positioning. The official text reads: "coding and office capabilities comprehensively upgraded, can autonomously program for over ten days delivering complete projects," "handles legal, finance, design and hundreds of professional tasks, one conversation end-to-end delivering production-grade results."

Translated: the model is designed to take on a project that runs for over ten days-planning, executing, verifying, iterating on its own, not one round of Q&A. The supporting features are laid out for exactly this: function calling to wire external tools, structured output to guarantee JSON, web search for real-time info, batch tasks for scale, and fine-tuning open too. Partial Mode lets you give a prefix it strictly continues-good for pinning generation rhythm in engineering contexts.

"In long-horizon tasks, autonomous planning and closed-loop iteration, continuous evolution"-if that holds up under real load, it means the agent framework and the model itself are starting to ship fused together. Agents used to be a shell calling a model; now the model is growing its own agent bones.

How to try it right now

Three entry points, by use:

Take

The domestic flagship race is no longer about benchmark digits, it's about "how long a job can it finish." 2.4T params plus 1M context is the entry ticket; autonomous programming for over ten days with closed-loop iteration is the flag Qwen3.8-Max wants to plant. Pricing cache hits down to a tenth shows Alibaba has done the math: for long-horizon tasks to run at all, a single call has to be cheap enough to let it retry freely.

The real question is whether "ten-plus days to delivery" reproduces in real engineering. The positioning reads well, but a model planning a ten-day task chain on its own without drifting or collapsing mid-way is the hardest part-and that only shows when developers throw it into real codebases and run long-chain tasks. Today, claim an entry point and try it yourself; that beats any benchmark.

Sources

This article is AI-assisted and human-edited. Last updated: 2026-08-03

FAQ

How many parameters does Qwen3.8-Max have?
2.4 trillion parameters, MoE (Mixture-of-Experts) architecture, flagship tier. MoE means the total parameter count is large, but each inference activates only a subset of expert networks, so actual compute is far smaller than the parameter total-the key to scaling big without blowing up inference cost.
How long is the context window?
The context window is 1M tokens. A single turn takes max 991K input and 131K output; in thinking mode max input is 983K, max output 131K, with a max thought chain of 262K. Native vision supports image, text, and video input.
What is the API pricing?
Qianwen AI platform pricing: ¥12 per million tokens input, ¥36 per million output. Cache hits are far cheaper-auto cache hit ¥1.5, explicit cache hit ¥1 (versus ¥12 uncached input, a 12x gap). Explicit cache creation is ¥15, paid once with subsequent hits at ¥1, suited to prefix reuse in long-horizon tasks.

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