dsh-bailian-models

Bailian Models

Alibaba Bailian (DashScope) 模型目录预设 + DeepSeek Harness 自动适配器 — 推理强度级别、思考预算、上下文窗口、现有 Bailian 路由的自动适配、可配置的上下文上限

目标 Profile

插件会安装到这里；不确定时保持 web。

npx -y @deepseek-ai/dsh plugin --profile web add github:CroissanTTs/dsh-bailian-models#53cf053be605ea1cee32a1043ccf5346b71a1e9a

兼容性与来源证明

Bailian Models 以 dsh-bailian-models 发布，当前版本为 1.5.0。Plugin Hub 会校验它的 manifest，并保存精确安装来源，便于复现安装结果。

DSH 兼容范围: *
运行环境: any
发布来源: github
Registry 更新时间: 2026/9/20

版本

1.5.0stable

2026/9/20

1.4.0stable

2026/9/16

README

dsh-bailian-models

English | 中文

A DeepSeek Harness (DSH) plugin for Alibaba Bailian (DashScope):

Preset route: a ready-to-use bailian provider route with a built-in catalog of 36 major text models — context windows, max output tokens, and per-family reasoning effort controls (enable_thinking / reasoning_effort / thinking_budget);
Auto-adapter: detects any of your existing provider routes whose baseURL points at Bailian and fills in the missing dialect, reasoning levels, and capacity declarations — fields you wrote yourself are always kept.

Once installed, Bailian models appear in the DSH model picker with working reasoning-effort levels — no hand-written settings.yaml required.

Why

DSH's built-in pi-ai adapter does not recognize the dashscope.aliyuncs.com endpoint: requests fall back to the plain OpenAI dialect (wrong store/developer fields, no enable_thinking), and model entries carry no context-window or reasoning metadata — so reasoning strength is fixed and context is unknown. This bundle supplies the correct dialect, effort levels, and capacities for every listed model.

Install

DSH Desktop / Web (community market): search for dsh-bailian-models in the market and confirm the install.

CLI:

dsh plugin --profile web add dsh-bailian-models

Set your API key (referenced by env var name only — keys are never stored by this plugin):

export DASHSCOPE_API_KEY=sk-...

You can later edit the bailian route in DSH settings (Models page) to use a different env var name or regional endpoint.

Auto-adapting existing routes

Beyond the preset bailian route, the bundle mounts an auto-adapter: it watches the llm-pi-ai settings section, and any existing route whose baseURL hostname matches a Bailian endpoint (dashscope.aliyuncs.com / dashscope-intl / dashscope-us / *.maas.aliyuncs.com) gets filled in automatically:

route-level dialect compat (thinkingFormat: qwen, supportsStore: false, supportsDeveloperRole: false);
contextWindow / maxTokens / input / reasoningEfforts / model-level compat for models known to the bundled catalog (snapshot suffixes like -0902 or -2026-05-20 fall back to the parent model);
the route-level thinkingBudgets level map when budget-type models are present.

Only gaps are filled — values you wrote are never overwritten; unknown model ids are left untouched. Every write is validated against dsh-llm-pi-ai's own schema first; if a write is rejected, your config stays as-is and a warning is logged. Disable per profile by disabling the bailian-models-autoadapt patch row, or set its config.autoAdapt: false; self-hosted gateways can be added via config.extraHosts.

Auto-filled fields land in the user layer of your settings (settings.yaml), so they survive uninstalling the plugin (harmlessly); the preset bailian route disappears with uninstall.

How reasoning levels map to the wire

Bailian model families speak incompatible thinking dialects; the bundle adapts each one:

Family type	Wire params	DSH level behavior
A. Effort levels	`enable_thinking` + `reasoning_effort`	Selecting a level sends that effort; Off disables thinking
B. Budgets	`enable_thinking` + `thinking_budget`	Levels map to token budgets: minimal 1024 / low 4096 / medium 16384 / high 65536 (clamped per model)
C. Toggle	`enable_thinking` only	Off / High (High = thinking on)
D. Always-thinking	none (model always thinks)	No levels offered — sending any toggle risks a 400; reasoning content still displays

With no level selected, type A/B/C models explicitly send enable_thinking: false (predictable cost). Type D models are unaffected.

Model table

All capacities from the official Alibaba Bailian docs. Context / max output in tokens.

