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AI & Tech

AI News: Anthropic and OpenAI Spark a New Race to Cut Model Costs

Nic Reeve5 min read
AI News: Anthropic and OpenAI Spark a New Race to Cut Model Costs

Anthropic and OpenAI unveiled cheaper models on September 22, 2026, intensifying a price battle with lower-cost AI developers and giving business customers new alternatives. The releases are central to the latest AI news: Anthropic launched Claude Opus 5.5, while OpenAI added GPT-6 Sol and GPT-6 Luna to its model family.

What did Anthropic release?

Anthropic introduced Claude Opus 5.5 on September 22, 2026, positioning it as a less expensive option that can approach the performance of its top model. The company said the system matches Claude Fable 5.1 on most tasks, while independent reporting focused on lower token prices and reduced operating costs.

  • According to Business Standard, 2026: Claude Opus 5.5 costs $4 per million input tokens and $20 per million output tokens.
  • According to The Korea Economic Daily’s English report, 2026: those prices are below Opus 5’s $5 input and $25 output rates.
  • According to Anthropic’s claims reported by Fortune, 2026: Opus 5.5 performs at the level of Fable 5.1 and costs about 40% less to run than Opus 5 in practical use.

Anthropic’s pricing distinction matters because a token rate does not capture the full cost of an AI task. A model that produces fewer unnecessary tokens or uses computing resources more efficiently can reduce a customer’s final bill even when the headline API price tells only part of the story.

How did OpenAI respond?

OpenAI announced GPT-6 Sol and GPT-6 Luna within hours of Anthropic’s release. The two systems target different workloads. Sol is aimed at more demanding tasks such as coding, while Luna is designed for high-volume jobs including document summarization and information extraction.

  • According to CNBC, 2026: OpenAI cut API prices for GPT-6 Sol and GPT-6 Luna by 50% compared with promotional pricing for GPT-5.6.
  • According to OpenAI’s statement quoted by Tech.co, 2026: improvements in caching and inference enabled the company to serve the models at a lower cost.
  • According to CNBC, 2026: GPT-6 Sol sits below OpenAI’s GPT-6 Astra model in the company’s lineup.

The split reflects a practical change in how providers package advanced systems. Instead of offering one premium model for every job, companies are creating tiers for coding, reasoning, extraction, summarization and other workloads with different cost and speed requirements.

Why are the new models cheaper?

The companies point to efficiency rather than a simple reduction in capability. Anthropic said Opus 5.5 uses computing resources more effectively and produces less verbose responses. OpenAI attributed its lower pricing to caching and inference improvements, which affect how much computing power is needed when a customer sends a request.

  • According to National Technology, 2026: Anthropic priced Opus 5.5 20% below Opus 5 on a per-token basis.
  • According to National Technology, 2026: Anthropic said practical running costs fall by about 40% because of efficiency gains and lower output volume.
  • According to The Korea Economic Daily’s English report, 2026: Opus 5.5 scored higher than Fable 5.1 on some coding and knowledge-work benchmarks, according to Anthropic.

Those figures come from company claims and should not be treated as a universal price comparison. The final expense depends on prompt length, response length, caching, usage volume, latency targets and the way a customer integrates a model into its software.

Who is affected by the price cuts?

Businesses that process large volumes of text or code stand to gain the most immediately. Lower inference costs can change the economics of customer-service agents, search tools, coding assistants, document systems and internal knowledge applications. Developers also gain more freedom to assign routine work to cheaper models while reserving premium systems for difficult requests.

  • High-volume users can benefit from Luna’s focus on extraction and summarization, according to CNBC, 2026.
  • Software teams working on code can evaluate GPT-6 Sol and Opus 5.5 against higher-priced frontier models, based on the product descriptions reported by CNBC and Business Standard, 2026.
  • Customers comparing providers must assess total task cost rather than API prices alone, because efficiency and output length affect the final bill.

Cheaper access may also increase experimentation. A startup that could not justify frequent use of a premium model may now test automated workflows, provided the model meets its accuracy, privacy and reliability requirements.

Is this a response to cheaper rivals?

Yes. Reporting from CNBC, NDTV and other outlets links the releases to growing pressure from lower-cost and open-weight models, particularly from China and other cost-conscious markets. The competitive question is no longer only which model performs best. It is also which provider can deliver acceptable results at a price that supports large-scale deployment.

Open-weight systems add a different kind of pressure because customers can run them on their own infrastructure or through competing hosting services. Closed providers such as Anthropic and OpenAI retain advantages in model access, managed infrastructure and integrated tools, but price-sensitive buyers can compare more options than before.

