How to set up DeepSeek Flash + GLM 5.2 advisor in OpenCode - the exact config
A few people asked how to set up the DS Flash + GLM 5.2 combo from my last post. Here's the exact config.
The idea: DeepSeek Flash handles the routine orchestration (cheap, fast, 1M context). GLM 5.2 steps in as an advisor subagent when the task needs actual reasoning. Flash pays ~$0.0003 per mechanical call. GLM only burns credits on the calls that need it.
The config
Add this to your opencode.jsonc. Any of the three locations work:
~/.config/opencode/opencode.jsonc # global, all projects
~/.opencode/opencode.jsonc # project-level
.opencode/opencode.jsonc # per-repo, can commit
{
"$schema": "https://opencode.ai/config.json",
"default_agent": "deepseek-flash",
"agent": {
"deepseek-flash": {
"description": "Primary agent. Fast, cheap orchestration for routine engineering work.",
"mode": "primary",
"model": "opencode-go/deepseek-v4-flash",
"steps": 30
},
"glm-advisor": {
"description": "Strategic advisor for second opinions, plan critique, and architecture tradeoffs.",
"mode": "subagent",
"hidden": true,
"model": "opencode-go/glm-5.2",
"steps": 15,
"temperature": 0.3,
"permission": {
"read": "allow",
"glob": "allow",
"grep": "allow",
"list": "allow",
"webfetch": "allow",
"edit": "deny",
"write": "deny",
"bash": "deny",
"task": "deny",
"question": "allow",
"todowrite": "deny"
}
}
},
"provider": {
"opencode-go": {
"apiKey": "{env:OPENROUTER_API_KEY}"
}
}
}
How it works
Set default_agent to deepseek-flash. Flash handles every session, cheap and fast. When you hit a task that needs judgment -- architecture decision, plan critique, second opinion -- tell Flash to dispatch the glm-advisor subagent via the task tool.
Flash's system prompt already knows how to route:
- Bounded mechanical work (classify, edit JSON, summarize): handles itself
- Strategic work (tradeoffs, plan review, second opinions): dispatches to
glm-advisor
Prompt for the advisor subagent (optional)
If you want the advisor to follow a consistent output format, save this as .opencode/prompts/glm-advisor.md:
You are a sharp, honest senior advisor. All context is inline in the prompt below.
Never reference files, external sources, or prior conversations.
Structure every response in three sections:
1. CONCLUSION -- your direct answer or recommendation in 1-3 sentences.
2. REASONING -- the key factors, evidence, or logic behind your conclusion.
3. WATCH OUT -- caveats, failure modes, or what may have been missed.
Be direct. If the question has no good answer, say so and explain why.
Do not hedge unnecessarily. Calibrate confidence honestly.
Then reference it in the config by adding to the glm-advisor block:
"prompt": "{file:.opencode/prompts/glm-advisor.md}"
What changes
Before: GLM 5.2 running every call. Burning through opencode-go quota on routine work like "list files" and "run tests."
After: Flash handles everything routine. GLM only fires when you (or Flash) decide analysis is needed. In my usage, roughly 70-80% of calls stay on Flash. The other 20-30% use GLM, and those are the ones that actually needed it.
Why this split
Flash is $0.14/M input, GLM is $1.40/M. For classification and formatting work under 2K tokens, Flash costs about $0.0003 per call. GLM on the same task costs an order of magnitude more for no meaningful quality difference.
The advisor subagent pattern keeps GLM in reserve for the work it actually improves: multi-factor analysis, architecture judgment, and second opinions. Everything else stays on Flash.
Caveats
- Flash and GLM both have 1M context. Context length is not a differentiator between them.
- GLM's reasoning mode (effort=max) takes 60-120 seconds. Budget for it if you call it synchronously.
- The advisor is read-only. It can read files, search, and fetch URLs, but cannot edit files, write code, or run commands. Output lands in the primary agent's context for review.