GPT-5.6 の再帰的自己最適化により GPT-5.4 の知能コストが 13 倍低下し価格も大幅に引き下げられる

One of our big “hero charts” a year ago (eventually adopted by Demis) made the stunning observation that, holding LMSys Elo constant, GPT4 level intelligence fell by 1000x over 18 months:

Back then, it was unclear if these were “noob gains” - going from unoptimized to optimized, going from completions to reasoning, going from dense to MoE, with all the low hanging fruit gone. Another 18 months later, it seems the answer is no; constant-level intelligence is continuing to get precipitously cheaper.

Yesterday, OpenAI published findings on how GPT 5.6 had optimized its own serving:

1. Inference Acceleration

To serve more tokens on the same hardware without sacrificing quality, OpenAI focused on several systemic upgrades:

Self-Optimization: GPT-5.6 Sol was actively used to analyze production traffic, tune load balancing, and autonomously rewrite production kernels in OpenAI’s own Triton and Gluon languages. This autonomous kernel optimization reduced end-to-end serving costs by 20%.

Speculative Decoding: Improved its own draft model (by designing and running hundreds of experiments on its architecture, testing changes in size, structure, and features, while monitoring the speculator training process, autonomously intervening when issues arose, including hardware failures and training instability) increasing token-generation efficiency by over 15%.

KV Caching: Optimized batching, sharding, and cache management specific to different workloads (primarily Sol in Codex) to extract more inference from existing hardware.

1. Agentic Harness Improvements

To streamline the complex, multi-step tasks in tools like Codex and ChatGPT Work, OpenAI optimized their Rust orchestration layer to reduce repetitive compute costs:

Avoiding Context Bloat: Tools, skills, and plugins are only surfaced when needed (deferred discovery), and tool outputs are capped at 10,000 tokens by default to prevent the context window from expanding unnecessarily.

Prompt Caching: To avoid reprocessing the same instructions and history repeatedly, the harness treats all model-visible history as append-only. This preserves the prompt prefix, allowing the system to reuse previously computed data and maintain a high cache hit rate.

Today, they proved it wasn’t just theory, announcing large price cuts for the smaller models and a new 2.5x Faster mode in Sol (though not the hinted 10x faster Cerebras-driven mode promised in July):

That is a beautiful Pareto curve — if AA’s Cost per Task is reflective of real world tasks, then OpenAI has beat even open models like DeepSeek, GLM, and MiniMax, and dedicated cheap-but-good models like Gemini Flash-Lite.

Good, but what is truly jaw dropping is Nicdunz’s observation:

GPT-5.4 full at xhigh scored 51, exactly where Luna max sits today. GPT-5.4 costs $2.50/$15; Luna now costs $0.20/$1.20. In other words, roughly four months later, OpenAI is selling March’s full flagship intelligence at about one-thirteenth the token price.

That’s an annualized rate of ~2000x a year, a huge acceleration since our last observation - which you SHOULD discount because all public benchmarks like AA’s get trained to some extent whereas Elos are less directly trainable. While leading Chinese and American open models like Poolside’s Laguna and Thinky’s Inkling offer full control and sovereignty, if your only goal is cost-effective non-finetuned intelligence, you will find it -very- hard to beat OpenAI right now.

AI News for 7/29/2026-7/30/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

AI Twitter Recap

OpenAI Pricing Cuts, Harness Semantics, and the ARC-AGI-3 Memory Debate

OpenAI cut GPT-5.6 prices aggressively and added a faster Sol tier: OpenAI reduced GPT-5.6 Luna by 80% and Terra by 20%, while introducing Sol Fast at up to 2.5× lower latency for 2× the standard price with “no change in intelligence,” per @OpenAIDevs. The downstream effect is notable for agent workflows: Auto-review in ChatGPT app and Codex CLI is moving from GPT-5.4 to Luna, with OpenAI expecting roughly 10× lower cost. Multiple observers framed this as a meaningful shift in the price/performance frontier, including @sama, @nicdunz, and @kimmonismus. OpenAI also tied the cuts to systems-level efficiency improvements spanning “model, inference stack, and agentic harness” per @OpenAIDevs.

ARC-AGI-3 re-emphasized that “the model” is not the whole system: The most technical eval discussion centered on harness design, memory retention, and context compaction. François Chollet clarified ARC’s rules: bespoke benchmark-specific harnesses are disallowed, but general-purpose API features available to all users are acceptable if settings and cost are reported. A detailed summary from @kimmonismus contrasted Opus 5 at 30.2% on the official semi-private ARC setup with GPT-5.6 Sol at 7.8% under the standard harness, while noting OpenAI’s internal use of Responses API retained reasoning + compaction raised Sol’s public-set score to 38.3%. The takeaway echoed by @gneubig, @scaling01, and others: long-horizon evals increasingly measure the complete agent system—reasoning retention, truncation policy, compaction, tool orchestration—not just base weights.

Thinking Machines’ Inkling-Small and the Continuing Open-Weights Push

Inkling-Small compresses Inkling-class capability into a much smaller active footprint: Thinking Machines released Inkling-Small, an open-weights, natively multimodal MoE model with 276B total parameters and 12B active, positioned as delivering performance comparable to the original Inkling at roughly a quarter the size. The company says it processes audio and images jointly with text and supports Python-based image inspection during reasoning in multimodal settings, per their follow-up. The release immediately landed across the open inference stack: vLLM announced day-0 support, Modal highlighted single-B300 deployment, LMSYS/SGLang reported decode throughput figures, and Unsloth published a local-running/GGUF guide.

