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Google Releases Gemini 4 Argon, Topping Multiple Leaderboards and Focusing on Cybersecurity Defense

Google has released its new-generation flagship model Gemini 4 Argon, which scores 77.9% on DeepSWE v1.1, surpassing Claude Opus 5.5's 74.2% and GPT-6 Astra's 74.1%, and tops the Vals Index with 68.90%. The model supports output of up to one million tokens, and during the promotional period costs $2 per million input tokens and $10 per million output tokens, with a per-question cost of about 40% of Astra's. It is currently available only to vetted cybersecurity defense teams, with the first batch entering Google's Fairwind program.

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Anthropic releases Claude Sonnet 5.5, 30% faster than Sonnet 5 with unchanged per-token pricing

Anthropic has released Claude Sonnet 5.5, the second model in the Claude 5.5 family. It is smarter and more efficient than Sonnet 5 and 30% faster, with unchanged per-token pricing; because it needs fewer tokens to complete the same work, costs for most tasks drop by up to 30%. The official recommendation is to use Sonnet 5.5 for well-scoped everyday tasks such as bug fixing and rapid feature iteration, while complex long-running work should still use Opus 5.5; Haiku 5.5 will join the family in the coming weeks. The model ID is claude-sonnet-5-5, and it is already available on platforms including the Claude API, Amazon Bedrock, Google Cloud, and Microsoft Foundry. Starting with v2.1.284, the sonnet alias in Claude Code points to this model, which runs at medium effort by default and natively supports a 1M context window.

GPT-6 Family's First Model Astra Released, Author's Hands-On Test Consumed 4 Billion Tokens

OpenAI released Astra, which the author calls the beginning of the GPT-6 family; it performs outstandingly on ARC-AGI-3 and scored highest on Zapier's AutomationBench, with pricing the same as Fable 5.1. During weekend trials, the author consumed 4 billion tokens and found its capability fluctuates noticeably—it is a showcase model rather than one that reliably does work. Astra in Codex can now continue advancing tasks without waiting for an answer, and OpenAI also claims it has achieved its automated research intern goal, with the next goal being an automated AI researcher by March 2028.

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Ant Ling releases finance-enhanced model Ling-3.0-flash-Fin

Ant Ling releases its first finance-enhanced model, Ling-3.0-flash-Fin, which continues the Ling-3.0-flash architecture, maintaining 124B total parameters and 5.1B activated parameters. Through continued pre-training on financial corpora, domain post-training, and tool-use optimization, it strengthens the handling of complex financial content such as annual reports and financial workbooks. The model was tested on benchmarks including FinFIRST, FinSearchComp Verified, Finance Agent, APEX-Agents, SpreadsheetBench, and τ³-Banking, with its AA Intelligence Index v4.1.1 score rising from 38 to 41. The model will offer a one-month limited-time free API on OpenRouter, and its weights will be open-sourced next week.

Ant Ling Open-Sources Ling-3.0-tiny-base and Ling-3.0-flash-base

Ant Ling has officially open-sourced two Base Models, Ling-3.0-tiny-base and Ling-3.0-flash-base, and simultaneously released the weights from three stages—pre-training, mid-training, and WSM merging—for a total of 6 checkpoints. Ling-3.0-tiny-base has 7.9B total parameters and 1.3B activated parameters, while Ling-3.0-flash-base has 124B total parameters and 5.1B activated parameters; both can be used for continued pre-training, supervised fine-tuning, preference optimization, reinforcement learning post-training, and distillation. The official notes state that the Base Models have not completed instruction alignment and are not recommended for direct deployment as end-user-facing chat services without post-training.

Ethan Mollick Tries GPT-5.5 Early: Capability Gains Are Still Accelerating

Ethan Mollick was granted early access to try GPT-5.5 and believes its capabilities have improved markedly; GPT-5.5 Pro completed the same 3D port town simulation task in 20 minutes, compared with 33 minutes for the previous generation, GPT-5.4 Pro. Using Codex together with GPT-5.5 to process a decade's backlog of his own crowdfunding data, he generated a paper including a genuine literature review and statistical methods with just four prompts, and he considers its quality equivalent to a second-year PhD project. He also noted that OpenAI's new image model (sometimes called GPT-imagegen-2) can render high-quality text and images, but that AI still has rough edges in long-form fiction writing, such as florid prose and homogeneous dialogue.

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