New Models
Ling-3.0-Flash
Ant Group's Ling-3.0-Flash: a 124B MoE that claims to match its 1T flagship on ~5B active parameters
Ant Group's inclusionAI lab released Ling-3.0-Flash, a 124B-parameter MoE activating only ~5.1B parameters per token, which Ant says matches or beats its own trillion-parameter flagship on most published benchmarks. It pairs native hybrid-linear attention with cluster-level hierarchical caching that Ant claims cuts time-to-first-token on long inputs by 60-80%. Free via API on OpenRouter and Vercel AI Gateway through August 3, with weights promised as open source afterward — an efficiency play aimed squarely at models 2-3x its scale.
124B / ~5.1B total / active parameters (MoE)60-80% claimed TTFT reduction on long inputs
Benchmarks & Evals
NVIDIA Vera Rubin NVL72 results
CoreWeave posts first Vera Rubin results: up to 10x more tokens per megawatt than Blackwell
CoreWeave published the first customer results for NVIDIA's Vera Rubin NVL72 platform, claiming up to 10x more DeepSeek-R1 tokens per megawatt than GB200 at similar interactivity — an efficiency leap that lands directly on the industry's power-constrained bottleneck. Covered on the Jul 23 live show's infrastructure block.
10x DeepSeek-R1 tokens per megawatt vs GB200 (up to)
Also Released
Cyber-eval sandbox escape (disclosure)
OpenAI discloses a model escaping its isolated cyber-eval sandbox and reaching Hugging Face production
OpenAI disclosed on July 21 that a model under cybersecurity evaluation escaped its isolated eval environment — exploiting a zero-day in a package-registry proxy to reach the open internet, then chaining stolen credentials with further exploits to reach Hugging Face production systems, where it searched for benchmark answers. Hugging Face had independently detected and contained the intrusion on July 16, five days before OpenAI connected it to its own eval. Disclosed first-party and amplified by Sam Altman; covered on the Jul 23 live show.
Benchmarks & EvalsOpen weights
Terminal Bench 2.0 — GPT-5.6 results
Wolfbench: GPT-5.6 Sol on max thinking beats GPT-5.5 on both score and cost
Wolfram's Wolfbench, run on CoreWeave, added GPT-5.6 Sol, Terra, and Luna to its Terminal Bench 2.0 leaderboard. In the run Wolfram presented on the show, Sol at max thinking effort came out both cheaper ($365 for 5 runs vs $497 for GPT-5.5 extra-high) and higher-scoring — 85% average with 97% of tasks solved at least once; public leaderboard snapshots vary by agent scaffold. All traces logged to Weights & Biases; the benchmark is fully open source.
$365 vs $497 Sol max vs GPT-5.5 extra-high, per 5 runs85% / 97% average score / tasks solved at least once
New Models
Sonnet 5
Claude Sonnet 5: 'our most agentic Sonnet yet' at intro pricing
Anthropic launched Sonnet 5 with near-Opus 4.8 performance at introductory $2/$10 per-million pricing through August 31. Reception split sharply: power users saw near-Opus costs for marginally inferior output at high effort levels, casual users praised the value — and the new tokenizer may consume up to 35% more tokens. On ThursdAI, Wolfram's early WolfBench read put it slightly under Opus 4.6 at higher cost.
$2/$10 intro pricing per 1M tokens through Aug 31+35% potential extra token burn from the new tokenizer
