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Qwen 3.8 in Rope Notes: Frontier Preview for Plan and Execute

Alibaba's Qwen team previewed Qwen3.8-Max-Preview, a 2.4-trillion-parameter multimodal model with a 1-million-token context window, at WAIC in Shanghai, claiming it ranks second only to Anthropic's Claude Fable 5. Independent leaderboards and a full model card are not yet public, and open weights are promised "soon" with no date or license. The model is available via cloud API for use in Rope Notes' agent for Plan and Execute workflows, while local use still relies on the existing open-weight Qwen3 line.

read4 min views2 publishedJul 20, 2026

Three days after Moonshot’s Kimi K3, Alibaba’s Qwen team previewed Qwen3.8-Max-Preview at WAIC in Shanghai — a 2.4-trillion-parameter multimodal flagship with a 1-million-token context window. Alibaba positions it near the top of the frontier stack (their claim: second only to Anthropic’s Claude Fable 5). Independent leaderboards and a full model card are not public yet, so treat the ranking as the vendor’s until third-party numbers land.

If you already use Rope Notes’ agent for Dart, Flutter, or multi-file refactors, you can point a cloud provider at the preview today. Open weights are promised “soon,” with no date or license yet — so local 3.8 Max is not a thing yet.

Fact Status
Model ID qwen3.8-max-preview
Parameters 2.4T (Alibaba); activated-parameter count not published
Context 1M tokens (per

There is no new “Qwen 3.8B” dense twin in this launch. The name is Qwen 3.8 (family version), not an 8-billion-parameter sibling. For on-device work you still use the existing open-weight Qwen3 line (qwen3:8b

and friends via Ollama / llama.cpp) until 3.8 Max weights appear.

Rope Notes is a local-first, IDE-style editor: rope-backed editing, Dart analysis, and an agent that can Chat, Plan, and Execute — with tools, then multi-file edit proposals you accept or reject. A long-context frontier model is useful for the Plan → Execute loop; a local Qwen3 keeps day-to-day work on-device.

Qwen 3.8 Max strength How Rope Notes uses it
Long-horizon coding + tools Agent tool loop (read_file , search, definitions, …) over many turns
1M context Open tabs, @ mentions, plans, and memory stay coherent longer
Multimodal input Screenshot / layout feedback when your session includes image context
OpenAI-compatible API Same Preferences → Agent flow as other cloud backends
Open weights later Same UX once you host weights or a LAN OpenAI-compatible endpoint

Alibaba markets strong coding and full-stack work relative to Qwen3.7-Max. Until independent scores exist, the practical test is your own Plan/Execute sessions — not launch-day leaderboard claims.

qwen3.8-max-preview

(not general pay-as-you-go at announce time).Open weights, when they land, change the local story. Until then, treat 3.8 Max as a cloud provider in Preferences.

You need a desktop or mobile build with cloud agent backends (web builds do not run the agent).

https://dashscope-intl.aliyuncs.com/compatible-mode/v1

qwen3.8-max-preview

If your Token Plan console shows a dedicated base URL (some Alibaba plans use a plan-specific host), use that URL instead — the model ID stays qwen3.8-max-preview

.

Provider configs live under .rope_notes/preferences.toml

. Share the setup, not the secret keys.

Rope Notes remains Ollama-first on desktop (llama.cpp is also first-class). For offline or source-sovereign sessions:

ollama pull qwen3:8b

Then select the Ollama (local) or llama.cpp (local) provider and the pulled tag. That is the current Qwen3 open line — not Qwen3.8-Max-Preview.

@lib/...

or by dragging from Explorer.plan-*.md

/ checklist under .rope_notes/

.The 1M window helps across long checklists; still prefer targeted @

mentions so turns stay cheaper and sharper.

When Execute proposes changes, Rope Notes surfaces an inline diff review (Accept / Reject per file, or Accept all). The model does not write straight through your buffer — you still own the rope.

A common split:

qwen3:8b

(or similar)qwen3.8-max-preview

Switch providers from the agent panel; no project rewrite required.

On Android, cloud backends work even though local Ollama is a desktop feature. Use session transfer to move buffers and agent artifacts between machines, then keep the same Qwen Cloud provider selected.

Accurate expectations beat marketing:

@

mentions (with LSP diagnostics on those snippets)/remember

), optional cognitive memory hits, active skillslist_directory

, grep

, read_file

, …) for on-demand deeper readsUse @

and Plan artifacts deliberately; a 1M window is not a reason to paste the whole repo by default.

Before long Execute sessions:

.rope_notes/permissions.toml

.git

and secret paths as neededFrontier models are most useful when they cannot touch everything.

qwen3.8-max-preview

qwen3.8-max-preview

)Qwen3.8-Max-Preview is available now as a hosted preview. In Rope Notes it is a Preferences change — and a natural cloud counterpart to the local Qwen3 models you may already run on Ollama.

Rope Notes — a high-performance, local-first editor with a real AI agent, Dart analysis, and optional P2P sync. ropenotes.dev

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