GPT-5.6 Sol, along with Terra and Luna, will launch publicly this Thursday

Original Article ↗ Hacker News Discussion ↗

OpenAI’s upcoming GPT‑5.6 “Sol/Terra/Luna” models are drawing strong interest from developers who currently juggle OpenAI and Anthropic tools for coding, data analysis, and interface design. Many hope Sol will match or approach Anthropic’s Fable in reasoning, orchestration, and computer use while keeping GPT‑5.5’s speed and lower cost, though some doubt it can reach Mythos‑class intelligence given likely similar model size. Commenters also debate OpenAI’s naming and reasoning-token design choices, the value of aggressive goal‑seeking agents versus tightly steered ones, and whether subscription access and capacity advantages will let OpenAI outcompete Anthropic’s more constrained offerings.

Model quality vs. Fable/Claude/Gemini

  • Many see GPT‑5.5 as strong for data analysis and coding, but notably weaker than Fable for deep math/linear algebra, orchestration, and long-horizon work.
  • Early preview feedback on GPT‑5.6 Sol: “very capable,” better instruction following and “tenacity,” fixes many 5.5 issues, but still generally perceived as below Fable in raw “smartness.”
  • Some claim 5.6 Sol feels Fable‑level for coding and computer use; others are skeptical this can match a much larger “Mythos‑class” model.
  • Several users still prefer Claude/Fable for backend, architecture, and interface design; GPT models are praised for speed, crispness, and pedantic code review.

Reasoning tokens, context, and agents

  • An OpenAI employee confirms: in the Responses API, reasoning tokens are discarded after each user turn; only input/output tokens are carried forward.
  • Rationale: dropping reasoning tokens allows more work within context before needing lossy compaction, especially with older, shorter‑context reasoning models.
  • Some users feel this design explains why GPT‑5.5 loses “session understanding” over turns; workarounds include saving running context into markdown and re‑feeding it.
  • Others emphasize logging and explicit orchestration (decision logs, task queues, project maps) to make agent work auditable and transferable across models.

Performance, pricing, and size debates

  • Official pricing for Sol reportedly matches GPT‑5.5; some argue Sol/Terra/Luna are mainly rebranded 5.6/mini/nano with extra post‑training, not larger models.
  • One camp predicts similar tokens/second to 5.5 (implying similar size); others note tok/s isn’t a clean proxy for parameters and “bigger isn’t always better.”
  • There’s ongoing argument over whether Mythos‑class models are fundamentally larger than anything OpenAI currently exposes.

Developer UX: Codex vs Claude Code

  • Many prefer Codex for responsiveness, steering, shorter responses, and better image handling; others dislike missing features like /revise and /undo.
  • Claude Code is seen as more powerful for complex engineering but slower, more verbose, and heavily rate‑limited.
  • Some users build tools to share context across Codex/Claude and normalize transcripts.

Naming, access, and product strategy

  • New Sol/Terra/Luna names draw criticism as confusing and marketing‑driven; defenders say they’re better than “mini/nano” which sounded “inferior.”
  • Preview access is being expanded before a broader Thursday launch; exact subscription/TPS details for $20 Codex‑tier users remain unclear.
  • Anthropic’s shifting Fable availability and capacity limits are seen as a weakness versus OpenAI’s larger infrastructure.