OpenClaw Full‑Domestic Model Stack: 6 Role‑Based Selections and Strategies

Problem addressed

A six‑model configuration is proposed for OpenClaw, assigning each model a distinct role: control (planning, scheduling, tool orchestration, global consistency), writing (long‑form, stylistic, structured output), coding (code generation, refactoring, patching), fallback (low‑value tasks, retries, cost control), vector (memory retrieval), and local (privacy‑sensitive, low‑latency tasks).

Common pitfalls when switching to an all‑domestic stack

1. Ignoring role‑based division

Choosing a single “strongest” domestic model for every role leads to instability: a model good at planning may produce poor prose, or a model strong at coding may have unstable tool calls.

Conclusion: role‑match outweighs single‑point strength.

2. Equating inference strength with control suitability

Strong reasoning does not guarantee suitability for the control role, which requires stable instruction following, reliable tool invocation, clean context switching, resistance to task drift, and graceful error recovery. The control role functions more like a project manager + scheduler than a pure reasoning engine.

3. Overlooking cost and latency

Domestic models vary widely in cost and speed. Using a high‑cost model for simple rewrites or a low‑cost model for core planning creates inefficiency. Models should be layered by value density rather than vendor preference.

Recommended domestic stack

Control model: GLM‑5 Writing model: Kimi K2.5 Coding model: DeepSeek (or GLM‑5)

Fallback model: GLM 4.7 / GLM‑Flash Vector model: BGE‑M3 Local model:

Qwen3‑Coder‑Next

Rationale per role

Control – GLM‑5

GLM‑5 emphasizes agentic engineering, native tool calling, multi‑step planning, and supports 200K context. It shows balanced performance on code, long tasks, and multi‑step workflows, making it a robust “total‑control” model comparable to the previously used GPT 5.4.

Writing – Kimi K2.5

Kimi K2.5 excels in Chinese naturalness, ultra‑long text handling, consistent style, and extracting structure from extensive material. It is well‑suited for tutorials, industry analysis, and multi‑chapter documents. Known limitations include occasional consistency drops and style jumps; therefore the workflow is: control defines structure → Kimi generates long text → a lightweight model performs polishing.

Coding – DeepSeek (or GLM‑5)

DeepSeek combines strong code generation, reasoning, and cost efficiency. It handles complex logic, multi‑step problem decomposition, and integrates smoothly with the control model’s tool orchestration. GLM‑5 can serve as a backup when DeepSeek is unavailable.

Fallback – GLM 4.7 / GLM‑Flash

Fallback models need stability, low cost, reasonable instruction following, and error recovery. GLM 4.7 and GLM‑Flash share interfaces with the control model, reducing migration friction and handling large volumes of low‑value work.

Vector – BGE‑M3

BGE‑M3 provides multilingual local vectorization for memory retrieval; no change is required.

Local – Qwen3‑Coder‑Next

Qwen3‑Coder‑Next is open‑source, deployable locally, privacy‑friendly, and excels at reading private repositories, offline completion, and fallback execution.

Alternative configurations

Option A – Balanced (closest to current experience)

Control: GLM‑5 Writing: Kimi K2.5 Coding: DeepSeek / GLM‑5 Fallback: GLM 4.7 / GLM‑Flash Vector: BGE‑M3 Local:

Qwen3‑Coder‑Next

Option B – Writing‑first

Control: GLM‑5 Writing: Kimi K2.5 Coding: GLM‑5 / DeepSeek Fallback: MiniMax M2.5 Vector: BGE‑M3 Local:

Qwen3‑Coder‑Next

Option C – Cost‑focused, local‑first

Control: GLM‑5 Writing: Qwen‑Max Coding: DeepSeek Fallback: Qwen‑Flash / GLM‑Flash Vector: BGE‑M3 Local:

Qwen3‑Coder‑Next

Migration roadmap (three steps)

Step 1 – Replace control model

Swap GPT 5.4 with GLM‑5 and monitor multi‑step task drift and tool‑call stability.

Step 2 – Replace writing and coding models

Swap Claude 4.6 Opus with Kimi K2.5 and GPT 5.3 Codex with DeepSeek (or GLM‑5). Evaluate text quality against publishing standards and verify patch‑level code reliability.

Step 3 – Optimize cost layer

Adjust fallback, batch‑rewrite, and local model proportions to reduce long‑term expenses.

Conclusion

Fully domestic OpenClaw is feasible and advantageous for cost control, privacy, and long‑term stability. The key is assigning each model to the role it performs best rather than chasing a single “most powerful” model.