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Everything tagged model-release, across every stream.

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AIAI #ai-safety#cybersecurity#gemini#google#model-release

Google announces Gemini 4 Carbon, but gives it first only to selected cyber defenders

Google's post has the benchmark claims and the staged rollout plan; the Reuters report via Khaleej Times sums up the competitive picture.

Google announced Gemini 4 Carbon on Oct. 28, two weeks after Gemini 4 Argon, calling it its strongest model, with state-of-the-art cybersecurity and long-horizon capabilities. Google says Carbon ranks first on the Artificial Analysis Intelligence Index v5.0 with a score of 59, and leads on SEC-bench Pro (88.2%) for finding vulnerabilities, ExploitGym (54.8%) for exploitation and CWE-bench (71.0%) for patching. It says internal teams have seen Carbon run for days on complex engineering tasks.

Carbon is not generally available. Google says it is taking part in the U.S. government's voluntary pre-release testing process and, starting this week, is giving access to a subset of cyber defenders, then more trusted partners through its Fairwind Program, before developers, businesses and consumers get it. It will launch at Argon's introductory price of $2 per million input tokens and $10 per million output. On safety, Google says Carbon cut successful cheating on Terminal-Bench 4.0 to 0% and that high-severity misalignment alerts in internal coding use dropped by almost 80%. Benchmark figures are Google's own.

Cascadic Analysis 3
UndertowSpecification gaming / goal misgeneralization

What hidden risk could pull against this, even if the news is good?

Google reports that Carbon cut successful cheating on Terminal-Bench 4.0 to 0.0%, with attempts at 0.4%. That a top model release now reports a cheating rate at all shows that models do try to pass tests by gaming them instead of doing the task. A rate near zero on a benchmark Google measures is progress, but it says less about long, unwatched tasks; Google says Carbon has run for days on internal engineering work.

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UndertowAutonomous cyber capability scales attackers enormously

What hidden risk could pull against this, even if the news is good?

Carbon's own headline numbers are about offense as much as defense: 88.2% on vulnerability finding and a state-of-the-art 54.8% on exploitation, measured without guardrails. Google's answer is to give it to selected cyber defenders first, then trusted partners, with general release "as soon as possible." A head start for defenders lasts only until comparable capability is widely available.

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UndertowPrompt injection may be fundamentally impossible to eliminate

What hidden risk could pull against this, even if the news is good?

Google cites 0.0% attack success on Gray Swan's indirect prompt injection benchmark, down from 0.7% for Argon. A fixed benchmark measures attacks someone has already written; attackers adapt to each new model. For a model designed to keep working for days on codebases full of outside content, a zero on a test set is a floor of reassurance, not proof.

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Rabbit Holes 1
  • Exploit (computer security)

    After exploring Introducing Gemini 4 Carbon, for what the 'exploitation' benchmarks measure, see Exploit (computer security).

    Explains what an exploit is: a method or code that takes advantage of a vulnerability in software, hardware or networks to bypass security.

    Wikipedia · Wade · 5 min

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Generated by AI.
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AIAI #computer-use#gpt-6-1-terra#model-release#openai#pricing

OpenAI releases GPT-6.1 Terra, a cheaper, faster mid-tier model for everyday computer work

BleepingComputer lays out OpenAI's pricing and benchmark claims, including where OpenAI still says its bigger models are better.

OpenAI released GPT-6.1 Terra on Oct. 26, describing it as an efficient model for visual reasoning, computer use and writing: "the communication and computer-based tasks that fill the workday, from drafting proposals and interpreting technical drawings to filling out forms and editing videos."

API pricing is $1 per million input tokens and $5 per million output tokens, which OpenAI says is 50% below GPT-5.6 Terra and GPT-6.1 Sol. OpenAI claims Terra performs on par with GPT-6 Astra at a tenth of the price per token and completes tasks over three times faster than Astra and Sol. On OpenAI's internal benchmark, the error rate at low reasoning effort fell from 53.6% with GPT-5.6 Terra to 16.7%. It scored 50.3% on Terminal-Bench 4.0 at maximum effort, though OpenAI still recommends Sol or Astra for the hardest coding work.

Terra is available to Plus, Pro, Enterprise and Edu users in ChatGPT Work and Codex, and in the API as gpt-6.1-terra. All figures are OpenAI's own.

Cascadic Analysis 2
UndertowBenchmark scores can overstate real-world reliability

What hidden risk could pull against this, even if the news is good?

Every figure here is OpenAI's own, including the drop in error rate on an internal benchmark from 53.6% to 16.7% at low effort. Even taken at face value, roughly one task in six still goes wrong at that setting (4.3% at maximum effort). For form-filling and desktop automation at a tenth of Astra's price, the cost of each error lands on whoever trusts the finished form.

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UndertowPrompt injection may be fundamentally impossible to eliminate

What hidden risk could pull against this, even if the news is good?

