allnewscastallnewscast
Breaking News
Gillibrand and Welch Set Fall Senate Hearing Schedule For Tariff Repeal Bill— Politics & ElectionsFederal Judge Says Trump Team Defied Mail-Voting Injunction With New USPS Rule— Politics & Electionsnews1— Politics & ElectionsGillibrand and Welch Set Fall Senate Hearing Schedule For Tariff Repeal Bill— Politics & ElectionsFederal Judge Says Trump Team Defied Mail-Voting Injunction With New USPS Rule— Politics & Electionsnews1— Politics & Elections
AI & Tech

AInews: xAI Unveils Grok 4.7 With Expanded Reasoning for Developers

Nic Reeve6 min read
AInews: xAI Unveils Grok 4.7 With Expanded Reasoning for Developers

AInews: xAI introduced Grok 4.7 on September 21, 2026, positioning the model as its strongest system yet for software development, agentic tasks and professional knowledge work. The release is available through Cursor, Grok Build, the Grok API, third-party coding tools, model routers and cloud platforms, according to xAI’s launch announcement.

What did xAI release on September 21?

xAI released Grok 4.7 as a new flagship model for work that requires extended reasoning, code generation and research. The company says the system is built on a larger base model than Grok 4.6 and received further reinforcement learning on difficult, long-running tasks.

  • Launch date: September 21, 2026, according to xAI.
  • Primary uses: coding, agentic workflows and knowledge work, according to xAI.
  • Model identifier: grok-4.7 on the xAI API, according to xAI’s release information.
  • Inputs and outputs: text and images in, with text output, according to xAI’s published model details.

The launch followed several days of speculation about a new Grok version. Reports published before the announcement pointed to possible appearances in cloud-service quotas, but those reports did not establish a public release. xAI’s September 21 announcement supplied the first direct confirmation.

What capabilities does Grok 4.7 claim?

Grok 4.7 is designed to stay engaged with complex tasks for longer, check its own work and handle large amounts of context. Those claims come from xAI and describe the company’s intended use case rather than an independent performance verdict.

  • Context window: 500,000 tokens, according to xAI’s model information.
  • Reasoning controls: low, medium, high and xhigh settings, according to xAI’s release documentation.
  • Default reasoning level: high, according to release details summarized by independent AI industry publications.
  • Tools: function calling, web search, X search and code execution, according to xAI’s API documentation.

The model’s focus is practical rather than limited to chat. A coding agent can use tool calls, inspect files, revise code and run tests. A research workflow can retain more material in one session. The 500,000-token window also gives developers room to pass large repositories, technical documents or multi-step task histories without splitting every request.

How did the new model perform in published tests?

Early benchmark figures come mainly from xAI’s own launch materials and should be read as company-reported results. Independent coverage has repeated the figures, but the available reports do not establish that every test used identical prompts, tools or evaluation rules across competing systems.

  • CursorBench 4.0: 46.3%, according to xAI’s reported benchmark results.
  • DeepSWE v1.1: 71.0%, according to xAI’s reported benchmark results.
  • EEBench: 64.0%, according to xAI’s reported benchmark results.
  • HealthBench Professional: 56.7%, according to xAI’s reported benchmark results.
  • Harvey Legal Agent Benchmark: 19.6%, according to xAI’s reported benchmark results.

These tests cover different abilities. Coding benchmarks measure software-engineering performance, while health and legal evaluations examine professional question answering or agent behavior. A single percentage cannot describe the model’s performance across every use case.

Users will also need to distinguish benchmark scores from production reliability. A system can produce a strong result under a controlled test and still require human review when it changes a live codebase, handles sensitive records or makes decisions in regulated fields.

Where can customers use Grok 4.7?

Grok 4.7 launched across developer products rather than as a single consumer-only update. xAI says customers can access it through Cursor and Grok Build, while API users can connect it to applications and coding agents.

  • Cursor: available at launch, according to xAI and independent release coverage.
  • Grok Build: available at launch, according to xAI.
  • xAI API: available under the grok-4.7 name, according to xAI.
  • Other routes: third-party coding harnesses, model routers and cloud platforms, according to xAI.
  • GitHub Copilot: independent release reports said the model was added for Pro, Pro+, Max, Business and Enterprise plans on launch day.

