OpenAI GPT-4 vs. Groq Mistral-8x7B

Original Article ↗ Hacker News Discussion ↗

Meaning of “N/A”

  • Several comments debate whether “N/A” means “not available” or “not applicable.”
  • Some argue “not applicable” is standard (backed by dictionaries), others say they’ve always used it as “not available.”
  • Consensus: in practice it’s overloaded and often just means “no value here,” whichever wording you prefer.

Accuracy vs Speed and Hallucinations

  • Many emphasize that for high‑stakes tasks, correctness matters far more than speed.
  • Examples include a single missing “not” flipping a critical parameter, or repeated confident hallucinations.
  • Multiple responses argue that if an LLM doesn’t know a fact, more sampling won’t fix it; ensembles or retrieval are needed.
  • Others note that models will often change answers when challenged, even if they were correct, so “self‑checking” via a simple prompt isn’t reliable.

Retrieval-Augmented and Hybrid Approaches

  • RAG (LLM + external data source) is highlighted as the standard way to mitigate limited context and factual gaps, but described as brittle.
  • Some argue current LLMs don’t distinguish “facts vs grammar,” which limits how well they can decide when to look things up.

LLMs for HTML Parsing and Scraping

  • Many are skeptical of using LLMs to parse structured formats like HTML, calling it slow, expensive, non‑deterministic, and prone to hallucinations.
  • Suggested alternatives:
    • Use LLMs to generate or update deterministic parsers/selectors.
    • Use LLMs only where HTML is highly variable and human maintenance is costly.
  • Several say the benchmark prompt is underspecified; better results would come from:
    • Extracting one field at a time.
    • Not asking for full JSON in a single step.
    • Investing more in system/“character” setup and workflow over clever wording.

Groq, Hardware, and Performance

  • Groq’s speed is attributed to custom chips and Mixtral’s sparse Mixture‑of‑Experts architecture, with many chips networked together.
  • Commenters question real‑world economics and scalability (memory footprint, multiple models, continuous uptime) versus GPUs.

GPT‑5 and Competition

  • Some speculate OpenAI may delay GPT‑5 due to market dominance and incremental gains; others think competition from Claude and others is eroding that lead.
  • There’s disagreement on how big the next leap will be; improvements are expected but not guaranteed to be dramatic.

Energy Use and Ethics

  • Concerns raised about burning large amounts of compute to do work trivially handled by traditional parsers.
  • Counterpoints: AI has broad practical value whereas proof‑of‑work crypto is seen as wasteful; efficiency is improving, and economics already incentivize lower‑energy methods.