How to Choose the Right LLM: Intelligence, Speed, Cost, and Use Case

There’s no single “best” large language model—only the one that fits your goals, budget, and workload. Benchmarks like the Artificial Analysis Intelligence Index give you a starting point, but your choice should also factor in speed, cost, and the kinds of tasks you run. Here’s a simple way to think about it.

Start With Your Use Case

• Agents and automation – You often want strong instruction-following and tool use. Models like Claude Opus 4.6 and GLM-5 tend to do well here.
• Coding and long code context – GPT-5.2 Codex and similar models are built for this; check Artificial Analysis for coding-specific evals.
• Knowledge and reasoning – Gemini 3.1 Pro currently leads the aggregate index; Claude and others are close behind.
• High volume / low cost – Open or cost-optimized models like DeepSeek V3.2 can be much cheaper; use Artificial Analysis to compare price per 1M tokens.

Weigh Intelligence, Speed, and Cost

Intelligence (e.g. Artificial Analysis Intelligence Index) tells you how capable a model is across reasoning, knowledge, and coding. Higher is better, but only if you need that level—often a slightly lower-scoring model is “good enough” and cheaper.

Speed (output tokens per second) matters for interactive apps and throughput. Artificial Analysis tracks this; if latency is critical, pick a model that’s fast enough for your UX.

Cost (USD per 1M tokens) adds up at scale. Use benchmarks to find the least expensive model that still meets your quality bar—and consider mixing a small/fast model for easy queries and a bigger one for hard ones.

Use Benchmarks, Then Validate Yourself

Use Artificial Analysis and our list of leading LLMs and comparison posts to narrow the field. Then run your own tests on your data and prompts. Benchmarks don’t replace real-world evaluation—they make it easier to know where to start.

For more on LLMs, AI, and technology, browse LilacPearls.