Microsoft shipped a really compelling product on top of @OpenClaw today. We worked with them since March to make the codebase ready for large-scale deployments, they are a great partner and open-source contributor. 🙏🦞

Highest throughput GLM 5.3 🫡 docs.primeintellect.ai/infer…

"The AI agent independently identified vulnerabilities and attempted to progress actions without direct human authorisation to ensure it was able to complete the activity it was assigned." This was crawling unlisted files btw. Unlisted files on a public network

This is the literal Sunday Morning Herald headline!

ClusterMAX 3.0: The Industry Standard GPU Cloud Rating System Returns In gory detail: reliability, performance, support, pricing —and, of course, security— in our most thorough analysis of GPU cloud providers globally semianalysis.substack.com/p/…

Australia has been hacked. 'And today, I spoke with the CEO of OpenAI, Sam Altman, to express Australia's extreme concern about this incident. And I also expressed my disappointment that it took the company way too long to inform the government what had occurred, and the nature of the way that that notification occurred as well was unacceptable.'

Introducing Prime Sandboxes: MicroVM sandboxes purpose-built for RL training. Model training requires running tens of thousands of concurrent sandboxes, leading to complex and costly configuration. We built Prime Sandboxes for our own team. Today we're releasing them publicly.

Jason Gwartz reports: > On GPQA Diamond, Jev scored 77%, comparable to GPT-5.5 with reasoning off, or Fable 5 with low reasoning - but did the whole 198 questions in 3 seconds at a cost of $0.007

I'm VERY disappointed in GPT-6 Sol. I'm VERY pleased with GPT-6 Luna. But Opus 5.5 genuinely makes no sense to me... Opus 5 was such a bad experience that I cancelled Anthropic entirely. Now I've been using Opus 5.5 on High for 5 straight hours today... and I've only used 8% of my WEEKLY limit. Not to mention the model itself feels WAY better. I genuinely don't understand how Anthropic turned Opus around this hard in one release. Sol disappointed me. Luna surprised me. Opus 5.5 won me back.

We just pushed a big update to Ramp AI Index. Available today: token and spend *volumes* not just shares or token counts. Updated weekly. Model-by-model, including open-source models. And more to come, including team-based spend.

GPT-6 Sol and Luna push the cost efficiency frontier by halving cost relative to GPT-5.6 Sol and Luna. Intelligence Index and Coding Agent Index scores remain level with GPT-5.6, with progress in some evaluations and regressions in others Pricing is approximately half that of GPT-5.6: Sol drops from $4/$20 to $2/$10 per million input/output tokens, and Luna from $0.20/$1.20 to $0.10/$0.50, with the same 90% discount for cache reads and 25% premium for cache writes. Key takeaways: ➤ Halves Cost per Task: GPT-6 Sol (max) costs $1.06 per task to run the Artificial Analysis Intelligence Index, ~50% less than GPT-5.6 Sol (max) at $1.99. GPT-6 Luna (max) costs $0.07 per task, ~60% less than GPT-5.6 Luna (max) at $0.18. This is driven by the price cut, as both models use slightly more output tokens per task (31k vs 29k for Sol, and 51k vs 41k for Luna). These two releases allow OpenAI to capture a significant portion of the cost efficiency Pareto frontier. ➤ In the Coding Agent Index, Sol improves but Luna regresses: In OpenAI's Codex harness, GPT-6 Sol (max) scores 57 in the Artificial Analysis Coding Agent Index, up 2 points from GPT-5.6 Sol (max), with gains in Terminal-Bench 4.0 (43% vs 37%) and SWE-Atlas-QnA (58% vs 54%). At $2.99 per task it costs ~50% less than GPT-5.6 Sol (max) and sits on the Pareto frontier of Coding Agent Index vs Cost per Task. GPT-6 Luna (max) scores 41, down 2 points from GPT-5.6 Luna (max), with lower scores in SWE-Atlas-QnA (44% vs 49%) and DeepSWE v1.1 (64% vs 66%), at ~60% lower cost per task. ➤ Significant reduction in hallucination: Both models hallucinate less in AA-Omniscience, our knowledge and hallucination benchmark. GPT-6 Sol (max) cuts its hallucination rate from 92% to 60% and GPT-6 Luna (max) from 93% to 77%. Sol achieves this by declining to answer more often: it attempts 83% of questions vs 99% for GPT-5.6 Sol (max), which cuts wrong answers by about a quarter but also lowers accuracy 5 points from 59% to 54%. Luna's accuracy is broadly unchanged at 44% vs 43% while it answers fewer questions. On the AA-Omniscience Index, Sol improves from 22 to 27 and Luna from -10 to 1. ➤ Mix of improvement and regression across evals: Beyond AA-Omniscience, both models improve in AutomationBench-AA (Sol 62% vs 60%, Luna 53% vs 50%) and Terminal-Bench 4.0 (Sol 44% vs 40%, Luna 13% vs 12%). However, we observe regressions in two key knowledge work evaluations. In GDPval-AA v2.1, our benchmark adapted from OpenAI's dataset of economically valuable tasks across 44 occupations, Sol drops ~100 Elo points and Luna ~75. Luna also drops ~45 Elo points in AA-Briefcase v1.1, while Sol is level. AA-Briefcase v1.1 is a private evaluation across multi-week knowledge work projects, with thousands of input files. Our team has manually inspected hundreds of model outputs: the regressions tend to be driven by reduced presentation quality and deliverables that omit rubric elements. Congratulations @OpenAI and @sama on the launch!

Claude Opus 5.5 will be the first Opus meant to fall back to a less capable model for "a small set of capabilities related to the development of frontier LLMs, such as kernel development.."

The biggest disagreement JSD and I had in this podcast was on the impact of distillation. In our research for Interconnects, @xeophon and I agree that there's no hard evidence that distillation is a massive impact for the Chinese labs. At the same time, the gossip mill in SF has been doubling down on the Chinese labs getting massive gains from distilltion. The argument where distillation is a huge impact is something along the lines of distilled traces go into mid training and make RL work far more easily. My argument, that distillation is a 1-2 month pull ahead in capabilities closer to the frontier, is that Chinese labs already have sufficiently strong models, where this mid-training setup can be done on their own models, and most of the capabilities gains are from scaling RL environment training, which doesn't link cleanly to the distillation data pathway. We feel like the recent RL dashboard from @XiaomiMiMo supports this claim. A lot of it comes down to a gut call on if you can believe it that the Chinese labs are really great at building LLMs, maybe even better than focusing than OpenAI/Ant etc, as their current ambitions are a bit narrower (catching up), rather than transformative products/inventions

MiMo V2.6 is shipping mimo.xiaomi.com/mimo-v2-6