A. Effort levels — qwen3.8-max, qwen3.8-max-0902, qwen3.8-flash (off/low/medium/xhigh, 1M ctx, 131k out, text+image) · glm-5.3 (low/high/max — thinking cannot be disabled; 1M ctx, 131k out) · glm-5.2, glm-5.2-us, glm-5.2-fast-preview (off…max, 1,048,576 ctx) · glm-5.1, glm-5 (off…xhigh) · deepseek-v4-pro, deepseek-v4-pro-0813, deepseek-v4-flash, deepseek-v4-flash-0731 (off/high/max, 1M ctx, 393,216 out) · deepseek-v4.1-flash (minimal…max — six levels, no off; 1M ctx, 393,216 out, text+image).

deepseek-v4.1-flash levels: the official docs say reasoning_effort takes an integer 1–100, but the live compatible-mode endpoint rejects integers ('reasoning_effort' must be an object with 'effort' field or a String) and only accepts the enum minimal/low/medium/high/xhigh/max/ultra — ultra being one level beyond the documented set.

⚠️ The harness caps the level list at 7, and off occupies one of those slots (pi-ai's EXTENDED_THINKING_LEVELS = off/minimal/low/medium/high/xhigh/max; undeclared levels are dropped). The official enum has exactly seven values, so "drop off, minimal→ultra = seven levels" is physically impossible in the harness — that would be eight. The choice is "off + 6 levels" or "no off, 6 levels". This catalog picks the latter: the slider's floor is minimal (thinking cannot be disabled for this model), and max maps to the official top tier ultra — i.e. the high max is sacrificed to keep the low minimal. enable_thinking:false does genuinely disable thinking (tested), so this is a deliberate trade-off; to restore "can disable thinking", add an empty off: line to reasoningEfforts (the top then becomes xhigh).

glm-5.3 levels: only low / high / max, and thinking cannot be disabled. The docs say passing enable_thinking=false "has no effect", but the live endpoint returns a hard 400 (InternalError.Algo.InvalidParameter: The value of the enable_thinking parameter is restricted) — this catalog follows the measured behaviour: three levels, no off.

Because the harness's qwen dialect sends enable_thinking: false when no level is selected (a guaranteed 400 here), this entry overrides thinkingFormat from the route's qwen to openai in its model-level compat. pi-ai then sends reasoning_effort only when a level is picked and sends nothing at all otherwise, falling back to the model default (thinking on). The levels do scale depth: on the same hard problem, low ≈ 10, high ≈ 59, max ≈ 686 reasoning tokens.

B. Budgets — qwen3.7-max/plus/flash, qwen3.6-plus/flash, qwen3.5-plus/flash, qwen3-max, qwen-plus (off/minimal/low/medium/high).

C. Toggle — qwen-flash, qwen-turbo, deepseek-v3.2, deepseek-v3.2-exp, deepseek-v3.1, kimi-k2.6, kimi-k2.5.

D. Always-thinking — kimi-k3, kimi-k2.7-code, kimi-k2-thinking, MiniMax-M2.5, MiniMax-M2.1, deepseek-r1, deepseek-r1-0528, qwq-plus.

The full per-model table with sources lives in README.zh.md.

Customizing

The bundle writes into the composition base layer; anything you put in the llm-pi-ai: section of ~/.dsh/settings.yaml (or edit in the Models page) merges over it per provider key and applies without a restart:

llm-pi-ai:
  providers:
    bailian:
      baseURL: https://dashscope-intl.aliyuncs.com/compatible-mode/v1  # Singapore
      apiKeyEnv: MY_BAILIAN_KEY
      # Narrow the model list. Note that settings merges arrays wholesale, so this
      # list replaces the base-layer catalog; fields you leave out are restored by
      # the auto-adapter by id (including reasoningEfforts) — the id alone suffices.
      models:
        - id: qwen3.8-max

⚠️ Careful when editing the Bailian list on the Models page: the GUI writes the resolved list back into settings.yaml as models, replacing the base-layer catalog (this is how a 37-model route shrinks to a handful). The auto-adapter restores the missing fields by id on the next settings change, so effort levels survive — but if you want the whole catalog, delete that models: block from the user layer.

Uninstall removes the base-layer route; any user-layer overrides of bailian stay harmless but inert — delete them too if you like.

How it works (for maintainers)

Two parts:

Pure-config preset: package.json's dsh.bundle.patch points to cordis.patch.yml, which overrides (by id) the dormant llm-pi-ai row in the dsh-base composition, injecting the bailian route into the base layer. The name field is a drift guard: if a future base composition mounts a different plugin under that id, this patch is skipped with a warning instead of silently corrupting configuration.
Auto-adapter (src/index.js, mounted by the same patch's insert row): listens to settings/document-updated, detects Bailian routes by hostname, and fills gaps via an idempotent pure function (computeRoutePatch). The model catalog is generated from cordis.patch.yml (npm run build → src/catalog.mjs); the YAML is the single source of truth. Zero runtime dependencies.

