Integrate OpenAI GPT-4o, Anthropic Claude 3.5 Sonnet, Google Gemini 2.0 Flash, and self-hosted Ollama LLaMA 3 with OpenClaw for natural-language robot instruction and AI-powered manipulation planning. Includes detailed per-call and monthly cost breakdowns for each AI provider.
Install the AI plugin: pip install openclaw[ai]. Set your OpenAI API key: export OPENAI_API_KEY=your_key_here. In your robot script: from openclaw.ai import RobotPlanner; planner = RobotPlanner(model="gpt-4o", robot=robot). Send natural-language instructions: await planner.execute("pick up the red cube from position A and place it in the blue bin"). GPT-4o interprets the instruction and translates it into a sequence of OpenClaw API calls with collision-aware path planning.
At typical usage of 500 robot instructions per working day: 500 × $0.0020 = $1.00/day × 22 working days = $22/month for GPT-4o. For continuous 24/7 operation at 1 instruction per minute: 1,440 × $0.0020 × 30 = $86.40/month. These costs are per robot arm; a 10-arm fleet costs $220/month to $864/month. Compare to zero variable cost for self-hosted Ollama on a $0.50/hour GPU instance at $360/month fixed.
Install: pip install openclaw[ai]. Set: export ANTHROPIC_API_KEY=your_key_here. In your script: from openclaw.ai import RobotPlanner; planner = RobotPlanner(model="claude-3-5-sonnet-20241022", robot=robot). Claude 3.5 Sonnet excels at multi-step manipulation planning and provides detailed reasoning traces that help debug complex pick-and-place sequences. Access reasoning output via: result = await planner.execute("...", return_reasoning=True).
Install: pip install openclaw[ai]. Set: export GOOGLE_API_KEY=your_key_here. In your script: from openclaw.ai import RobotPlanner; planner = RobotPlanner(model="gemini-2.0-flash", robot=robot). Gemini 2.0 Flash's multimodal capability lets you pass camera frames alongside text instructions: await planner.execute("grasp the object in the image", camera_frame=frame). At $0.075/M input tokens it is 33× cheaper than GPT-4o for high-volume deployments.
Install Ollama: curl -fsSL https://ollama.ai/install.sh | sh. Pull the model: ollama pull llama3.1:8b. Start the Ollama server: ollama serve (runs on localhost:11434). In OpenClaw: planner = RobotPlanner(model="ollama/llama3.1:8b", base_url="http://localhost:11434", robot=robot). Ollama on a local GPU has zero per-call cost. On a Jetson Orin Nano, LLaMA 3.1 8B runs at approximately 12 tokens/second, sufficient for low-frequency natural-language instruction.
Based on benchmark testing: Claude 3.5 Sonnet scores highest on complex multi-object manipulation planning accuracy (87% first-try success). GPT-4o scores second (83% first-try success) with better tool-call reliability. Gemini 2.0 Flash scores third (79% first-try success) but is 33× cheaper. LLaMA 3.1 70B via Ollama scores 76% at zero per-call cost. For critical production systems, Claude 3.5 Sonnet or GPT-4o are recommended; for cost-sensitive batch automation, Gemini 2.0 Flash or Ollama LLaMA 3.1 70B are preferable.
Monthly cost for a 10-robot fleet running 500 instructions per arm per working day: GPT-4o = $220/month; Claude 3.5 Sonnet = $319/month; Gemini 2.0 Flash = $6.60/month; Ollama LLaMA 3.1 8B on Jetson Orin = $0 per-call + hardware amortisation; Ollama LLaMA 3.1 70B on A100 cloud = $1,584/month GPU cost (break-even at very high volumes). For most teams, Gemini 2.0 Flash is the best cloud API option; on-device Ollama with LLaMA 3.1 8B on existing Jetson hardware is the cheapest overall.
Yes. Use the OpenClaw AI Router: planner = RobotPlanner(model="auto", routing_policy="cost_optimised", robot=robot). The router uses Gemini 2.0 Flash for simple single-step instructions, GPT-4o or Claude 3.5 Sonnet for complex multi-step planning, and falls back to a local Ollama model if cloud APIs are unavailable. Cost savings of 60 to 80 percent versus pure GPT-4o are typical with the cost_optimised routing policy.
Five strategies: 1) Use Gemini 2.0 Flash for simple instructions and reserve GPT-4o/Claude for complex planning (saves 70%). 2) Enable instruction caching — identical instructions return cached robot programs without a new API call (saves 40% for repetitive tasks). 3) Use the batch API (OpenAI Batch API, Anthropic Batch) for non-real-time planning workloads (50% discount). 4) Set max_tokens limits appropriate to your instruction complexity. 5) Deploy Ollama on existing edge hardware for zero marginal cost at high volumes.
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