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Describe the enhancement you want to request

Problem

When a primary agent invokes a subagent via the Task tool, it cannot dynamically control which model the subagent uses. The current model is determined by:

• The agent's pre-configured model (in opencode.json)
• Fallback to the parent session's model

This creates friction in two scenarios:

• Users create duplicate agents with identical prompts just to vary the model - explore-quick, explore-standard, explore-advanced all pointing to the same prompt file.
• The primary agent cannot assess task complexity and select an appropriate model. A trivial file search uses the same model as complex architectural analysis, wasting cost or capability.

Solution A: Global Model Tiers

Add a model_tier parameter to the Task tool with three values: quick, standard, advanced. Users configure tier-to-model mappings globally.

Configuration

{
  "model_tiers": {
    "quick": { "model": "anthropic/claude-haiku-4-5" },
    "standard": { "model": "anthropic/claude-sonnet-4-5" },
    "advanced": { "model": "anthropic/claude-opus-4-5" }
  }
}

Task Tool Usage

Task(
  description = "Find config files",
  prompt = "List all JSON config files in the project",
  subagent_type = "explore",
  model_tier = "quick",
)

Why Not Raw Model IDs?

An alternative would expose a model parameter accepting raw model IDs directly. This has drawbacks:

• Training cutoff: LLM may not know newer models released after its training date
• Model proliferation: Providers like OpenRouter expose 100+ models - either bloat the tool description or provide no guidance
• No validation: LLM could specify unavailable models; errors surface only at runtime

The tier abstraction solves these: the LLM assesses task complexity (which it can do), mappings are user-configured and guaranteed valid, and token cost is fixed (3 enum values).

Solution B: Agent-Level Model Tiers

Add the same model_tier parameter, but configure tier-to-model mappings per-agent instead of globally.

Configuration

{
  "agent": {
    "explore": {
      "model_tiers": {
        "quick": { "model": "anthropic/claude-haiku-4-5" },
        "standard": { "model": "anthropic/claude-sonnet-4-5" },
        "advanced": { "model": "anthropic/claude-opus-4-5" }
      }
    },
    "code-review": {
      "model_tiers": {
        "quick": { "model": "anthropic/claude-sonnet-4-5" },
        "standard": { "model": "anthropic/claude-opus-4-5" },
        "advanced": { "model": "anthropic/claude-opus-4-5" }
      }
    }
  }
}

Task Tool Usage

Task(
  description = "Security-focused review",
  prompt = "Review auth.ts for vulnerabilities",
  subagent_type = "code-review",
  model_tier = "advanced",
)

This approach allows agent-specific mappings - quick for explore (simple file search) might map to Haiku, while quick for code-review might still need Sonnet.

Solution C: Unified Approach

Combine both solutions with layered resolution. Same model_tier parameter, but agent-level model_tiers override global tiers when defined.

Configuration

{
  "model_tiers": {
    "quick": { "model": "anthropic/claude-haiku-4-5" },
    "standard": { "model": "anthropic/claude-sonnet-4-5" },
    "advanced": { "model": "anthropic/claude-opus-4-5" }
  },
  "agent": {
    "code-review": {
      "model_tiers": {
        "quick": { "model": "anthropic/claude-sonnet-4-5" }
      }
    }
  }
}

Task Tool Usage

Same as Solutions A and B - the parameter is identical.

Resolution

• explore with model_tier="quick" -> no agent override -> uses global tier -> Haiku
• code-review with model_tier="quick" -> uses agent model_tiers -> Sonnet (override)
• code-review with model_tier="advanced" -> no agent override -> falls back to global tier -> Opus

Model Selection Hierarchy

All solutions extend the existing hierarchy:

1. Agent's configured model              -> Explicit agent.model wins (tier ignored)
2. Agent-level model_tiers[tier]        -> If defined (Solution B/C)
3. Global model_tiers[tier]             -> If defined (Solution A/C)
4. Parent session's model               -> Fallback

Implementation Notes

Solution A requires:

• Task tool: add model_tier: z.enum(["quick", "standard", "advanced"]).optional()
• Config: add model_tiers: { quick?: { model: string }, ... }
• Model resolution: check global tier mapping before session fallback

Solution B requires:

• Task tool: add model_tier: z.enum(["quick", "standard", "advanced"]).optional()
• Agent config: add model_tiers: { quick?: { model: string }, ... }
• Model resolution: check agent's tier mapping before session fallback

Solution C requires:

• All of the above
• Model resolution: check agent tier first, then global tier, then session fallback

Future Extensibility

The object structure for tier configuration allows future enhancements without breaking changes:

{
  "model_tiers": {
    "quick": {
      "model": "anthropic/claude-haiku-4-5",
      "temperature": 0.2,
      "max_tokens": 4000
    },
    "advanced": {
      "model": "anthropic/claude-opus-4-5",
      "temperature": 0.7
    }
  }
}

Potential future additions:

• temperature - override default temperature for tier
• max_tokens - limit output tokens for cost control
• top_p - adjust sampling for tier
• timeout - per-tier timeout settings

This is out of scope for the initial implementation but the object structure supports it.

Would any of these approaches be welcome? Is there a preferred direction, or alternative solutions already considered?