Availability can vary by product, plan and region. API access also depends on account approval, endpoint support and a developer’s chosen integration. A model appearing in a routing service does not necessarily mean that every customer receives the same limits, latency or tool access.

How much does Grok 4.7 cost?

xAI priced the standard API model at $2 per million input tokens and $6 per million output tokens for prompts below 200,000 tokens, according to the company’s published pricing. Longer prompts use a higher price tier.

  • Input: $2 per million tokens below the 200,000-token prompt threshold, according to xAI.
  • Cached input: $0.50 per million tokens below that threshold, according to release documentation.
  • Output: $6 per million tokens below the threshold, according to xAI.
  • Longer prompts: $4 input, $1 cached input and $12 output per million tokens above 200,000 tokens, according to xAI’s listed rates.

Token pricing is only one part of the bill. Applications that run repeated tool calls, web searches or code execution can consume more tokens and generate extra service costs. Developers also need to account for storage, monitoring and the cost of human review.

What changed from Grok 4.6?

Grok 4.7 keeps the 500,000-token context window and the standard $2 input and $6 output rates associated with Grok 4.6 for shorter prompts, according to release reports. xAI instead presents the upgrade as a capability improvement aimed at harder tasks, stronger verification and longer-running work.

  • Model scale: xAI describes Grok 4.7 as using a larger base model than Grok 4.6.
  • Training: xAI says the newer system received extended reinforcement learning on harder and longer tasks.
  • Self-checking: xAI says Grok 4.7 verifies its outputs more reliably.
  • Pricing: the standard short-prompt API rates remain $2 per million input tokens and $6 per million output tokens, according to xAI.

The company also lists a faster version called Grok 4.7 Fast. Independent release coverage reported that the faster variant is available through Cursor and Grok Build at twice the standard token rates, while it was not listed as a public xAI API model at launch.

What should developers watch next?

The next test will come from real deployments. Developers will assess whether the model’s longer context and self-checking reduce debugging time, whether higher reasoning settings justify their latency and whether the system behaves consistently across different coding tools and cloud services.

  • Independent benchmark replication could clarify how Grok 4.7 compares with rival systems.
  • API documentation and regional rollout details will determine who can access each feature.
  • Enterprise users will examine privacy, logging, retention and permission controls before wider deployment.
  • Developers will compare the standard and Fast versions on cost, speed and task accuracy.

Grok 4.7 arrives as AI companies compete for developer workloads that involve more than text generation. The product’s success will depend on measurable software outcomes, predictable operating costs and the ability to keep human oversight in the loop when an automated agent changes code or handles sensitive professional work.

Sources

  1. 1.x.ai
  2. 2.releasebot.io
  3. 3.sqmagazine.co.uk
  4. 4.aiweekly.co
  5. 5.cellcog.ai
  6. 6.scriptbyai.com
  7. 7.ai-tldr.dev
  8. 8.kie.ai
  9. 9.orcarouter.ai
  10. 10.mungomash.com
  11. 11.layer3labs.io
  12. 12.iweaver.ai
  13. 13.x.com
  14. 14.cellcog.ai
  15. 15.ai-tldr.dev

