GPT-5.6 Sol Ultra will be in Codex
OpenAI’s forthcoming GPT‑5.6 “Sol Ultra” model, slated for integration into its Codex coding agent, is stirring interest both for its multi‑agent “subagent” orchestration and for claims of major inference cost reductions. Commenters debate how much of this is genuinely new versus just better prompting and packaging of existing capabilities, and compare it heavily to Anthropic’s Fable and other frontier models in terms of coding quality and economics. Underneath the model‑naming hype, many focus on token costs, corporate usage limits, and the broader arms race between US and Chinese labs, questioning whether current business models and secrecy around “compute multipliers” are sustainable.
Inference cost cuts & “compute multipliers”
- Thread connects GPT‑5.6 Sol / Ultra to reports that OpenAI found a way to halve inference costs.
- People speculate on techniques: shared KV/prefix caching, speculative decoding, more efficient architectures, DeepSeek‑style tricks (draft models, DSpark‑like systems).
- Several argue simple response caching or prefix caching won’t help much for long‑tail, long‑context workloads (e.g., coding), where costs matter most.
- “Compute multipliers” are seen as the core trade secrets of labs; keeping them secret is compared to quant trading algorithms. Others criticize this secrecy as anti‑scientific and socially harmful.
Sol Ultra, Pro, subagents & workflows
- Sol Ultra in Codex is described (based on source code) as: same model/“max” effort, plus a prompt nudge to proactively use subagents. No special backend mode exposed.
- GPT‑5.5 Pro is believed to use parallel test‑time compute (multiple passes + a selection/synthesis model), explaining its much higher effective cost.
- Comparisons drawn to Claude Code’s “ultracode” / dynamic workflows: scripts that orchestrate many subagents deterministically, with some disagreement on how mature or “broken” these are.
Model quality, competition & access
- Many see Fable/Mythos as current coding leaders, especially on complex, multi‑step tasks (e.g., reverse‑engineering hardware).
- GPT‑5.5 Pro is praised for quality but criticized as prohibitively expensive in API usage; some hope 5.6 Sol Ultra narrows the gap at subscription prices.
- Some users report 5.5 getting “lazier”/worse recently in Codex, possibly as preparation for 5.6, but this is anecdotal.
- DeepSeek’s open research and low prices are repeatedly cited as pressure on US labs; debate over whether Chinese labs are ahead on efficiency or just structurally forced to optimize.
Enterprise usage, costs & bubble worries
- Corporate users report already seeing 5.6‑Sol Ultra; management initially gamified “most tokens used,” then abruptly pivoted to warnings about high token spend and urging cheaper models.
- Multiple commenters see this as an early sign the economic bubble may deflate: tools are useful but too expensive to replace human work at scale.
- Strategies mentioned: use strong models for planning, smaller ones for “grunt work”; use local models for most tasks and cloud LLMs only when stuck.
Reliability, safety & automation
- Strong skepticism about embedding stochastic LLMs directly in critical, automated business processes: businesses want determinism, auditability, and stable behavior.
- Some argue you can build reliable systems from stochastic parts with feedback and tests; others counter that current LLMs can’t truly learn from deployment‑time feedback and remain opaque.
Branding, naming & UX
- Confusion and annoyance over non‑descriptive names (“Sol”, “Ultra”, “Codex”) versus technical labels; others note marketing and ease‑of‑use often trump descriptive naming.