What happens next?

Anthropic said it expects to release Sonnet 5.5 and Haiku 5.5 in the coming weeks. That planned expansion would give customers additional choices below or alongside Opus 5.5. OpenAI’s GPT-6 Sol and Luna releases also suggest that the company is building a broader range of models around its GPT-6 platform.

  • According to Fortune, 2026: Anthropic expects Sonnet 5.5 and Haiku 5.5 to arrive in the following weeks.
  • According to The Peninsula, 2026: the releases came days after officials from both companies had called for a slowdown in AI development.
  • According to TechXplore, 2026: the cheaper launches arrived amid continuing concerns about the safety of rapidly advancing AI systems.

That timing creates a clear tension. Company leaders can call for caution around frontier development while their businesses continue releasing models designed to win more workloads. For customers, the immediate result is a wider field of systems with different trade-offs among price, capability, speed and safeguards.

Sources

  1. 1.cnbc.com
  2. 2.thepeninsulaqatar.com
  3. 3.business-standard.com
  4. 4.en.sedaily.com
  5. 5.ua.news
  6. 6.techxplore.com
  7. 7.news.abs-cbn.com
  8. 8.chosun.com
  9. 9.fortune.com
  10. 10.nationaltechnology.co.uk
  11. 11.ndtv.com
  12. 12.tekedia.com
  13. 13.inc42.com
  14. 14.tech.co
  15. 15.siliconangle.com

Read more →

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AInews: Grok Bot gains deep integration with X social platform
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AInews: Grok Bot gains deep integration with X social platform

AInews: Grok Bot gains deep integration with X social platform On August 29, 2026, xAI announced that Grok Bot now connects directly to user accounts on X, marking a new phase in AInews coverage and interaction inside the social network through native access to timelines, posts, mentions and developer tools. How does Grok Bot now work with X day to day? Grok Bot can link to an X account, read timelines and mentions, search public posts and act on live social data from inside its chat interface. Paid Grok Bot users receive free X API credits once connected, turning the assistant into a working agent inside the social platform rather than a separate chatbot. According to xAI’s product update on August 29, 2026, the new integration lets users: Connect an X account directly inside Grok Bot via an official X connector. Have xAI automatically create a developer account on X for users who do not already have one. Receive free API credits on X if they are paying Grok Bot subscribers, allowing immediate programmatic access. Ask Grok Bot to search posts, read the personal timeline, check mentions or assemble summaries of what is happening on X in real time. The integration runs on a dedicated cloud execution environment that combines real‑time X data, browser‑level UI control and the Grok model family. This makes Grok Bot more than a chat interface. It becomes an autonomous teammate designed to operate persistently against social data streams. What changed for Grok Bot users with this rollout? The August rollout opened X integration to Grok Bot subscribers and broadened access across multiple paid plans. Users on SuperGrok, Cursor Pro tiers and Cursor Teams can now attach X accounts, use bundled API credits and run bots that watch and respond to activity across X without building their own infrastructure. 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NASA lunar AI puts IBM’s valuation back under the microscope
AI & Tech