Read more →

Related Articles

Illinois State’s ‘End of the World’ Class Puts AI on Trial
AI & Tech

Illinois State’s ‘End of the World’ Class Puts AI on Trial

Students Confront AI Ethics in Illinois State’s ‘End of the World’ Classroom In a seminar room at Illinois State University (ISU), an apocalyptic thought experiment is helping students grapple with one of the most disruptive technologies of their lifetimes: artificial intelligence . Framed as “feminism at the end of the world,” the class invites students to imagine futures shaped by climate crisis, economic collapse, and runaway automation—and then ask what justice, care, and responsibility look like when AI is woven into every aspect of life. The course, titled WGS 391/491: Feminism at the End of the World , is taught by Dr. Jacklyn Weier in Illinois State’s Women’s, Gender, and Sexuality Studies program. Using speculative fiction, feminist theory, and contemporary reporting on AI, Weier’s students interrogate who benefits from emerging technologies and who is left more vulnerable when those tools are deployed in unequal societies. ‘End of the World’ as a Lens on AI Rather than treating AI as a neutral tool, the course positions it as a technology emerging in an already crisis-ridden world. Students consider scenarios in which climate disasters, pandemics, or authoritarian politics intersect with increasingly powerful AI systems. That apocalyptic framing, Weier explains in the Illinois State University News feature, is less about doomsday spectacle and more about clarity: it allows students to see existing inequalities—and the potential amplification of those inequalities—without the distractions of business-as-usual. Class discussions draw on questions such as: Who designs AI systems, and whose values are embedded in them? Which communities are most exposed when automated decision-making is used in policing, immigration, or social services? How might feminist and queer perspectives offer alternative models for building or governing AI, especially in times of crisis? Students are encouraged to treat AI not only as a technical system but as a social infrastructure: something that redistributes power, labor, and risk. That perspective resonates with broader concerns raised by scholars and civil-society groups about bias in algorithms, surveillance capitalism, and the concentration of AI capabilities in a small number of corporations. Illinois State’s Wider Debate Over AI in the Classroom The apocalyptic classroom arrives amid a campus-wide—and statewide—reckoning over how AI should be used in education. Illinois State has devoted increasing resources to helping faculty and students navigate generative AI tools like ChatGPT, Gemini, and Copilot, and to clarifying when such tools enhance learning and when they undermine it. In 2025, the university’s Office of the Cross Endowed Chair in the Scholarship of Teaching and Learning launched a grant program inviting faculty to study how generative AI is used or resisted in courses, and what that means for student learning, assessment, and equity. Those projects are structured around a central question: how is AI being integrated into higher education, and with what consequences for teaching and learning at ISU? Illinois State’s professional development arm has since published guidance for instructors on generative AI in the classroom. That guidance emphasizes transparency and critical engagement: instructors are urged to state clearly in their syllabi when AI use is permitted, explain why particular assignments prohibit AI, and design assessments that prioritize process, reflection, and local or experiential knowledge. Faculty workshops encourage instructors to have students critique AI-generated content, practice fact-checking, and reflect on where AI’s limitations become visible—especially when it comes to hallucinations, bias, and context. The goal is not to ban AI outright but to turn it into an object of analysis and a prompt for metacognition, much like what happens in Weier’s apocalyptic classroom. State Policy: AI Can Assist, But Not Replace, Human Teachers The conversations at Illinois State unfold against a backdrop of new laws in Illinois that specifically address AI in education. Recent legislation requires community colleges to ensure that courses are taught by qualified human faculty and explicitly prohibits using AI systems as the sole source of instruction in place of an instructor. At the same time, the law clarifies that faculty are allowed to use AI as a teaching tool—whether for generating practice problems, simulating scenarios, or tailoring feedback. Another measure directs the Illinois State Board of Education to develop statewide guidance on AI in K–12 settings. That guidance must explain how AI works, offer examples of instructional uses, address