Which LLM Should I Use?
Answer 6 quick questions and get a personalized recommendation backed by benchmark data from BenchLM.ai.
Question 1 of 6
What will you primarily use the model for?
How the LLM selector makes its recommendation
The selector evaluates 56 models using data current through September 2026. Use case determines which benchmark categories matter most, while budget and context filters narrow the field.
Privacy and deployment preferences can require or favor self-hostable open-weight models. The reasoning preference separates deliberate reasoning models from faster direct-answer models. Every result links to a model profile, current pricing, and a direct comparison so you can inspect the evidence before choosing.
FAQ
How does the LLM selector work?
Our selector uses your answers about use case, budget, context needs, privacy and deployment constraints, speed, and reasoning preferences to filter and rank models from our benchmark database. Models are scored using weighted category averages that match your stated priorities.
Which LLM should I use for coding?
If coding is the whole job, Gemini 3.5 Flash, GPT-5.5, Claude Opus 4.7, GLM-5.1, and Kimi K2.7 Code are all available in this selector with benchmark-backed tradeoffs. Use the coding and agentic filters when you need software-work performance rather than a generic overall score.
What is the best free LLM?
The strongest open-weight options in BenchLM's current data are GLM-5 (Reasoning), GLM-5.1, and Qwen3.5 397B (Reasoning). These can be self-hosted or accessed through affordable API providers depending on the deployment path you want.