GPT-5.6

Original Article ↗ Hacker News Discussion ↗

OpenAI’s release of GPT‑5.6 (Sol, Terra, Luna) is being weighed against Anthropic’s Claude Opus/Fable models and various coding “harnesses” like Codex, Claude Code, OpenCode and Pi. Commenters highlight strong benchmark results and better token efficiency for GPT‑5.6, but question how much is marketing or “benchmaxxing,” and note that Sol can aggressively consume usage limits in agentic workflows. A recurring theme is trade‑offs: OpenAI is seen as cheaper, faster and less restrictive but less trustworthy, while Anthropic’s models are praised for reasoning and code quality yet criticized for unstable quotas, strict safety filters and a more closed ecosystem.

Model performance & benchmarks

  • Many see GPT‑5.6 Sol as a strong coding/model upgrade over GPT‑5.5, with better planning, fewer bugs, and more “get it done” behavior, sometimes rivaling or surpassing Fable in day‑to‑day work.
  • Others report Sol still trails Fable/Mythos on hard reasoning or code‑reading tasks; some say Fable remains uniquely good at deep code review and architecture.
  • Benchmarks spark debate:
    • OpenAI’s “Agents’ Last Exam” and other in‑house graphs are criticized as cherry‑picked, with misleading axes and non‑max Fable settings.
    • SWE‑Bench Pro is called “broken” or saturated by multiple commenters; some note OpenAI now deemphasizes it while promoting DeepSWE and FrontierCode where GPT models score better.
    • Sol’s 7.8% on ARC‑AGI‑3 is seen as genuine progress but still far from “AGI”.

Developer experiences & harnesses

  • Large subthread on Codex vs Claude Code:
    • Many praise Codex for lower drama, better stability, clearer permissions, and more generous usage; they dislike Claude Code’s bloat, bugs, outages, and opaque quotas.
    • Others feel Claude/Fable produce more elegant, intention‑aligned code and better design/UI work, with Codex described as more literal, verbose, or “austere”.
    • A common pattern: use both—one plans, the other implements or reviews; some chain multiple models (e.g., Fable + GPT + DeepSeek) via OpenCode, Pi, or other harnesses.

Pricing, quotas & efficiency

  • 5.6 pricing (Sol/Terra/Luna) is viewed as competitive versus Fable; Terra/Luna in particular look attractive on intelligence‑per‑dollar graphs.
  • Strong focus on token efficiency: several note Sol is far more efficient than Fable and Opus on cost‑per‑task benchmarks, but:
    • Sol Ultra and heavy subagent use can burn through 5‑hour windows extremely fast; some users see entire windows consumed in minutes.
  • OpenAI’s bankable quota resets are widely praised; Anthropic’s random resets and Fable’s tight subscription caps are widely disliked.

Safety, guardrails & policy

  • Fable’s safety filters (especially around biology, security, and even innocuous code with “DNA” or “security” terms) are a major pain point; many biologists and security researchers report abandoning Anthropic over this.
  • Some welcome OpenAI’s stated stance against over‑blocking cyber‑defense tasks, but others already see new “additional safety checks” and slowdowns in 5.6.
  • There is unease about all major labs’ ties to governments and defense (Palantir, Maven, etc.); some commenters say they now prefer open or non‑US models.

Prompting behavior & UX

  • OpenAI’s own guidance (shorter prompts, avoid generic “be concise”) is discussed heavily:
    • Some like the shift away from “verbal diarrhea”; others complain that needing long, precise brevity instructions defeats the purpose of “just be short”.
  • Users report 5.6 is slower than 5.5 in many harnesses, especially at higher reasoning levels, but often higher quality.
  • The new Sol/Terra/Luna naming is called both intuitive (sun/earth/moon by size) and confusing marketing compared to older “mini/nano” or simple “Pro/Max” labels.

Miscellaneous reactions

  • The long‑running “pelican on a bicycle” SVG test shows clear qualitative differences across Luna/Terra/Sol and reasoning levels; some argue the test is now in training data and partly saturated, others still find it a useful visual sanity check.
  • Overall sentiment: excitement about strong competition to Fable and better cost/efficiency, mixed with skepticism about benchmark marketing, safety trade‑offs, and rapidly escalating complexity in model/effort choices.