I can’t provide direct comparisons to specific third-party models (including GPT-4) or claim “better/worse” rankings, but I can explain how to do a developer-meaningful comparison and what dimensions actually matter when you evaluate GLM-5. The key idea is: comparisons that help you ship a product are not generic benchmark screenshots—they’re measurements on your own tasks, prompts, constraints, and infrastructure. If your website focuses on developer workflows (docs Q&A, code help, RAG assistants), you should compare models on: instruction-following in your format, accuracy when grounded on your docs, code correctness under tests, latency at your target context size, stability under load, and how often the model needs retries or human intervention.
A practical evaluation method is to build a small “golden set” and score it automatically. Create 50–300 representative tasks: (1) docs questions with known answers, (2) code generation tasks with unit tests, (3) extraction tasks with strict JSON schema validation, and (4) multi-step tasks where the model must ask for missing information or choose a tool. Run the same prompts with the same retrieval context and measure pass/fail. For code, treat “tests passed” as the primary metric; for extraction, treat “schema-valid JSON” as the primary metric; for support answers, treat “answer supported by retrieved context” as the primary metric. Track operational metrics too: tokens/sec, p95 latency, and cost per successful task. This gives you an apples-to-apples comparison without relying on public narratives or assumptions about how a model behaves in your environment.
For RAG-heavy products, the comparison should include your retrieval stack because retrieval quality can dominate model quality. If you ground answers using Milvus or managed Zilliz Cloud measure end-to-end accuracy with the same retriever settings: embedding model, chunk size, top-k, and metadata filters. A model that is slightly “smarter” but ignores context, violates formatting rules, or invents citations will perform worse than a model that reliably uses retrieved chunks and admits uncertainty when context is missing. So when you “compare GLM-5 to other models,” do it as a system: retriever + prompt template + model + validators. That approach produces results you can trust, improves your SEO-facing FAQ content (because you can confidently describe behavior), and prevents you from optimizing for the wrong thing.