Serialization is done by @deepseek-ai/dsh-llm-pi-ai (pi-ai runtime): with the qwen thinking dialect it sends enable_thinking / reasoning_effort / thinking_budget, and reasoning streams back via reasoning_content.

Validate after editing the patch:

npm install
npm test   # schema-validates cordis.patch.yml + catalog sync + auto-adapter behavior (22 assertions)

Platform constraints worth knowing

Thinking mode requires streaming — DSH always streams, so this is a non-issue.
reasoning_effort and thinking_budget are mutually exclusive on the qwen3.8 family, hence no budget levels on type-A Qwen models.
kimi-k3, deepseek-r1, MiniMax-M2.x, qwq-plus etc. are always-thinking models; thinking cannot be disabled — a platform behavior, not a plugin defect.
The bundle cannot see deprecations; if Bailian retires a model, delete its entry in settings.

Contributing

Add or fix a model by editing cordis.patch.yml: copy the entry shape of its family and attach a link to the official Bailian doc page for the numbers. Run npm run validate before submitting.

License

MIT

README

dsh-bailian-models

English | 中文

A DeepSeek Harness (DSH) plugin for Alibaba Bailian (DashScope):

Preset route: a ready-to-use bailian provider route with a built-in catalog of 36 major text models — context windows, max output tokens, and per-family reasoning effort controls (enable_thinking / reasoning_effort / thinking_budget);
Auto-adapter: detects any of your existing provider routes whose baseURL points at Bailian and fills in the missing dialect, reasoning levels, and capacity declarations — fields you wrote yourself are always kept.

Once installed, Bailian models appear in the DSH model picker with working reasoning-effort levels — no hand-written settings.yaml required.

Why

Install

DSH Desktop / Web (community market): search for dsh-bailian-models in the market and confirm the install.

CLI:

dsh plugin --profile web add dsh-bailian-models

Set your API key (referenced by env var name only — keys are never stored by this plugin):

export DASHSCOPE_API_KEY=sk-...

You can later edit the bailian route in DSH settings (Models page) to use a different env var name or regional endpoint.

Auto-adapting existing routes

route-level dialect compat (thinkingFormat: qwen, supportsStore: false, supportsDeveloperRole: false);
contextWindow / maxTokens / input / reasoningEfforts / model-level compat for models known to the bundled catalog (snapshot suffixes like -0902 or -2026-05-20 fall back to the parent model);
the route-level thinkingBudgets level map when budget-type models are present.

Auto-filled fields land in the user layer of your settings (settings.yaml), so they survive uninstalling the plugin (harmlessly); the preset bailian route disappears with uninstall.

How reasoning levels map to the wire

Bailian model families speak incompatible thinking dialects; the bundle adapts each one:

Family type	Wire params	DSH level behavior
A. Effort levels	`enable_thinking` + `reasoning_effort`	Selecting a level sends that effort; Off disables thinking
B. Budgets	`enable_thinking` + `thinking_budget`	Levels map to token budgets: minimal 1024 / low 4096 / medium 16384 / high 65536 (clamped per model)
C. Toggle	`enable_thinking` only	Off / High (High = thinking on)
D. Always-thinking	none (model always thinks)	No levels offered — sending any toggle risks a 400; reasoning content still displays

With no level selected, type A/B/C models explicitly send enable_thinking: false (predictable cost). Type D models are unaffected.

Model table

All capacities from the official Alibaba Bailian docs. Context / max output in tokens.

deepseek-v4.1-flash levels: the official docs say reasoning_effort takes an integer 1–100, but the live compatible-mode endpoint rejects integers ('reasoning_effort' must be an object with 'effort' field or a String) and only accepts the enum minimal/low/medium/high/xhigh/max/ultra — ultra being one level beyond the documented set.

⚠️ The harness caps the level list at 7, and off occupies one of those slots (pi-ai's EXTENDED_THINKING_LEVELS = off/minimal/low/medium/high/xhigh/max; undeclared levels are dropped). The official enum has exactly seven values, so "drop off, minimal→ultra = seven levels" is physically impossible in the harness — that would be eight. The choice is "off + 6 levels" or "no off, 6 levels". This catalog picks the latter: the slider's floor is minimal (thinking cannot be disabled for this model), and max maps to the official top tier ultra — i.e. the high max is sacrificed to keep the low minimal. enable_thinking:false does genuinely disable thinking (tested), so this is a deliberate trade-off; to restore "can disable thinking", add an empty off: line to reasoningEfforts (the top then becomes xhigh).

glm-5.3 levels: only low / high / max, and thinking cannot be disabled. The docs say passing enable_thinking=false "has no effect", but the live endpoint returns a hard 400 (InternalError.Algo.InvalidParameter: The value of the enable_thinking parameter is restricted) — this catalog follows the measured behaviour: three levels, no off.

