The Grid

Minimax M2.7 released! And its a big one Highlights: Self-evolving - first model that helped build itself, running 100+ autonomous optimization loops during its own RL training (30% internal improvement). Strong coder - 56.2% on SWE-Pro (near Opus 4.6), 55.6% on VIBE-Pro, production debugging down to under 3 minutes. ML research agent - 66.6% medal rate on MLE Bench Lite, tying Gemini 3.1. Office work - top open-source ELO on GDPval-AA (1495), 97% skill adherence, can do end-to-end analyst workflows (reports, models, PPTs). Native multi-agent and a new open-source interactive character demo called OpenRoom.

1/ OpenAI just launched GPT-5.4 Pro, their premium model at 12x the API cost of standard GPT-5.4. $30/M input tokens, $180/M output vs. $2.50/$15. I ran TaxCalcBench on Pro. The result: exactly tied with standard GPT-5.4 12x the price, 0% improvement But the full story is more nuanced:

Founder of Chicago-based prop trading firm DRW says compute will be world's top commodity in 10 years. People will spend more on GPUs than an oil, which means that there should be a futures financial market for GPUs.  Interesting implications for startups and cloud providers:

He's directionally right but inference is the commodity. GPUs are an input. We don’t build commodity markets around the machine that produces the good.

Founder of Chicago-based prop trading firm DRW says compute will be world's top commodity in 10 years. People will spend more on GPUs than an oil, which means that there should be a futures financial market for GPUs.  Interesting implications for startups and cloud providers:

This alone is a $1B+ idea. Price discovery for compute is a huge issue. If you want to price compute, you have to email/call every neocloud. Everyone gives a different price. Some deal direct, others push you to brokers. + all the creative financing deals complicates price discovery. Compute markets solve this.

Google just priced intelligence at $0.25 per million input tokens. Let that math sink in. Gemini 3.1 Flash-Lite costs 4x less than Claude 4.5 Haiku on input ($0.25 vs $1.00) and 3.3x less on output ($1.50 vs $5.00). It runs 2.5x faster time-to-first-token than Google’s own 2.5 Flash. And it scores 86.9% on GPQA Diamond, which beats larger Gemini models from previous generations. This tells you everything about where the AI model war is actually being fought right now. Everyone’s watching the frontier models compete on reasoning benchmarks. The real war is in the efficiency tier, where the actual infrastructure bills get paid. Here’s why. Enterprise AI is at roughly 10% adoption heading toward 50%. The workloads that drive that adoption curve aren’t complex reasoning tasks. They’re translation, content moderation, intent routing, catalog processing. Millions of calls per day where the difference between $0.25 and $1.00 per million tokens compounds into hundreds of thousands of dollars per month. Google is doing something specific here. They’re using 3.1 Flash-Lite as a wedge to lock developers into the Vertex AI ecosystem on high-volume workloads, then upselling them to 3.1 Pro for complex reasoning at $2.00 per million input. The cascading architecture play: cheap model handles 90% of requests, expensive model handles 10%. Total cost drops by 80%+ versus running everything through a frontier model. OpenAI sees the same dynamic. GPT-5 Nano is priced at $0.05/$0.40 per million tokens. That’s 5x cheaper than Flash-Lite on input. The efficiency tier is becoming a loss leader for ecosystem capture. The company that wins the next 2 years of enterprise AI is the one whose cheap model is good enough to run every log file, every customer chat, every moderation call without exhausting the cloud budget. Google just made their bid.