Two new Gemini models, reduced 1.5 Pro pricing, increased rate limits, and more

Original Article ↗ Hacker News Discussion ↗

Google’s latest Gemini updates introduce two new models, steep price cuts, and higher rate limits, positioning the service as a cheaper alternative to rival frontier models like GPT‑4o and Claude 3.5. Commenters debate whether aggressive pricing and Google’s in‑house TPU infrastructure can offset concerns about model quality, flaky APIs, overzealous safety filters (including a notorious “recitation” error), and poor documentation. Many see potential in deep product integrations and improved safety‑filter control, but question Google’s reliability, data‑handling guarantees, and long‑term product commitment.

Pricing & Competitive Positioning

  • Major price cuts for Gemini 1.5 Pro are widely noted; input/output now significantly cheaper than GPT‑4o and Claude Sonnet (at least as initially advertised).
  • Some see this as Google leveraging TPUs and in‑house infra to undercut rivals; others question whether this is “dumping” vs just lower costs.
  • Confusion over pricing: discrepancies between Google AI Studio, Vertex AI, and third‑party platforms, plus a quietly changed output price on the docs.
  • Debate whether Google is pursuing an “Android-style” strategy: slightly worse but much cheaper model aiming for oligopoly rather than dominance.

Model Quality & Benchmarks

  • Mixed views: some say Gemini is “not as smart” as GPT‑4o/Claude and has high hallucinations and looping behavior.
  • Aider code benchmarks show the new 1.5 Pro revision roughly flat vs the previous version and lagging behind o1, Claude Sonnet, etc.
  • Others report Gemini outperforming Llama on puzzles and being very reliable for function calling, with decent price/performance for many tasks.

Developer Experience & Reliability

  • Strong criticism of the Gemini API: flaky, changing behavior, unstable safety settings, broken agent scaffolding, incorrect/outdated docs, and unannounced API changes.
  • Some say building real products on it was “futile,” even with heavy incentives and credits from Google.
  • A minority say using it via AI Studio is “decent” and praise the free quota for experimentation.

Safety Filters & Recitation Issues

  • Prior versions had aggressive safety filters that blocked benign queries (economics questions, NFL quarterback lists, some novels).
  • “Recitation” errors (blocking outputs similar to training data) are called a show‑stopper for production apps; examples include trivial prompts like “Who is Google?” or boilerplate code.
  • One commenter believes the latest models have mitigated recitation; others remain wary and call this problem a major trust breaker.
  • New release makes most safety filters opt‑in, seen as a crucial improvement.

Product Integration, Moats & Usability

  • Some think Google’s moat could be deep integration with Gmail, Drive, Maps, Android, and Workspace.
  • Actual shipped integrations (e.g., Gmail search assistant, YouTube video summaries) are described as slow, inaccurate, or borderline useless, undermining that moat narrative.
  • Concerns that AI inference costs and Google’s “ship something fast” culture lead to half‑baked, resource‑constrained features.

Privacy & Data Use

  • Confusion over data privacy: consumer Gemini often uses data for training, while paid API and certain enterprise/Vertex offerings explicitly do not.
  • Some argue only on‑prem/local LLMs truly avoid leakage risk; others counter that regulated, BAA/HIPAA‑style cloud setups are “private enough” for most.

Code Assist & Tooling

  • Gemini Code Assist is widely judged inferior to GitHub Copilot and Claude‑powered tools (e.g., Cursor, Aider) in speed and usefulness.
  • A few users still find Gemini Pro strong for targeted, complex coding tasks via generic IDE extensions.