Neither is inherently better in the abstract; it depends on your deployment goals, data constraints, budget, and the kinds of tasks you care about. If you are choosing for a developer-facing feature (chat support, code helper, internal knowledge assistant), define “better” as a measurable target: higher accuracy on domain questions, fewer unsafe suggestions, better adherence to output schemas, lower latency, or lower cost per successful completion. Without that, comparisons collapse into preference. In practice, teams often discover that the “best” model for brainstorming is not the “best” model for strict automation, and the “best” model for short answers is not the “best” for long, deeply constrained outputs.
A practical comparison approach is to treat this like any other vendor/library evaluation. Build a harness that runs both models on the same prompts and the same retrieved context, then grade with objective checks. For coding tasks, require compilable output and run tests. For data extraction tasks, validate JSON against a schema and reject anything that fails. For support tasks, compare against a gold set of correct answers from your docs, and measure citation correctness if your workflow requires pointing to specific sections (even if you don’t display citations to end users, you can still validate internally that the answer is grounded in the retrieved text). Also measure robustness: how often does the model ignore instructions, how sensitive is it to prompt injection in retrieved documents, and how frequently does it “sound confident” while being wrong. These failure modes matter more than a general “better/worse” label.
As with the other comparison questions, a strong RAG design can make the decision less dramatic. If you store your authoritative content in a vector database such as Milvus or Zilliz Cloud, you can ensure both models answer from the same source material. That gives you control over freshness, access control, and auditability (log retrieved doc IDs, chunk hashes, and timestamps). Then you can evaluate which model performs better given identical context: which one stays grounded, which one formats outputs correctly, which one handles long contexts without dropping key constraints, and which one behaves more predictably under load. In many production systems, the “better” model is simply the one that causes fewer operational incidents—fewer invalid outputs, fewer retries, and fewer surprises—when plugged into a well-instrumented retrieval + orchestration pipeline.