Z.ai released GLM-4.6 on September 30 with open weights under the MIT license on Hugging Face: the GLM-4.5 mixture-of-experts architecture — 355B total parameters with 32B active per token — with the context window expanded from 128K to 200K tokens and maximum output raised to 128K. Z.ai reports gains over GLM-4.5 across eight public benchmarks covering agents, reasoning and coding, and says GLM-4.6 beats Claude Sonnet 4 in its own 74-task real-world coding suite run inside the Claude Code environment, while trailing Claude Sonnet 4.5 on coding.

Two things separate this from a routine model drop. First, published pricing of $0.60 per million input tokens and $2.20 per million output, against Sonnet 4.5's $3/$15 — roughly a fifth on input and a seventh on output — with self-hosting free under MIT if you have the GPUs. Second, Z.ai published all 74 CC-Bench agent trajectories, prompts and tool calls included, as a dataset, which lets you inspect failure modes instead of trusting a bar chart.

DeepSeek published DeepSeek-V3.2-Exp on September 29: a 671B-parameter mixture-of-experts model with 37B active parameters per token, a 128K context window and MIT-licensed weights, posted to Hugging Face alongside a technical report and TileLang and CUDA kernels. It is V3.1-Terminus plus DeepSeek Sparse Attention, which uses a learned lightning indexer to score the context and compute attention over roughly the top 2,048 tokens per query instead of the whole sequence. DeepSeek reports benchmark parity with the dense-attention predecessor — 85.0 MMLU-Pro on both, Codeforces 2121 versus 2046 — while processing long inputs two to three times faster.

The price change shipped with the model rather than after it. DeepSeek's release post says API prices dropped more than 50% effective immediately, to about $0.28 per million input tokens, $0.028 per million cached input tokens and $0.42 per million output tokens, with the old V3.1-Terminus endpoint kept alive for comparison only until 15 October. The sparse-attention kernels are released as readable TileLang code plus production CUDA, so the efficiency trick is reproducible on your own serving stack rather than locked behind the vendor API.

Anthropic released Claude Sonnet 4.5 on September 29 — the same day DeepSeek's sparse-attention checkpoint dropped — claiming state of the art on SWE-bench Verified at 77.2% and 61.4% on OSWorld computer use, up from 42.2% for Sonnet 4 four months earlier. Pricing was held flat at $3 per million input and $15 per million output tokens with a 200K context window, and the model shipped under ASL-3 protections with CBRN classifiers that Anthropic says have cut false positives tenfold since their introduction. Anthropic reports sustained focus of more than 30 hours on multi-step tasks.

The more consequential half was the harness. Anthropic gave developers the Claude Agent SDK — the same scaffolding behind Claude Code — plus checkpoints with instant rollback in Claude Code, a native VS Code extension, and context editing and a memory tool in the API. Those are the unglamorous primitives of a production agent stack: durable memory, cheap context compaction, and a way to rewind a run without re-paying for it.

Governor Gavin Newsom signed Senate Bill 53, the Transparency in Frontier Artificial Intelligence Act, on September 29, a year after vetoing its predecessor SB 1047 over fears it would chill California's AI economy. The law binds large frontier developers — anyone training a foundation model above 10^26 floating-point operations — to publish a frontier AI framework covering catastrophic-risk assessment, mitigation, internal governance and weight-security measures, and to report critical safety incidents to the Office of Emergency Services within 15 days, or 24 hours where there is an imminent threat. It adds whistleblower protections, empowers the attorney general to levy civil penalties of up to $1,000,000 per violation, establishes the CalCompute public-computing consortium, and takes effect January 1, 2026.

It is a transparency statute, not a liability one: nothing in SB 53 creates new causes of action for harms caused by model outputs. What it does create is a written, dated, publicly posted artefact that companies can be measured against, and an incident-reporting channel into state government, at a scale threshold high enough that it currently catches a handful of labs rather than the deployer ecosystem.