openclaw-model-advisor

name

openclaw-model-advisor

description

Audit one or more OpenClaw agents, analyse what they require (complexity, tool use, context window, volume, autonomy, sensitivity), and recommend the best-fit OpenRouter models by pricing tier with ready-to-paste openclaw.json config snippets. Use when the user says "what model should I use", "recommend a model for my agent", "audit my agent models", "find a cheaper model", "openrouter model suggestions", "which model fits my agent", or invokes /openclaw-model-advisor.

OpenClaw Model Advisor

Audit one or more OpenClaw agents and recommend the best-fit OpenRouter models by pricing tier, based on what each agent actually needs.

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References

Source	URL
OpenRouter Model Catalog	https://openrouter.ai/models
OpenRouter Tool-Calling Collection	https://openrouter.ai/collections/tool-calling-models
OpenRouter Free Models Collection	https://openrouter.ai/collections/free-models
OpenRouter Pricing Page	https://openrouter.ai/pricing
OpenRouter Tool-Calling Docs	https://openrouter.ai/docs/guides/features/tool-calling
OpenRouter Model Docs	https://openrouter.ai/docs/guides/overview/models
TeamDay Best Models Guide (Mar 2026)	https://www.teamday.ai/blog/top-ai-models-openrouter-2026
TeamDay Free Models Guide (Mar 2026)	https://www.teamday.ai/blog/best-free-ai-models-openrouter-2026
OpenRouter Pricing Calculator	https://costgoat.com/pricing/openrouter
Free Models That Work for Agents	https://brainroad.com/openrouter-free-models-which-ones-actually-work-for-ai-agents/
LLM API Pricing Mar 2026	https://www.tldl.io/resources/llm-api-pricing-2026
AI API Pricing Comparison 2026	https://intuitionlabs.ai/articles/ai-api-pricing-comparison-grok-gemini-openai-claude

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OpenRouter Model Tiers (March 2026)

Prices are per million tokens (input / output). All models use OpenRouter's provider/model-id format in openclaw.json.

Tier 0 — Free (Rate-limited: 20 req/min, 200 req/day)

Use for: development, low-volume personal agents, testing. Not suitable for production or high-frequency autonomous agents.

Model	OpenRouter ID	Context	Strengths
Qwen3 Coder 480B	qwen/qwen3-coder-480b-a35b-instruct:free	262K	Best free coding model; tool calling
Qwen3 235B A22B	qwen/qwen3-235b-a22b:free	128K	MoE; reasoning + tool calling
NVIDIA Nemotron Nano 30B	nvidia/nemotron-3-nano-30b-a3b:free	128K	Efficient MoE; agentic dev tasks
Gemma 3 27B	google/gemma-3-27b-it:free	128K	Vision + tools
Llama 3.3 70B	meta-llama/llama-3.3-70b-instruct:free	128K	GPT-4 level general quality

Free tier caveats: Rate limits break autonomous loops. Never use :free models as the primary in a heartbeat agent or cron-triggered workflow.

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Tier 1 — Budget (≤$0.30/M input)

Use for: high-volume simple agents, summarisation, triage, notifications, memory-write agents.

Model	OpenRouter ID	Input	Output	Context	Strengths
MiMo-V2-Flash	xiaomi/mimo-v2-flash	$0.09	$0.29	256K	#1 open-source; ~Claude Sonnet 4.5 quality at 3.5% cost; hybrid thinking; tool calling
Gemini Flash Lite	google/gemini-2.5-flash-lite	$0.10	$0.40	1M	Fastest Gemini; high-volume tasks
Gemini 3.1 Flash Lite	google/gemini-3.1-flash-lite	$0.25	$1.50	1M	Outperforms 2.5 Flash Lite; Google's fastest
Grok 4.1 Fast	x-ai/grok-4.1-fast	$0.20	$0.50	2M	Best xAI tool-calling model; customer support & deep research
DeepSeek V3.2	deepseek/deepseek-v3.2	$0.26	$0.38	163K	~90% of GPT-5.4 quality at 1/50th cost; strong agentic tool use
MiniMax M2.5	minimax/minimax-m2.5	$0.20	$1.17	196K	80.2% SWE-Bench Verified; strong office/productivity agents

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Tier 2 — Mid ($0.30–$3/M input)

Use for: complex reasoning agents, code generation, multi-step workflows, agents that need reliable tool use at scale.