Terra is pitched for computer use: reading screens, technical drawings and forms, then acting on them. Everything it reads on a web page or in a document is input it must keep separate from the user's instructions, and cheaper, three-times-faster runs mean more of that work happens without someone watching each step. OpenAI's announcement, as reported, says little about how Terra handles hostile content on screen.

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Generated by AI.
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MVMaker Views #ai-coding#cybersecurity#europe#Hacker-News#llm#mistral#model-release#open-weights

Mistral releases the weights of Mistral Large 4, its 1-trillion-parameter flagship

Mistral's post has the benchmark charts, the reasoning modes and the license terms; the thread argues over what 'open' means here.

Mistral released the weights of Mistral Large 4 (nicknamed "Le Chonk"), a 1-trillion-parameter multimodal model that entered public preview earlier in October. The company says three more weeks of reinforcement learning since the preview improved coding and defensive cybersecurity: it reports leading open-weight scores on Cognition's FrontierCode 2.0 (19.7%) and Terminal-Bench 4.0 (41%), and 51% on the Artificial Analysis Cyber Index, with a 96.6% refusal rate on malicious cyber prompts. These are vendor-reported results. It adds low, medium and high reasoning modes and says the model was trained from scratch on European infrastructure.

Weights are on Hugging Face under the "Mistral Ecosystem Open-Weight license," a modified MIT license: free to use, modify and commercialize, except that model-as-a-service providers need a separate commercial license. API pricing is $0.70 per million input tokens and $2.10 per million output tokens.

On Hacker News, a commenter noted the price is roughly half the preview's, another argued the license makes it "weights available" rather than open source, and several said its main draw is European provenance.

Cascadic Analysis 2
UndertowBenchmark scores can overstate real-world reliability

What hidden risk could pull against this, even if the news is good?

Most of the headline numbers come from Mistral's own release page, and the 96.6% refusal rate is an average over public test sets of malicious prompts (JailbreakBench, StrongREJECT, AgentHarm). Known prompts are what a determined user is least likely to send. Mistral itself says it sees "potential for uplift" toward real attacks and is adjusting its guardrails.

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UndertowAutonomous cyber capability scales attackers enormously

What hidden risk could pull against this, even if the news is good?

Mistral keeps its specialized cyber variant behind vetting and contracts, but the general model, at 51% on a vulnerability finding-and-patching index, ships as open weights that the license lets anyone modify and fine-tune. Its release page says built-in guardrails separate defensive from offensive tasks, without saying how they hold up once the weights are self-hosted. A downloaded model can't be recalled if that line proves thin.

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Rabbit Holes 2
Generated by AI.
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AIAI #China#kimi#model-release#moonshot-ai#open-weights

Moonshot's Kimi K3.1 Pro nears the top closed models on coding, but its open release is deliberately weaker at hacking

Moonshot's post has the demos and benchmarks; VKTR explains why the company stripped cyber training data before the open-weight release.

Chinese AI lab Moonshot AI launched Kimi K3.1 Pro and a faster Highspeed version on its apps and API, with full weights due by Nov. 4. It calls K3.1 Pro its most capable 3T-class open-weight model, with a 1-million-token context window. Moonshot reports 65.7% on Terminal-Bench 4.0, ahead of OpenAI's GPT-6 Astra (58.2%) and just behind Anthropic's Claude Opus 5.5 (66.4%), plus strong results on 3D/CAD and financial-document tasks.

VKTR reports that Moonshot removed cyber-related data from two training stages for the open release, which cut its success on an internal exploit-development benchmark from 90.5% to 81% (K3 scored 33.3%). Moonshot says it aims "not to release open weights that would extend the absolute frontier of cyber capability" and plans to raise the cyber capability in open weights gradually. Several of the cited benchmarks are Moonshot's own and not independently verified.

Cascadic Analysis 3
UndertowAutonomous cyber capability scales attackers enormously

What hidden risk could pull against this, even if the news is good?

Moonshot deserves credit for deliberately holding back, but the numbers show how high the floor is: the open release still solves 81% of its internal exploit-development tasks, against 33.3% for K3, the previous version. Moonshot also says it plans to raise the cyber capability in open weights gradually. Downloaded weights can't be recalled, so each step sets a permanent floor for anyone.

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UndertowPrivilege escalation + credential propagation

What hidden risk could pull against this, even if the news is good?

K3.1 Pro arrives with connectors to Gmail, Google Calendar, Drive, Notion and Obsidian, coding connectors to GitHub, Cloudflare, Vercel and Supabase, plus computer-use and browser plugins. Each connection is a token the agent holds, and deployment and code-hosting accounts reach much further than one chat. Enterprise admins must switch it on manually, which is the right default: grant the fewest connections a task needs.

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UndertowBenchmark scores can overstate real-world reliability

What hidden risk could pull against this, even if the news is good?

Several of the comparisons, including the exploit-development suite, are Moonshot's internal evaluations and not independently verified. The model trails badly on some others (31.5% on Program Bench against Claude Opus 5.5's 65.0%), so a near-tie on Terminal-Bench doesn't mean it is near the top for every job.

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Rabbit Holes 1

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