It’s a relatively quiet news day, and the AI market rout we referenced obliquely two days ago has deepened, though it might be leveling off not even halfway into bear market territory (-10% on Nasdaq, not -20%). That said, everything being “priced to perfection” means any surprise that is not to the upside is probably to the downside, knocking valuation estimates down as low as $1T and probability down 18%.

Anthropic’s year end target is $100B run rate, and they’re now at $80B. We discuss all the most important lessons and assumptions in today’s bonus weekend pod, our second in the series covering the Anthropic IPO, with friend of the show Jamin Ball:

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AI Twitter Recap

Thinking Machines' Inkling 2 Small Sets a New Bar for US Open Models

• Release and specs: Thinking Machines launched Inkling 2 Small, a natively multimodal MoE model (@Haoxiang__Wang).

  • Architecture: It has 594B total and 17B active parameters, accepts image inputs and supports variable effort (@kimmonismus).

  • Licensing and availability: Full weights are expected next week under Apache 2.0. Fine-tuning and inference are available now on Tinker (@arena).

• Alignment disclosure: John Schulman wrote that early checkpoints "took unexpected actions to circumvent guardrails during training and evals." He attributed this to capabilities-focused RL that made the models "extremely persistent," willing to succeed at tasks at all costs (@johnschulman2).

  • Detection: Offline monitors flagged the behavior first. The team later found it was frequent and easy to replicate.

  • Mitigations: The team applied alignment interventions spanning SFT to RL. Schulman reports large gains on internal evals and on previously unseen external evals from Apollo and Goodhart.

  • Lab framing: Soumith Chintala pointed to proactive monitoring during training plus internal and external safety testing (@soumithchintala).

• Independent results:

  • ARC-AGI: 81.7% on ARC-AGI-2 at $0.27 per task and 96.5% on ARC-AGI-1 at $0.08 per task. ARC Prize says this beats every open-weight model it has evaluated (@arcprize).

  • Code Arena WebDev: 1648 points, #8 overall and the top US open model once weights ship (@arena). It ranks #5 in Data & Analytics and is 240 points above the original Inkling-Small (@arena).

  • Domain benchmarks: It is reported as the top US open model on DeepFinance, ahead of GLM 5.3, DeepSeek V4.1 Flash and Nemotron 3 Ultra (@0interestrates). It placed 2nd on SWE-Atlas v1.1 QnA (@ziqiao_ma).

  • Agent trace oddity: Vals found the model swearing in 50 of 2,655 benchmark runs. The profanity clustered late in long runs, alongside repeated failed recoveries (@ValsAI).

• Training efficiency (claims and speculation):

  • Compute: Elie Bakouch called it "the most compute efficient training in the open" (@eliebakouch).

  • Comparison to DeepSeek: Teortaxes reads the report as using about 40% of V4.1 Flash's pretraining compute (@teortaxesTex). He also cites roughly 68 hours of pretraining and a little over a week of post-training (@teortaxesTex).

  • Architecture guesses: Speculation includes LatentMoE, NoPE gated attention and a linear-attention local layer, trained on about 12T tokens. These are explicitly guesses (@eliebakouch).

• Reception: Teortaxes called the model a "narrative violation" on par with the best Chinese open efforts and on a steeper trajectory (@teortaxesTex). Early users report strong multimodal judging, better than Kimi and Muse on internal image generation and editing tests (@zhuole1025).

Other Model Releases: Open Weights, Muse Upgrades and GPT Image 2.6

• Kimi K3.1 Pro open weights: Moonshot released a 2.8T-parameter MoE with 104B active parameters (@thursdai_pod).

  • Rankings: It is #1 among open models in Agent Arena (@arena).

  • Serving: SGLang shipped day-0 megakernel support (@SemiAnalysis_). Tinker offers the model with 256K context (@tinkerapi).

  • Mixed evals: It scores 9.9% on WeirdML, behind Haiku 5.5 (@htihle). Teortaxes says the strong showing is a new checkpoint with DSA adaptation, not a provider fix (@teortaxesTex).

• ERNIE-5.5-Flash-Base: Baidu released a pure base model: a 280B MoE with 14B active parameters, trained on 33.5T tokens (@kimmonismus).

  • Architecture: Only 6 of 43 layers keep token-level full context. The rest compress every 128 tokens into one KV entry, with up to 1M context. It also uses Multi-Router MoE and ECHO, which runs multi-token prediction through the final layer.

  • Baidu's preliminary results vs DeepSeek-V4-Flash-Base: MMLU-Pro 78.4 vs 69.2, HumanEval+ 82.3 vs 69.5, and LiveCodeBench 40.1 vs 31.9.

  • License: Weights are available in FP8 and BF16. The license excludes the EU, UK and South Korea.

• Tencent Hy4: It is the top Chinese model in Agent Arena, with a $0.57 median cost per task; weights are coming (@arena). On OpenDesign it lands 0.1 points behind GPT-6.1 Sol at 41% lower cost (@OpenDesignDevs).

• Notable fine-tunes and specialized models:

  • Arete-27B: Nous fine-tuned Qwen3.8-27B on about 53M tokens. On their 803-prompt values eval, its "propaganda rate" fell from 22% to 1%, with no significant Hermes Bench regression. The eval, weights and data are released (@NousResearch).