data privacy and security, and highlight the risk of unintended bias baked into AI products. It also calls on educators to explicitly teach responsible and ethical AI use, preparing students to evaluate automated systems rather than accept them uncritically. Illinois education officials have since released public-facing guidance that echoes those themes, stressing that AI should support, not supplant, human relationships in teaching and learning. The documents encourage schools to balance innovation with vigilance, especially when it comes to student data and the potential for algorithmic discrimination. An ‘Apocalyptic’ Syllabus Meets Real-World Tech Within this rapidly shifting policy and technological landscape, ISU’s “end of the world” class serves as a kind of laboratory. Students might read feminist science fiction that imagines AI governing resource distribution after climate collapse, and then compare those visions with real-world deployments of predictive analytics in disaster response or public assistance programs. Assignments invite students to bring news coverage, corporate marketing, and government documents into conversation with theoretical texts. For example, a student might juxtapose a tech company’s promise to use AI for equitable healthcare with reports of biased diagnostic algorithms, or analyze how AI-enhanced policing could change under conditions of social unrest or environmental migration. By situating AI in imagined end-times, Weier’s course asks students to strip away the sheen of inevitability that often accompanies innovation narratives. If AI is introduced into a fragile or unjust world, she asks, what safeguards and alternative designs would be needed to prevent it from reinforcing existing hierarchies—or making crises worse? Feminism, Care, and the Future of Work The feminist framing of the course pushes students to pay particular attention to care work, reproductive labor, and the often-invisible human effort that underlies technological systems. Discussion topics include: How AI may reshape care professions, from nursing to education, and what happens when emotional labor is automated or monitored. Who performs the ghost work of data labeling, content moderation, and user support that keeps AI systems running. How automation might intersect with gender, race, and class in future labor markets, especially under crisis conditions. What a more just AI ecosystem would require in terms of labor protections, democratic oversight, and alternative ownership models. Students are encouraged to imagine AI futures in which care, reciprocity, and mutual aid are central design principles rather than afterthoughts. In some projects, that means sketching out hypothetical policies for community-run data trusts or workers’ cooperatives overseeing AI tools in essential services. AI Education Beyond One Classroom Illinois State is also building technical capacity around AI. The university has promoted AI-focused professional development sessions for faculty, including workshops on demystifying AI for teaching and learning and on designing assignments that cannot easily be outsourced to generative tools. In 2026, ISU highlighted a new “AI + Robotics” initiative that introduces pre-service STEM educators to so-called physical AI—systems embedded in robots and other devices. The project, supported by an internal innovation grant, aims to help future teachers understand both the capabilities and limits of AI, and to translate abstract concepts into hands-on classroom activities. Another Illinois State faculty member, Dr. Elahe Javadi from the School of Information Technology, was selected for the inaugural cohort of NSF NAIRR AI Education Fellows. That national role positions ISU at the intersection of AI research and education policy, and underscores the university’s effort to engage with AI not only as an object of critique but as a field in which its faculty and students can lead. Questioning the Future, Not Just the Tools The apocalyptic classroom at Illinois State shows how humanities and social science courses can complement technical and policy efforts around AI. By combining speculative scenarios with rigorous critique, students learn to move beyond questions like “Is AI good or bad?” and toward more specific, grounded inquiries: Which AI, deployed where, under whose control, and with what safeguards? For Weier’s students, the end of the world is less a prophecy than a lens—a way to see clearly the stakes of technological change and the kinds of futures they are willing to build or resist. In that sense, Illinois State’s experiment in apocalyptic pedagogy offers a model for universities everywhere: treat AI not only as a tool to be mastered, but as a system whose power must be scrutinized, contested, and, where possible, redirected toward more just worlds.