Model	OpenRouter ID	Input	Output	Context	Strengths
Claude Haiku 4.5	anthropic/claude-haiku-4-5-20251001	$1.00	$5.00	200K	Fast; extended thinking; full tool/computer-use support
Gemini 2.5 Pro	google/gemini-2.5-pro	$1.25	$10.00	1M	Strong reasoning; multimodal
GPT-5	openai/gpt-5	$1.25	$10.00	1M	Broad capability
GPT-5.4	openai/gpt-5.4	$2.50	$15.00	1M	Built-in computer use; unified Codex+GPT
Claude Sonnet 4.6	anthropic/claude-sonnet-4-6	$3.00	$15.00	200K	Best mid-tier agent model; iterative dev, codebase nav, computer use

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Tier 3 — Premium ($3+/M input)

Use for: mission-critical agents, long-running autonomous workflows, sensitive data, agents where errors are expensive.

Model	OpenRouter ID	Input	Output	Context	Strengths
Grok 3	x-ai/grok-3	$3.00	$15.00	131K	Strong reasoning; real-time data
Claude Opus 4.6	anthropic/claude-opus-4-6	$5.00	$25.00	200K	Strongest Anthropic model; full agent workflows; security-hardened
GPT-5.4 Pro	openai/gpt-5.4-pro	$30.00	$180.00	1M	Maximum capability; only for extreme use cases

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Audit Procedure

Step 1 — Discover Agents

Ask the user which agents to audit. Offer these options:

• All agents: scan ~/.openclaw/ for all workspace directories
• Single agent: user provides the path or agent ID
• Active openclaw.json: read model assignments from ~/.openclaw/openclaw.json

For each agent found, locate:

• SOUL.md — role, personality, scope
• AGENTS.md — rules, tools, workflows, memory instructions
• IDENTITY.md — name, status
• GOALS.md — what it's trying to achieve (if present)
• HEARTBEAT.md — autonomous schedule (if present)
• openclaw.json or the global config — current model assignment

Step 2 — Score Each Agent on 6 Requirement Dimensions

For each agent, score 1–5 on each dimension by reading SOUL.md and AGENTS.md:

2a. Task Complexity (1–5)

Score	Signal in workspace files
1	Simple Q&A, FAQ lookup, single-step responses
2	Summarisation, triage, basic memory writes
3	Multi-step reasoning, research, drafting
4	Code generation, complex agentic workflows
5	Deep coding, system design, long-horizon planning

2b. Tool Use Intensity (1–5)

Score	Signal
1	No tools listed in AGENTS.md
2	1–2 simple tools (search, send message)
3	3–5 tools with conditional logic
4	6+ tools, chained tool calls, external APIs
5	Computer use, bash execution, file system, browser

2c. Context Window Need (1–5)

Score	Signal
1	Short conversations, no documents
2	Single document < 10K tokens
3	Multiple documents or long chat history
4	Whole codebase, large reports, 50K+ tokens
5	Multi-document, multi-session, 100K+ tokens

2d. Response Volume (1–5)

Score	Signal
1	< 10 messages/day, manual trigger only
2	10–50 messages/day
3	50–200 messages/day
4	200–1000 messages/day
5	1000+ messages/day or high-frequency cron

2e. Autonomy Level (1–5)

Score	Signal in HEARTBEAT.md / AGENTS.md
1	Human-initiated only, supervised
2	Occasional autonomous tasks, human reviews output
3	Regular autonomous tasks with occasional check-ins
4	Full heartbeat schedule, minimal supervision
5	Fully autonomous 24/7, no human in loop

2f. Sensitivity (1–5)

Score	Signal
1	Public info only, no private data
2	Business-internal data
3	Client data, business financials
4	Credentials, auth tokens, personal data
5	Highly sensitive: financial, medical, legal, security systems

Step 3 — Calculate Composite Score & Assign Tier

Composite = (Complexity × 2) + (Tool Use × 2) + Context + Volume + (Autonomy × 1.5) + (Sensitivity × 1.5)
Max = 60

Composite Score	Recommended Tier
0–20	Tier 0 (Free) or Tier 1 (Budget)
21–35	Tier 1 (Budget)
36–45	Tier 2 (Mid)
46–60	Tier 2–3 (Mid to Premium)

Override rules (apply regardless of composite score):

• Sensitivity ≥ 4 → minimum Tier 2
• Sensitivity = 5 → minimum Tier 3 (Claude Opus recommended)
• Autonomy = 5 → never use free tier; minimum Tier 1
• Tool Use ≥ 4 → must use a model with confirmed tool-calling support
• Context ≥ 4 → must use a model with ≥ 100K context window