Benchmarks suggest an unusually efficient open model for coding and multimodality: Artificial Analysis placed Inkling-Small at 40 on its Intelligence Index—within a point of the flagship Inkling—with strengths on Humanity’s Last Exam, GPQA Diamond, CritPt, and SciCode, though weaker on some agentic tasks and factual knowledge. Community summaries emphasized that the smaller model can beat or match the larger Inkling on several coding tasks, including @kimmonismus and @mervenoyann. The combination of open weights, multimodal input, 1M-context support in deployment stacks, and 12B active compute makes this one of the more practically important open releases in the batch.

Google’s Gemini Robotics 2 and the Acceleration of Embodied AI

Gemini Robotics 2 expands from tabletop manipulation to full-body control and multi-robot coordination: Google DeepMind launched Gemini Robotics 2, describing it as “one brain for any robot,” with demos spanning whole-body humanoid control, advanced dexterity, and multi-robot collaboration. Google AI added that the stack includes Gemini Robotics ER 2, a high-level embodied reasoning model that can observe, plan, coordinate with a VLA model, track progress, and recover from failed steps during multi-minute tasks. The demos emphasized nontrivial motor tasks such as knot-tying, screwing in a bulb, bending to pick up objects, and collaborative garage cleanup.

The practical story is heterogeneity and adaptation, not just nicer demos: Technical commentary highlighted that the same checkpoint controlled multiple hardware types and that On-Device 2 can reportedly adapt to a new two-arm robot with fewer than 200 examples, summarized by @kimmonismus. @OfficialLoganK and @osanseviero focused on ER 2’s API availability and embodied reasoning metrics, while NVIDIA Robotics used the moment to push the local hardware side with Jetson AGX Thor for humanoid/autonomous systems. Relative to prior robotics announcements, this one stood out because it combined platform breadth, planning, dexterity, and live-streaming APIs rather than a single narrow manipulation benchmark.

Agents, Cloud Development Environments, and Persistent Memory Infrastructure

Cloud agents are graduating from demos to core engineering workflows: One of the stronger operational datapoints came from Cursor: in December, 1 in 10 merged PRs came from cloud agents; now that share is 56%, attributed to giving agents their own cloud computers and allowing them to improve their environments over time. In the same vein, @jaredpalmer said he still hasn’t set up a laptop for local development after joining Cognition, preferring Devin in Slack/webapp, and @dabit3 showed Devin cloud agents running macOS with Xcode and simulator access to build/test native iOS apps. Cognition also added native GitHub stacked PR support, a useful adaptation for agent-generated changesets.

Persistent memory is becoming productized, but evidence on its value is mixed: Perplexity launched Projects, evolving Spaces into hubs for ongoing work with shared files and persistent memory via “Brain,” while @AravSrinivas positioned it as a multiplayer, agentic operating system for work. For memory infra lower in the stack, TurboPuffer described Mem0 migrating 400M+ agent memories from pgvector to turbopuffer, citing 70ms p90 hybrid retrieval and 97% recall@10. But the research signal was more cautious: @dair_ai highlighted a paper suggesting filesystem-style memory stores can halve retrieval cost at scale yet did not improve final answer quality in the study, and store quality degraded under most management agents except the strongest one. Net: memory infra is maturing as a product surface, but its causal contribution to capability remains unsettled.

Infra, Retrieval, and Tooling: Kernels, Search Transparency, and New Eval Infrastructure

Systems optimization remains a major source of gains: SemiAnalysis highlighted GPU Mode’s AMD kernel hackathon, saying the Readonflow team improved MI355X end-to-end performance by over 2×. At the individual-kernel level, @maharshii reported a custom attention kernel jumping from 1.5× to 2.17× over torch SDPA by replacing div.rn.f32 with rcp.approx, a reminder that PTX-level inspection still matters. Astral also open-sourced build pipelines for prebuilt wheels of GPU-heavy packages like FlashAttention and DeepSpeed, targeting reproducibility and easier Python packaging.

Retrieval and search infra became a transparency issue, not just a performance issue: Simon Willison criticized both OpenAI and Anthropic for depending heavily on search while obscuring the underlying search index and partnerships; he pointed to Anthropic’s subprocessor listings revealing ties to Brave and later TurboPuffer in a way not clearly surfaced in product docs. On the retrieval-model side, @antoine_chaffin introduced mDenseOn and mLateOn, fully open multilingual retrieval models for long-context and code retrieval, with follow-up metrics suggesting especially strong generalization for late interaction models.

Top tweets (by engagement)

OpenAI pricing reset: @OpenAI announced 80% Luna and 20% Terra price cuts plus Sol Fast, the clearest product/inference signal of the day.

Gemini Robotics 2 launch: @GoogleDeepMind and @GoogleAI unveiled a more general embodied stack spanning whole-body control, dexterity, and collaboration.

Inkling-Small release: @thinkymachines shipped a materially important open multimodal MoE with 12B active parameters.

Cloud agents in production software engineering: @cursor_ai shared the strongest concrete adoption stat in the set: 56% of merged PRs now coming from cloud agents.

Independent review of the Hugging Face / OpenAI incident: @METR_Evals said it reached agreement with OpenAI and Redwood Research on an independent review of the model behavior observed during the Hugging Face incident, with scope and tentative conclusions to be published.

AI Reddit Recap

/r/LocalLlama + /r/localLLM Recap

1. Kimi K3 and Inkling-Small Local MoE Runs

Read more