Nic Reeve¡
What’s Really Known About OpenAI’s Rumored Next Model
AI & Tech

What’s Really Known About OpenAI’s Rumored Next Model

OpenAI’s rumored next model is generating headlines, but the most defensible version of the story is narrower than the leak chatter suggests: there is no official GPT-6 announcement , and much of what is circulating remains unverified. What appears to be real is that OpenAI is working on a new frontier system under a different name, while the specific claims about “GPT-6” remain speculation. Recent reporting from AI-focused outlets says the company’s next major model is being discussed internally under the codename Astra , not GPT-6. Those reports also say OpenAI has not published a model card, pricing, public API route, or release date, and has not confirmed whether the model will ship as GPT-6, as another GPT-5.x update, or under a different product line entirely. In other words, the name “GPT-6” is being used by the public and by leakers, but it is not an official label. The strongest verifiable signal comes from reporting that OpenAI has shown or discussed progress on a new model family while also slowing parts of development for safety review. One report says OpenAI paused reinforcement learning training for its latest-generation model for about two weeks to upgrade security, monitoring, and alignment systems. That report also says preliminary findings showed the model could identify and exploit a previously unknown zero-day vulnerability in the Artifactory software package proxy service. If accurate, that would explain why OpenAI may be taking a more cautious path before any public release. What should readers believe, then? First, believe that OpenAI is still actively working on a major next-step model. Second, believe that the company has not confirmed that the model is called GPT-6. Third, treat exact claims about parameter counts, context windows, release timing, or benchmark jumps as unverified unless OpenAI itself publishes them. The internet is full of confident numbers, but the available reporting does not support most of those specifics. The current rumor cycle has focused on a few recurring claims: a massive jump in training scale, a much larger context window, and a dramatic leap in reasoning or agentic behavior. Yet none of those details has been confirmed by OpenAI. Even articles that argue a new model is imminent pair that language with strong caveats that no official release date, product page, or spec sheet exists. That distinction matters. A credible leak can indicate the direction of a project, but it is not the same as a product launch. There is also a broader pattern here. OpenAI has recently become more visible in discussing frontier-safety issues, and that creates a natural gap between internal experimentation and public rollout. If the company believes a model could uncover serious security vulnerabilities or behave unpredictably in cyber-related tasks, then additional evaluation would be expected before deployment. That does not prove a delay, but it does make the safety-review explanation plausible. For readers trying to separate signal from noise, the best rule is simple: only the company can confirm the model’s name, timing, and capabilities . Until that happens, “GPT-6 leak” is better understood as a label for a bundle of rumors around OpenAI’s next frontier system, not as a verified product announcement. That means the most credible takeaway is also the least sensational one. OpenAI is almost certainly building something new, but the exact branding and release plan are still unclear. The more specific the claim — whether it is about the model’s size, performance, or launch date — the more skeptical readers should be unless it appears in official OpenAI material or in reporting with direct sourcing. In short, the safe position is not that GPT-6 is fake, but that GPT-6 is unconfirmed . The next OpenAI model may eventually earn that name, or it may arrive as Astra or under some other label. For now, the only thing that can be said with confidence is that the speculation has moved faster than the evidence.

