NemoClaw cuts agent inference cost 10x
LangChain and NVIDIA released NemoClaw on July 8, combining Nemotron 3 Ultra with LangChain Deep Agents Code at $4.48 per eval run versus $43.48 next-best.
LangChain and NVIDIA released the NemoClaw Deep Agents Blueprint on July 8. The stack has three components: NVIDIA Nemotron 3 Ultra as the base model, LangChain Deep Agents Code as the agent harness (handling planning, tool use, memory, and task execution), and NVIDIA OpenShell as the secure sandbox runtime for agent-tool interactions.
On LangChain’s enterprise agent evaluation suite, the combination scored 0.86 aggregate at $4.48 per run. The next-best comparable model came in at $43.48, a 9.7x difference. NVIDIA and LangChain did not disclose what hardware configuration produced the $4.48 figure.
Nemotron 3 Ultra is NVIDIA’s open-weight model, available for deployment across cloud providers. Enterprises can run it on their own infrastructure, fine-tune it, and adjust independently of model provider pricing changes. The blueprint is available now for enterprise evaluation.
What it means for you
The cost gap is real, but it comes with conditions. Running NemoClaw requires NVIDIA hardware and a self-managed inference stack. It is not a drop-in replacement for GPT-5.6 Terra or Claude Sonnet 5. It is a reference architecture for teams that can commit to running their own model infrastructure.
For operators running high-volume agentic workloads where per-run cost is the binding constraint, the math is direct: a workload at $4,400 per month on the next-best model drops to $440 per month on NemoClaw, on the right hardware.
One important caveat: LangChain runs NemoClaw on LangChain’s own evaluation suite, which is not a neutral benchmark. Independent production results are needed before taking the 10x claim at face value. For a full breakdown of when the open-stack trade-off actually works, see NemoClaw and what 10x cost savings actually takes.