| 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.
References
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.
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 |
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 |
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 |
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, scopeAGENTS.md — rules, tools, workflows, memory instructionsIDENTITY.md — name, statusGOALS.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:
- Best Fit — optimal balance of capability and cost for this agent's profile
- Cheaper Alternative — one tier down if the agent could manage
- 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
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