NASA lunar AI puts IBM’s valuation back under the microscope

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NASA’s science office states that the model is hosted on public machine learning platforms with the full codebase on developer repositories so that any scientist can download, test and adapt it. Release date: September 10, 2026, announced jointly by IBM and NASA. Scope: Lunar ice, craters, volcanic history and surface mapping. Access: Model weights under an open license with code available for fine‑tuning and experimentation. Partners: IBM Research, NASA Science and academic collaborators. Wire coverage from Reuters describes the system as an open‑source AI tool designed to analyze decades of lunar observation data and help support a long‑term human presence on the Moon. Tech and science outlets emphasise that researchers can use the model to pinpoint likely buried ice in permanently shadowed craters, map craters at coarse resolution, and explore the Moon’s volcanic history more accurately than earlier methods. How much better is the NASA‑IBM lunar model than existing methods? Independent reports on the NASA‑IBM Lunar Foundation Model say it improves feature detection on the Moon’s surface by a little over twenty percent compared with widely used approaches, using less labeled data to achieve that performance. That uplift in accuracy is one reason investors are re‑examining IBM’s AI capabilities when discussing valuation. Reuters cites NASA and IBM as saying that in benchmark tests the lunar model identified key features on the Moon’s surface up to 23% more accurately than widely used methods. Tech‑focused coverage reports that predictions of buried ice in shadowed polar craters come with 22% less error than the best available dedicated algorithms, while crater mapping at coarse resolution reaches 19% better accuracy while using only half as much labeled training data. Ice prediction error: According to TechTimes on September 11, 2026, error rates are reduced by 22% compared with the best prior algorithm. Crater mapping accuracy: The same report cites a 19% improvement at coarse resolution. Overall feature identification: Reuters reports up to 23% higher accuracy than widely used methods in benchmark tests. Labeled data usage: TechTimes notes the lunar AI model reached its gains with only half as much labeled training data. A technology analysis piece on the model states that NASA’s science team confirmed the system is available on public AI platforms with the complete codebase on code hosting sites, mirroring the distribution approach IBM used for the earlier Prithvi Earth‑observation foundation models. Those earlier models, introduced in 2023 to work on Harmonized Landsat Sentinel‑2 data, were reported by IBM to deliver about a 15% improvement over state‑of‑the‑art techniques in flood and burn‑scar mapping using half the labeled data. This pattern of releasing geospatial models with clear performance gains and open access has helped establish IBM as a reference player in scientific AI, which is now feeding into analyst and investor conversations about the company’s earnings power and valuation multiples. How does this lunar AI fit into IBM’s broader artificial intelligence strategy? The lunar foundation model extends IBM’s strategy of building domain‑specific foundation models under its watsonx portfolio and collaborating with public institutions on open geospatial AI. That strategy now spans Earth observation, weather, environmental intelligence and lunar science, and is increasingly cited in research coverage of IBM’s stock. IBM’s August 3, 2023 announcement of its geospatial foundation model described training a large AI system on one year of Harmonized Landsat Sentinel‑2 satellite data across the continental United States, with fine‑tuning for tasks such as flood and burn scar mapping. According to IBM, that Earth‑focused model delivered a 15% improvement over state‑of‑the‑art techniques using half the labeled data, and a commercial version was slated to be integrated into the IBM Environmental Intelligence Suite, part of the broader watsonx ecosystem. Foundation model family: IBM and NASA’s models join the Prithvi family of geospatial and weather foundation models highlighted in coverage of the lunar release. Commercialisation path: IBM’s geospatial model is linked to the Environmental Intelligence Suite, showing how scientific AI is tied to revenue‑producing software. Open science strategy: NASA and IBM host weights and code under open licenses, encouraging global research use. Brand positioning: IBM’s newsroom clusters the lunar model under its artificial intelligence press releases, presenting it as part of its AI leadership narrative. NASA’s coverage of the lunar foundation model emphasises collaboration not only with IBM but with academic partners, reinforcing IBM’s position as a scientific computing partner rather than simply a commercial vendor. For investors, that dual role matters because it shapes perceptions of IBM’s long‑term relevance in high‑impact domains such as space exploration and climate science. Why is IBM’s stock valuation “back in focus” following the lunar AI launch? Recent analyst reports show renewed attention on IBM’s earnings potential from AI and quantum initiatives, with the lunar model serving as a high‑visibility example of IBM’s technical depth. Consensus targets point to modest upside, and some coverage