Because the harness's qwen dialect sends enable_thinking: false when no level is selected (a guaranteed 400 here), this entry overrides thinkingFormat from the route's qwen to openai in its model-level compat. pi-ai then sends reasoning_effort only when a level is picked and sends nothing at all otherwise, falling back to the model default (thinking on). The levels do scale depth: on the same hard problem, low ≈ 10, high ≈ 59, max ≈ 686 reasoning tokens.

B. Budgets — qwen3.7-max/plus/flash, qwen3.6-plus/flash, qwen3.5-plus/flash, qwen3-max, qwen-plus (off/minimal/low/medium/high).

C. Toggle — qwen-flash, qwen-turbo, deepseek-v3.2, deepseek-v3.2-exp, deepseek-v3.1, kimi-k2.6, kimi-k2.5.

D. Always-thinking — kimi-k3, kimi-k2.7-code, kimi-k2-thinking, MiniMax-M2.5, MiniMax-M2.1, deepseek-r1, deepseek-r1-0528, qwq-plus.

The full per-model table with sources lives in README.zh.md.

Customizing

llm-pi-ai:
  providers:
    bailian:
      baseURL: https://dashscope-intl.aliyuncs.com/compatible-mode/v1  # Singapore
      apiKeyEnv: MY_BAILIAN_KEY
      # Narrow the model list. Note that settings merges arrays wholesale, so this
      # list replaces the base-layer catalog; fields you leave out are restored by
      # the auto-adapter by id (including reasoningEfforts) — the id alone suffices.
      models:
        - id: qwen3.8-max

⚠️ Careful when editing the Bailian list on the Models page: the GUI writes the resolved list back into settings.yaml as models, replacing the base-layer catalog (this is how a 37-model route shrinks to a handful). The auto-adapter restores the missing fields by id on the next settings change, so effort levels survive — but if you want the whole catalog, delete that models: block from the user layer.

Uninstall removes the base-layer route; any user-layer overrides of bailian stay harmless but inert — delete them too if you like.

How it works (for maintainers)

Two parts:

Pure-config preset: package.json's dsh.bundle.patch points to cordis.patch.yml, which overrides (by id) the dormant llm-pi-ai row in the dsh-base composition, injecting the bailian route into the base layer. The name field is a drift guard: if a future base composition mounts a different plugin under that id, this patch is skipped with a warning instead of silently corrupting configuration.
Auto-adapter (src/index.js, mounted by the same patch's insert row): listens to settings/document-updated, detects Bailian routes by hostname, and fills gaps via an idempotent pure function (computeRoutePatch). The model catalog is generated from cordis.patch.yml (npm run build → src/catalog.mjs); the YAML is the single source of truth. Zero runtime dependencies.

Validate after editing the patch:

npm install
npm test   # schema-validates cordis.patch.yml + catalog sync + auto-adapter behavior (22 assertions)

Platform constraints worth knowing

Thinking mode requires streaming — DSH always streams, so this is a non-issue.
reasoning_effort and thinking_budget are mutually exclusive on the qwen3.8 family, hence no budget levels on type-A Qwen models.
kimi-k3, deepseek-r1, MiniMax-M2.x, qwq-plus etc. are always-thinking models; thinking cannot be disabled — a platform behavior, not a plugin defect.
The bundle cannot see deprecations; if Bailian retires a model, delete its entry in settings.

Contributing

Add or fix a model by editing cordis.patch.yml: copy the entry shape of its family and attach a link to the official Bailian doc page for the numbers. Run npm run validate before submitting.

License

MIT

Bailian Models

兼容性与来源证明

版本

相关插件

Bailian Models

README

dsh-bailian-models

Why

Install

Auto-adapting existing routes

How reasoning levels map to the wire

Model table

Customizing

How it works (for maintainers)

Platform constraints worth knowing

Contributing

License

兼容性与来源证明

版本

相关插件

相关插件

相关插件

README

dsh-bailian-models

Why

Install

Auto-adapting existing routes

How reasoning levels map to the wire

Model table

Customizing

How it works (for maintainers)

Platform constraints worth knowing

Contributing

License