Step 4 — Select Recommended Models

For each tier, present:

1. Best Fit — optimal balance of capability and cost for this agent's profile
2. Cheaper Alternative — one tier down if the agent could manage
3. Upgrade Option — one tier up if reliability/quality needs to increase

Use the model table above. Prefer these combinations based on agent type:

Agent Type	Best Fit	Cheaper Alt	Upgrade
Simple assistant / FAQ	xiaomi/mimo-v2-flash	qwen/qwen3-235b-a22b:free	anthropic/claude-haiku-4-5-20251001
Triage / email classifier	deepseek/deepseek-v3.2	xiaomi/mimo-v2-flash	anthropic/claude-haiku-4-5-20251001
Research / summarisation	deepseek/deepseek-v3.2	google/gemini-3.1-flash-lite	anthropic/claude-sonnet-4-6
Productivity / office agent	minimax/minimax-m2.5	deepseek/deepseek-v3.2	anthropic/claude-sonnet-4-6
Code generation / dev agent	anthropic/claude-sonnet-4-6	xiaomi/mimo-v2-flash	anthropic/claude-opus-4-6
Agentic with tool chains	x-ai/grok-4.1-fast	deepseek/deepseek-v3.2	anthropic/claude-sonnet-4-6
Autonomous 24/7 heartbeat	deepseek/deepseek-v3.2	x-ai/grok-4.1-fast	anthropic/claude-sonnet-4-6
Sensitive data / security	anthropic/claude-sonnet-4-6	—	anthropic/claude-opus-4-6
Mission-critical / long-run	anthropic/claude-opus-4-6	anthropic/claude-sonnet-4-6	—

Step 5 — Generate openclaw.json Snippet

For each agent, produce a ready-to-paste config block:

// Agent: <agent-name>
// Role: <one-line role from SOUL.md>
// Tier: <tier> | Composite score: <score>/60
// Recommended: <model-id>
{
  agents: {
    list: [
      {
        id: "<agent-id>",
        model: {
          primary: "<recommended-model-id>",
          fallbacks: ["<cheaper-alt-id>"],
        },
      },
    ],
  },
}

If the agent is autonomous (Autonomy ≥ 4), also recommend adding at least one paid-tier fallback — never leave a free model as the only fallback for a heartbeat agent.

Step 6 — Present Summary Report

Output a single table covering all audited agents:

## Model Advisor Report

| Agent | Complexity | Tool Use | Context | Volume | Autonomy | Sensitivity | Score | Tier | Recommended Model |
|-------|-----------|----------|---------|--------|----------|-------------|-------|------|-------------------|
| ...   |           |          |         |        |          |             |       |      |                   |

### Cost Estimate

Estimated monthly cost at current volume:
- <Agent A>: ~$X.XX/month at <tier> using <model>
- <Agent B>: ~$X.XX/month at <tier> using <model>

### Key Recommendations

1. ...
2. ...

### Next Steps

- To apply: paste the config snippets above into your openclaw.json
- To verify model availability: run `openclaw model list` or visit https://openrouter.ai/models
- To update the model live: `/model <provider/model-id>` in any chat session
- Pricing may change — always verify current rates at https://openrouter.ai/pricing

---

Quick Reference — Model Selection Heuristic

Is the agent autonomous (heartbeat/cron)?
  YES → Never use :free tier
  NO  → Free tier OK for dev/low-volume

Does it use 4+ tools or computer use?
  YES → Claude Sonnet/Opus, Grok 4.1 Fast, or GPT-5.4
  NO  → DeepSeek V3.2 or MiMo-V2-Flash are excellent

Does it handle sensitive data (score ≥4)?
  YES → Claude Sonnet minimum; Opus for score 5
  NO  → Any tier appropriate to complexity

Is cost the primary concern?
  YES → DeepSeek V3.2 ($0.26/$0.38) gives best value/quality ratio
  NO  → Claude Sonnet 4.6 is the safest all-round choice

Need 1M+ context window?
  YES → Gemini 3.1 Flash Lite, Grok 4.1 Fast (2M), or GPT-5/5.4

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Integration with Other Skills

After running this audit:

• Current model is wrong tier → apply the generated snippets to openclaw.json
• Agent has no tools but needs them → run openclaw-agent-architect to redesign SOUL + AGENTS
• Agent is autonomous but has no failover → run openclaw-config to add failover config
• Model spend too high across team → consider openclaw-multi-agent to route cheaper models to simple agents

Always verify current pricing before committing to a model: → https://openrouter.ai/pricing → https://openrouter.ai/models