Nic Reeve¡
Google’s New AI Ad Rules Rein In Smart Bidding and Data Feeds in Search
AI & Tech

Google’s New AI Ad Rules Rein In Smart Bidding and Data Feeds in Search

Google is rolling out a series of policy and product changes that significantly tighten how artificial intelligence is used in ad bidding and how data feeds power search advertising, reshaping the playbook for brands and ad-tech startups that depend on Google’s ecosystem. The changes span smart bidding behavior, consent-driven data flows, migration to AI-first campaign types, and updated terms governing how advertiser data can train Google’s generative ad models. Together, they signal a more controlled, compliance-focused phase for AI in search and shopping ads. Smart Bidding: From “Over-Delivery” to Strict Target Enforcement At the heart of the shift is a fundamental update to Google’s Smart Bidding systems. A new mechanism, often described by analysts as Bidding Target Optimization , is scheduled to begin enforcement on August 17, 2026. It alters how cost-per-acquisition (tCPA) and return-on-ad-spend (tROAS) strategies behave in budget‑limited campaigns. Historically, Smart Bidding would sometimes deliver better‑than‑stated efficiency if it found high‑performing inventory within a campaign’s budget. Under the new rules, the algorithms are designed to pull performance toward the advertiser’s stated targets rather than “over‑delivering” efficiency beyond those thresholds. This effectively tightens the bid band around declared goals, forcing advertisers to calibrate their targets more carefully if they want to capture incremental upside. To ease the transition, Google introduced target adjustment tools in early July, allowing advertisers to recalibrate their CPA and ROAS goals before the new enforcement date. Industry commentators say this reduces volatility but also removes some of the hidden upside many performance marketers had come to expect from Smart Bidding. Exploratory AI Bidding Meets Stricter Guardrails In parallel, Google has expanded a feature known as Smart Bidding Exploration. Originally launched for search campaigns, the capability now reaches Performance Max campaigns that do not use product feeds, with feed-based placements such as Shopping still in beta. Exploration allows advertisers to specify a tolerance range around their target ROAS. Within that band, Google’s AI can bid on queries and placements that lack strong historical conversion data, effectively probing unproven traffic that might still meet acceptable efficiency thresholds. Marketers gain access to a wider surface of potential customers, but within tighter economic parameters dictated by their ROAS tolerance settings. Viewed together, Exploration and Target Optimization suggest a new philosophy: Google’s AI is allowed to experiment, but only inside clearly defined financial guardrails. The system is being nudged away from open‑ended opportunism and toward strict adherence to explicitly declared business goals. Consent Mode Reshapes the Data Supply for AI Ads Another critical change affects the data flows that power Google’s AI‑driven ads and measurement. As of June 15, 2026, Google’s Consent Mode v2 became the sole gatekeeper for advertising data collection across key properties such as Google Ads and Analytics. The ad_storage parameter now exclusively controls whether advertising cookies and identifiers can be set and whether ad‑related data can be transmitted. Legacy mechanisms—such as the Google Signals toggle and certain account-level data sharing overrides—have been retired. In practice, if a website does not obtain user consent for ad storage under the updated consent framework, Google’s systems will sharply limit data collection and audience building for that property. This reconfiguration has major implications for AI training. Without compliant consent signals, fewer user-level data points enter Google’s optimization pipelines, which can degrade targeting precision and attribution but improves alignment with privacy regulations. For advertisers and AI startups, the message is clear: consent configuration is no longer a secondary detail—it is now the defining factor in how much data the algorithms can see and learn from. AI Max Campaigns and Forced Migrations On the campaign structure side, Google continues to consolidate legacy formats into AI‑driven types. AI Max for Search, an AI‑centric successor to traditional search setups, moved out of beta and into broad availability in early 2026. New tools let advertisers apply text guidelines that shape automatically generated ad copy while the underlying system uses machine learning to customize messaging and targeting at scale. Dynamic Search Ads (DSA), once a mainstay for automatically matching queries to relevant landing pages, are slated for forced migration to AI Max for Search. The original deadline of September 2026 has been pushed back, with the sunset now delayed into 2027. Nonetheless, Google has confirmed that new DSA creation will be disabled and that existing campaigns will ultimately be transitioned to AI Max, preserving only limited URL controls. Similarly, automated assets and certain broad match configurations will auto‑upgrade to AI Max beginning in September 2026. For startups that have built tooling around DSA and legacy targeting structures, the consolidation raises strategic questions: invest in deeper AI Max integrations or pivot away from Google-specific campaign automation. Updated Terms Clarify How Advertiser Data Trains AI Models Underlying all these product changes are newly updated terms of service for Google Ads and related products, effective July 1, 2026. The revisions clarify how advertiser-supplied creative assets—such as text, images, and product data feeds—may be used to train Google’s generative AI systems for ads. While details vary by region and product, the broad thrust is that Google can use advertiser inputs as training material to improve AI-generated ad copy, image variations, and campaign optimization models, subject to consent, privacy, and contractual boundaries. For marketers, this institutionalizes a reality that has been emerging for several years: the creative and feed data they upload is not just serving current campaigns; it is also helping refine the algorithms that will shape future performance for themselves and others. Regulatory Pressure on AI Search and Data Use Regulators are also exerting pressure on how AI uses content and data in search experiences. In the United Kingdom, the Competition and Markets Authority (CMA) issued a landmark conduct requirement in June 2026, compelling Google to give publishers specific controls over whether their content powers AI-generated search summaries. Under that order, Google must offer granular opt-outs for AI Overviews and other generative features, explain how crawled content is used, and provide engagement metrics and meaningful attribution to publishers whose content appears in AI modules. The company has nine months to fully comply, although regulators expect visible progress well before the deadline. For the broader AI data supply chain, this underscores an emerging principle: access to content and behavioral data for AI training and summarization is no longer assumed—it must be negotiated, disclosed, and controlled. That shift affects not only Google but also third‑party data brokers, scraping-based startups, and ad-tech platforms that rely on Google’s search results and ad inventory as a primary signal source. Implications for Startups and Advertisers For startups operating in search, marketing analytics, or AI ad optimization, Google’s tightening of AI bids and data rules is a double-edged sword. On one hand, clearer guardrails around bidding targets and consent-driven data flows reduce uncertainty and regulatory risk. On the other, reduced access to unconstrained data, forced migrations to AI‑first campaign types, and stricter adherence to declared economic targets make it harder to extract “alpha” purely through arbitrage or aggressive experimentation. Advertisers now face a more technical optimization landscape. Success increasingly depends on: Precisely calibrating CPA and ROAS targets to balance stability with growth. Configuring Consent Mode and ad_storage signals to preserve legally compliant data volume. Adapting to AI Max and other AI‑centric campaign structures without losing essential controls. Understanding how their creative assets and product feeds feed into broader generative AI models. As Google’s AI ad stack matures under stricter rules, both brands and startups will have to treat data governance and bid strategy as core product disciplines, not peripheral operational details.

Nic Reeve¡