Inkling 2 Small is a 594B-total, 17B-active Mixture-of-Experts model with image inputs, scoring 31 on the Artificial Analysis Intelligence Index and available now on Tinker with closed weights.
Full Apache 2.0 weights are promised next week after external safety testing and red-teaming, a closed-first staged release that stands out against the Qwen3.8 exfiltration trust debate.
It reports 72.1% on DeepSWE v1.1 and tops the listed field on MMMU Pro (81.6%) and Vals Finance (61.5%), joining the open-weights race alongside Muse Spark Open and Kimi K3.
Revolut’s ops lead said at AI Engineer NYC that self-hosted GLM models became the default for coding, cutting inference costs by about 90%, with GLM-5.3 running on 56 Nvidia B200 GPUs for roughly 2,000 engineers.
Vision tasks at Revolut are routed to Gemini or GPT models, and the switch came at the expense of Claude.
Separately, a r/LocalLLaMA post relays a Qwen statement that Qwen 4.0 and 5.0 will remain open source after rumours Qwen4 would be the last open-weight series, a pledge that tests the open-weights trust-versus-adoption tension from the Qwen3.8 exfiltration story.
GPT-6 Cyber scores 48.5% on ExploitGym versus 32.1% for GPT-5.6 Cyber, stayed in scope in 99.93% of alignment tests without a deployment prompt, and is classed Critical under OpenAI’s Preparedness Framework.
Access is limited at first to a select group of approved Daybreak Red customers via API and Codex, while GPT-5.6 Cyber reached approved government customers through Daybreak Red in AWS GovCloud and Hacktron’s frontier-model access expanded through the Hacktron partnership.
A separate prototype pipeline analysed 2,308 candidate Linux kernel findings, reproduced 1,724 and produced 948 local privilege escalation exploits, with 304 critical findings disclosed with candidate patches at roughly $1.2M in API cost.
Personal Finance is in beta this week for Claude Pro, Max and Ultra users in the US, who connect bank and card accounts through Plaid so Claude never sees the bank password.
Access is read-only, so Claude cannot move, manage or invest money, and Anthropic warns it can make mistakes and says users should verify important figures with their bank.
Personal Finance hub chats are not used to train models, and Anthropic is inviting banks, wealth managers and financial services firms to help shape work with licensed professionals, a consumer step beyond the finance push in the Oct 25 edition.
Using GPT-6 Astra with no gradient descent, the experiments fixed a failing PPO car-control network by editing 3.5% of weights (0% to 100% success), wrote MinAtar networks that beat PPO trained for 100M steps across 3 games, and wrote a 1.5M-parameter Go network that beats an equal-sized PPO trained on 20M games.
Editing 0.016% of Qwen3.5-0.8B’s weights fixed a reasoning-loop bug and lifted MATH-500 from 7.9% to 48.1%, GSM8K from 13.8% to 67.1% and IFEval from 21% to 62.3%, though weight edits can remove an obstacle but not add a capability the model lacked (Gemma 270M math barely moved).
Implications include auditing models for backdoors without running inference, a fresh angle on the open-weights trust debate, and a companion result in which Simon Willison found a local Qwen 3.8 27B identified Pac-Man, Pong and Breakout from game weights but failed Freeway, at 46 to 65 minutes per run.
On 100 traces from an apartment-leasing agent, the post compares decision models including TypeSafe Jev 1.13, Perplexity Decider 1.1 27B, Liquid d1 and d1-ultra, Cloudflare Clef Flash and the OpenAI Decisions API (GPT-6 Luna).
OpenAI Decisions had the best separation at F1 0.87 for 3.72 cents per 100 eval runs, while Perplexity was about 4.5x cheaper at 0.82 cents with F1 0.79, and tuning the default 0.50 threshold helped Liquid d1 and Clef Flash most.
It adds independent evaluation to the OpenAI Decisions API GA and advises one narrow judge per failure mode, tested on your own data.