links positive sentiment directly to IBM’s AI collaborations and product roadmaps. A stock analysis article dated September 12, 2026 reports that Wall Street holds an overall Buy consensus on IBM shares, citing data that 25 analysts have set a 12‑month price target of USD 245.35, about 4.8% above IBM’s September 10 closing price of USD 234.02. The same coverage references other compilations indicating a Moderate Buy consensus and an average target price around USD 265.90, which would imply stronger upside from trading levels near USD 243. Closing price reference: TheStreet coverage cited by ad‑hoc news puts IBM’s closing price on September 10, 2026 at USD 234.02. Analyst count: 25 analysts in that survey with a 12‑month target of USD 245.35, according to TheStreet via ad‑hoc. Consensus descriptor: Separate market data services describe a Moderate Buy rating with an average target of USD 265.90. Valuation metrics: Seeking Alpha’s snapshot on around September 10 lists a forward non‑GAAP price/earnings ratio of 20.22, a GAAP trailing P/E of 22.15, and a price/book multiple of 6.81. The Seeking Alpha figures also show an enterprise value to sales ratio of 4.22 and an enterprise value to EBITDA of 17.72 for IBM, framing the company as a mature technology firm with premium valuation compared with many legacy peers but trading at a discount to some faster‑growing AI‑focused companies. Market commentary links that profile to IBM’s mix of stable infrastructure revenue and emerging growth in AI and quantum computing. Coverage describing IBM stock gains on quantum bets and AI mentions that, despite a legal probe referenced in passing, investor appetite for exposure to IBM’s advanced computing initiatives has remained strong. In that context, the lunar foundation model is cited as a showcase of IBM’s ability to collaborate with agencies such as NASA on cutting‑edge AI, reinforcing the argument that current valuation metrics may underestimate future cash flows from AI‑enabled products and services. Who is affected by the NASA‑IBM lunar AI, beyond IBM’s shareholders? The lunar model directly affects planetary scientists and engineers working on NASA’s Artemis program, while indirectly shaping vendors and partners involved in lunar infrastructure planning. It also influences academic researchers, AI developers and policy discussions about open scientific data and public‑private cooperation in space exploration. NASA’s material on the lunar foundation model points out that the AI system was trained primarily on data from the Lunar Reconnaissance Orbiter and other instruments, creating a unified dataset suitable for machine learning. IBM’s description of the project explains that the two organisations built what they describe as the first open‑source dataset that consolidates decades of lunar data in a format tuned for AI research. NASA Artemis planners: TechTimes notes that the system can help pick landing sites near the lunar south pole, where ice could support water, oxygen and fuel for onward missions to Mars. Planetary science community: NASA and technology outlets say any researcher worldwide can download and adapt the model for fresh studies of lunar phenomena. Academic partners: NASA references several universities involved in the collaboration, extending access to students and early‑career scientists. Space industry vendors: Clearer maps of ice and terrain support companies working on habitats, mining and resource use on the Moon. For AI developers, the lunar model demonstrates how foundation models can be adapted to domains beyond language and mainstream computer vision. For policymakers, the open‑license approach raises questions about how publicly funded data and private sector technology should be shared when they shape future resource extraction and national presence on the Moon. What happens next for IBM’s AI portfolio and valuation story? Commentary from science and market sources suggests several next steps: wider scientific use of the lunar model, commercial spin‑offs through IBM’s software suites, and ongoing analyst reassessment of IBM’s AI and quantum computing earnings potential. The outcome will influence whether current price targets move higher or stabilise as projects like the lunar AI mature. Technology coverage points out that the lunar foundation model follows the pattern IBM and NASA established with the Prithvi models: open weights, open code, and community‑driven fine‑tuning on public platforms. That history makes it likely that new versions will appear, trained on expanded datasets or adapted to related planetary bodies as agencies gather more remote‑sensing data. Scientific roadmap: NASA’s long‑term goal of a sustained human presence on the Moon gives the lunar AI a central role in mission planning. Commercial potential: IBM’s prior geospatial AI has already been linked to its Environmental Intelligence Suite, hinting that similar integration could follow for lunar or broader space‑data products. Valuation drivers: Analyst targets compiled by market data services will likely evolve as IBM reports concrete revenue tied to its AI models and quantum offerings. Risk factors: Legal probes and competitive pressure from other AI vendors are mentioned in stock coverage as counterweights to growth expectations. 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Nic Reeve·
AInews: TAO Price Forecasts Spike As Fresh AI Agent Platforms Go Live
AI & Tech

