Side-by-side

Not Diamond vs Cursor

Not DiamondCursor

Bottom line: Not Diamond for teams running multiple LLMs in production; Cursor for working developers who want agentic coding beyond basic autocomplete.

	Not Diamond Intelligent LLM router that picks the best model per query Visit	Cursor AI-native code editor with agents, full-codebase context, and multi-model support Visit
Votes	0	0
Pricing	Freemium	Freemium
Category	Coding	Coding
Tags	llm-routermodel-routingcost-optimizationai-infrastructurellmops	write-code
Best for	Teams running multiple LLMs in production Builders of coding and AI agents Cost-sensitive LLM applications	Working developers who want agentic coding beyond basic autocomplete Teams already invested in the VS Code ecosystem Engineers who want to switch freely between Claude, GPT, and Gemini
Pros	Directly targets cost savings without quality loss Supports custom routers trained on your own data Low added router latency (cited ~100-150ms) Free early-access tier to evaluate Works with major providers and coding agents	Agent-centric design goes well beyond autocomplete — agents can read full-codebase context, coordinate multi-file changes, run tests, and present finished work for review, which suits ambitious multi-step tasks. Genuine model flexibility lets you pick Claude, GPT, or Gemini per task, or fall back to Cursor's own Composer model, so you can balance capability against cost rather than being locked to one provider. Building on a VS Code fork preserves familiar extensions, keybindings, and themes, dramatically lowering the switching cost for teams already in that ecosystem. A capable surface area beyond the desktop editor — a CLI, cloud agents that run autonomously and in parallel, and Bugbot for agentic code review — supports both interactive and hands-off workflows. Team and Enterprise tiers add the controls organizations actually need, including centralized billing, usage analytics, SSO, pooled usage, and repository/model access controls.
Cons	Routing benefits depend heavily on your specific workload Exact per-token pricing is not fully published Vendor-cited savings figures need independent validation Adds an external dependency and network hop Crowded, fast-moving competitive category	The usage-based credit system makes spend hard to predict — enabling premium models or aggressive agent use can swing a $20 plan to several times that amount in a single month. Delegating to agents introduces a real learning curve: getting reliable results depends on writing good rules, scoping tasks well, and reviewing AI output carefully rather than trusting it blindly. The pending SpaceX/xAI acquisition leaves open questions about long-term product direction and model neutrality that buyers can't fully evaluate yet. Heavy reliance on frontier models and cloud agents raises privacy and data-handling considerations that teams must configure deliberately via privacy mode and access controls.

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