What makes an LLM good for AI agents specifically?

Raw intelligence isn't enough. Agent LLMs need reliable tool calling, correct function schemas, multi-step planning across long task chains, graceful error recovery when tools fail, strong context retention over extended sessions, and consistent instruction adherence so guardrails don't drift as the conversation grows.

Can open-source LLMs compete with proprietary ones for agent tasks?

Yes. In 2026, the gap has nearly closed, and several open-source models now compete directly with the best LLMs from proprietary providers. Fine-tuned open models like Qwen 3.6 Plus and GLM-5.1 can outperform generalist frontier models on narrow, well-defined tasks. The tradeoff is that you build more of the agent infrastructure yourself.

Should I use one LLM for everything or mix models?

Most production systems mix models. A common pattern is routing simple, high-volume tasks to a smaller, faster model like Sonnet 4.6 or DeepSeek V4-Flash, and escalating complex reasoning to a frontier model like Opus 4.7 or GPT-5.5. This keeps costs manageable without sacrificing quality where it matters.

How do I choose between Claude, GPT, and Gemini for my agent?

Start with your ecosystem. If you're deep in Google Workspace, Gemini 3.1 Pro gives you native integration. If you're in Microsoft 365, GPT-5.5 and the Assistants API reduce boilerplate. If you're ecosystem-agnostic or building coding agents, Claude Opus 4.7 leads on software engineering and multi-tool orchestration benchmarks. Test on your actual workload rather than relying on benchmark rankings alone.