AInews: TAO Price Forecasts Spike As Fresh AI Agent Platforms Go Live

AInews: TAO Price Forecasts Spike As Fresh AI Agent Platforms Go Live On 13–20 September 2026, analysts pushed Bittensor’s TAO token price forecasts sharply higher while new autonomous AI agent tools arrived on the market, turning AInews into a window onto both speculative crypto bets and practical software shifting how developers build automated digital workers. What is happening to TAO’s price right now? TAO is trading in the mid‑$250 range and has swung 8–9% in recent days as traders respond to protocol upgrades and the broader AI token rally. Forecasts for September 2026 cluster around a bullish band between roughly $220 and $541, depending on the model and risk assumptions. Recent market data shows: According to CoinMarketCap’s TAO coverage on 18–20 September 2026, TAO has traded roughly between $233 and $260 during several short rallies and pullbacks, with daily moves of 3–9% common in the last week. CoinLore’s short‑term forecast dated 20 September 2026 puts TAO’s current price near $262.32 and projects $250.14 for 21 September 2026, with a 24‑hour trading range expectation between $245.27 and $277.89. CoinMarketCap’s analysis of an 8–9% surge in the 25 hours to 18 September 2026 links the move to protocol tokenomics changes, AI‑linked capital rotation and a technical breakout pattern in the chart. Those swings sit on top of a still‑volatile backdrop. TAO dropped about 4% in a single overnight window in June 2026 when emissions changes and a soft market hit at the same time, according to CoinMarketCap’s June price series review. That context keeps short‑term traders cautious even as price forecasts rise. How high are current TAO price predictions for September 2026? Retail‑focused forecasting sites now show TAO’s possible September 2026 range spanning from the low‑$170s in bearish scenarios up to more than $1,100 in aggressive bullish models. Most cluster in a narrower band, around $220–$370 for traders and $364–$541 in analytical long‑range models. Different services publish notably different numbers: Cryptopolitan’s September 13, 2026 report on Bittensor expects TAO’s September average price around $210, with a bullish breakout target at $275 and a worst‑case low near $176 based on technical resistance and support. OpenPR’s September 13, 2026 note on “TAO Targets $300 After AI Upgrades” argues that $300 is a key recovery level, with TAO trading around $234 at the time and facing overhead resistance near $260. Changelly’s long‑range Bittensor forecast updated September 20, 2026 places September 2026 price scenarios between a minimum of $363.90 and a maximum of $541.27, with an average of $452.59, based on historical volatility and expected AI‑sector demand. CoinLore’s near‑term model, refreshed September 20, 2026, sees the week of 21–28 September sliding from about $250.14 down toward the low $240s before a small bounce, which sketches a more cautious near‑term path. CoinCodex’s predictive model, updated in late August 2026, projected a short dip to around $176.10 by early September, showing how older data can under‑state the later rally driven by AI token narratives. These predictions use different inputs. Some rely mainly on chart patterns and momentum, while others embed assumptions about expanding decentralized AI demand and sustained attention to Bittensor’s role as a specialist network for machine learning workloads. Why are TAO forecasts climbing despite recent volatility? Forecasts are climbing because TAO sits at the intersection of two stories: an evolving tokenomics design that reduces emissions from idle subnets and a global wave of interest in decentralized AI infrastructure. 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Traders watching TAO price action view expanding agent ecosystems as indirect confirmation that demand for decentralized, market‑priced compute services may grow, which feeds back into bullish token forecasts. Who is most exposed to TAO’s swings and the new agent ecosystem? TAO’s volatility and the emerging agent tools touch different groups. Short‑term crypto traders face sharp swings. Long‑term Bittensor stakers and subnet developers watch governance and emissions changes. Meanwhile, AI builders and startups evaluate whether new agents can lower operating costs or open fresh revenue streams. On the TAO side, exposure breaks down into: Day traders and derivatives users speculating on 3–9% daily moves documented by CoinMarketCap’s mid‑September 2026 charts, often using leverage that magnifies both gains and losses. Stakers who lock TAO to support subnets and earn emissions. The decision by Bittensor’s root governance to cut rewards to inactive subnets changes their yield expectations and influences how they allocate capital across the network. Developers who build AI models that rely on TAO‑denominated incentives. Their income streams depend on protocol rules and market prices staying supportive enough to cover infrastructure and research costs. For AI agent tools, exposure looks different: Startups adopting agent frameworks reported by DutchStartup.ai in early September 2026 hope to automate tasks such as booking meetings, handling first‑line customer support or managing data pipelines with minimal supervision. Enterprise IT teams test new orchestration features that link agents to internal systems, raising questions about security, audit trails and compliance as more processes execute without direct human commands. Individual professionals experiment with off‑the‑shelf agents that promise to manage email, research or scheduling, which shifts some white‑collar workloads toward software but still requires careful oversight. What might happen next for TAO and AI agent platforms? The next months will likely hinge on whether Bittensor’s tokenomics changes and governance debates calm investors, and whether AI agent tools move from pilot projects into production deployments. Forecasts remain wide, signalling uncertainty but also strong expectations of continued AI‑linked activity. On TAO’s path forward, available analyses highlight: OpenPR’s mid‑September 2026 prediction of a $300 recovery target assumes smooth integration of recent protocol upgrades and sustained AI‑sector interest. If governance disputes resurface, that path could stall. Cryptopolitan’s broader 2026–2032 outlook places a possible high near $371 for 2026, with an average around $210, underscoring that even optimistic scenarios see extended periods of consolidation and correction. Changelly’s longer‑term estimate of a near‑$1,000 average for TAO in 2026, framed as a theoretical end‑of‑year level, depends on aggressive adoption of decentralized AI and continued scarcity thanks to the 21 million token cap reported by crypto data services. For AI agent tools, DutchStartup.ai’s early‑September 2026 survey suggests that the coming quarters will bring more competition: new frameworks, tighter integrations into productivity suites and cloud providers, and experiments in combining financial incentives with agent performance metrics. As these systems scale, they may drive more demand for flexible AI compute networks, including those priced via tokens like TAO.

Nic Reeve·