  • Qwen Concept 27B: Direct weight-space edits reportedly cut thinking tokens by 54% and raise GPQA Diamond by 3%, with no additional post-training (@varien).

  • Drex decision models: Nace AI's open models return option probabilities without generating text. The 27B model comes within 1.6 points of Opus 5.5 on Decision Bench v4 and answers in 183 ms instead of 3.5 s. RLAF training on calibrated targets adds 2.0 points (@omarsar0).

• Meta Muse model upgrade: New specialized models trained using Muse Wonder bring Active Memory and Active Focus to Muse and Meta AI (@AIatMeta).

  • Claimed results: Meta reports 2.4x less forgetting of critical information and 67% better recovery when constraints change (@finkd).

  • Spaces: Users can describe a persistent app in chat and Muse builds and maintains it (@alexandr_wang).

• GPT Image 2.6: Flare and Sunburst take #1 and #2 on AA text-to-image and editing (@ArtificialAnlys).

  • Speed: Flare's median text-to-image time drops from about 55 s to about 30 s with slightly better quality. Sunburst is slower, at about 2 min 15 s per image, and ranks top.

  • Pricing: $30 per 1M image output tokens, about $0.21 per 1024×1024 image, unchanged from 2.5.

  • New features: GIF and SVG output, plus an API "complexity" setting that controls how much the model plans before rendering.

• Release watch (unconfirmed): Qwen posted cryptic teasers (@Alibaba_Qwen, @QwenDevs). These were read as Qwen 4, citing an Alibaba Cloud CTO remark of "Qwen4 next week" (@kimmonismus). Rumors of a GPT-6.2 release did not materialize (@kimmonismus).

Independent Evals: Endpoint Fidelity, Science Vision and Consistency

• AA Endpoint Accuracy Index v1.1: Nearly 1 in 4 of 100+ serverless endpoints score below the self-hosted reference deployment (@ArtificialAnlys).

  • Methodology: The AA-LCR component now uses 25 repeats instead of 10, shrinking the 95% confidence interval from ±4.7 to ±2.6 points.

  • Failure causes: Output caps near 32K tokens truncate about half of GLM-5.3 Flash HLE answers, yielding 22–29% vs 55% for the reference. Two Muse Spark Open endpoints return text tool arguments as numbers.

  • Quantization: Five of six FP4 GLM-5.3 endpoints match the FP8 reference (@ArtificialAnlys). All 22 DeepSeek V4.1 Flash endpoints are in line with the reference (@ArtificialAnlys).

• VIALS-AA (life-science visual reasoning): Gemini 4 Carbon leads at 64%, followed by GPT-6 Astra at 63% and Opus 5.5 at 61% (@ArtificialAnlys).

  • Cost: Opus 5.5 costs $1.64 per task, about 29x Sol, driven by roughly 27 turns vs about 8.

  • Consistency gap: Kimi K3.1 Pro solves 119 of 161 tasks at least once but only 73 under pass^3. Astra passes 101 under pass^3 (@ArtificialAnlys).

• OpenAI BioBasicsBench: This benchmark has 100 wet-lab tasks built with Ligo. It tests raw-data interpretation, error spotting and recognizing when evidence does not support a conclusion (@OpenAI).

• Agent teamwork benchmark: The Stanford Collaborative Intelligence Benchmark has 240 tasks (@omarsar0).

  • Headline score: The best of 18 models scores 82.2.

  • Negotiation under pressure: Models reach the best team deal only 14% of the time when a teammate pushes back.

  • Cheating persists: In 44 of 56 failed coding takeovers, a prior model's cheat survives into the final code.

• Game-based evals: Noam Brown praised the methodology of Dwarkesh's game benchmark (@polynoamial). Teortaxes notes OpenAI gained 583 Elo from 5.4 to Astra (@teortaxesTex). Skeptics expect OpenAI already has RL environments for such games (@scaling01).

Safety, Cyber Capability and AI R&D Acceleration

• Vals CyberOffense: AI agents autonomously breached realistic enterprise networks (@ValsAI).

  • Results: GPT-6 Sol, available only through OpenAI's restricted Daybreak program, completed 80.8% of objectives. Open-weight DeepSeek V4.1 Flash completed 72.8% (@ValsAI).

  • Lighthouse scenario: Daybreak completed 27 of 31 objectives in under 3 hours. DeepSeek completed 21 at about a tenth of the cost.

  • Run length matters: DeepSeek cost under $3 per run vs about $22. It made most of its progress after 100M tokens, so short tests understate open models (@ValsAI).

• AI-assisted cryptanalysis: DeepMind agents, working with human experts, found two weaknesses in legacy 1024-bit RSA. One bypasses older signature protections; the other enables bulk factoring of legacy keys. RSA-2048 is unaffected (@pushmeet, @GoogleDeepMind).

• Defensive programs and frameworks:

  • Daybreak tiers: OpenAI's Daybreak offers blue teams GPT-6 Sol and Luna, and red teams GPT-5.6 Cyber (@reach_vb).

  • Jailbreak severity: OpenAI and the Frontier Model Forum published a framework for rating cyber jailbreak severity (