Which LLM Should I Use?
Answer 6 quick questions and get a personalized recommendation backed by benchmark data from BenchLM.ai.
With 32 AI models in BenchLM's benchmark database as of July 2026, choosing the right one is harder than ever. Performance varies dramatically by task — the best coding model isn't necessarily the best for writing, and the cheapest option may cost you in quality. This quiz uses real benchmark data from BenchLM.ai to match your requirements to the models that actually perform best for your use case.
The recommendation engine scores each model using a weighted algorithm based on your answers. Use case selection determines which benchmark categories matter most (coding, math, knowledge, or reasoning). Budget and context window filters narrow the field, the privacy & deployment question lets you require or prefer self-hostable open-weight models, and the reasoning question picks between deliberate step-by-step models and fast direct-answer models. Results link directly to detailed model profiles, provider pricing, and head-to-head comparisons so you can verify the recommendation with data.
Question 1 of 6
What will you primarily use the model for?
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.