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OpenAI is encouraging developers moving from GPT-5.6 Sol to its newer GPT-6 Astra model to consider using lower reasoning settings, arguing that Astra at low reasoning can outperform Sol at high reasoning on some workloads.

Thibault Sottiaux, who leads OpenAI’s core products including Codex, said in a post on X that GPT-6 Astra at low reasoning performs better than GPT-5.6 Sol at high reasoning. He suggested that developers who were satisfied with high reasoning on Sol consider moving to low or medium reasoning on Astra.

The recommendation is significant because GPT-6 Astra carries higher API prices than GPT-5.6 Sol. OpenAI lists Astra at $10 per million input tokens and $50 per million output tokens, compared with $4 and $20, respectively, for Sol. The pricing means Astra costs 2.5 times as much per token, making the number of tokens used to complete a task an important part of the overall cost calculation.

OpenAI’s argument is that token pricing alone does not determine the final cost of a task. A more capable model may require fewer output tokens or fewer interactions to complete the same job, potentially offsetting its higher per-token price.

The company’s published evaluations provide examples of this. On Terminal-Bench 4.0, GPT-6 Astra scored 57.9%, compared with 37.3% for GPT-5.6 Sol, while OpenAI estimated Astra’s API cost per task to be about 9% lower. On GPQA Diamond, Astra’s lower-cost setting scored 94.9%, compared with 94.6% for Sol’s best result, with an estimated 37% lower API cost.

Independent testing also points to the importance of reasoning settings. In a developer comparison reported in the supplied material, Astra at medium reasoning completed a coding workload in fewer requests and with substantially fewer input tokens than Sol at high reasoning. The reported Astra-medium run took about 51 minutes and cost an estimated $25.67, compared with approximately 75 minutes and $31.79 for the Sol-high run.

However, those results should not be interpreted as evidence that Astra’s medium or low setting will always be cheaper or better. AI performance and cost can vary considerably depending on the workload, model setting, number of interactions and amount of reasoning required.

OpenAI’s own model documentation presents Astra as its flagship model for complex reasoning and coding, while positioning other GPT-5.6 models for different cost and workload requirements. Astra supports multiple reasoning levels, including low, medium, high, xhigh and max, giving developers more control over the amount of reasoning used.

The difference highlights a broader issue for developers evaluating AI models: per-token pricing is only one part of the economics of an AI application. A model with a higher price per token can potentially be less expensive for a particular workload if it needs fewer tokens, fewer requests or less time to reach a satisfactory result.

At the same time, developers cannot assume that lowering reasoning will always produce the best balance. More complex tasks may benefit from higher reasoning effort, while simpler or highly repetitive workloads may be better suited to lower-cost models or settings.

The practical implication for developers considering Astra is therefore not simply to compare the listed token prices. Instead, organizations will need to benchmark models using their own workloads, measuring accuracy, latency, token consumption and total cost.

OpenAI’s recommendation effectively shifts the migration question from whether Astra is more expensive per token to whether it can complete a given task more efficiently. For developers, that makes workload-specific testing increasingly important when deciding which model and reasoning level to use in production.

New Delhi, September 4: Deloitte has launched a global Open Model Engineering practice aimed at helping enterprises develop, customize and deploy open artificial intelligence models for business applications, reflecting growing interest among organizations in having greater control over their AI systems.

The new practice will initially focus on markets across North America, Europe and Asia Pacific. Deloitte said its approach will cover open-model engineering, model fine-tuning, AI security, open-source frameworks and sovereign AI, as enterprises increasingly move AI applications from experimentation into operational environments.

The initiative comes as businesses evaluate alternatives to relying entirely on closed, proprietary AI models. Open models can be adapted and fine-tuned for specific requirements and can potentially be deployed across different infrastructure environments, giving organizations greater flexibility over how AI systems are developed and operated.

Enterprise AI workloads can have significantly different requirements. Customer-service applications may prioritize speed and efficiency, while financial or government systems can require stronger data controls and specific deployment arrangements. Manufacturing applications may also require AI systems to operate closer to industrial infrastructure. An open-model approach can allow organizations to select and customize models according to these requirements.

Deloitte’s initial approach includes NVIDIA’s Nemotron open models and NIM microservices. The company is also emphasizing sovereign AI capabilities, which are intended to give organizations greater control over where AI systems and associated data are processed. Such considerations can be particularly relevant to governments and highly regulated industries where data location, security and compliance requirements play an important role.

Cost is another consideration as enterprises expand AI deployments. AI systems that process large numbers of requests can generate significant inference and infrastructure costs. Organizations may therefore evaluate models not only on their capabilities but also on factors including performance, computing requirements and operating costs. Open models can provide additional options for balancing these factors through customization and deployment choices.

The practice also reflects a broader shift in enterprise AI requirements. Organizations increasingly need to integrate AI models with existing data, software applications, infrastructure, security systems and governance processes. Deloitte said it plans to hire, train and certify Forward Deployed Engineers as part of the initiative, highlighting the growing demand for professionals capable of implementing AI systems within business environments.

The company’s strategy also includes agentic AI, in which AI systems can perform multi-step tasks, use digital tools and interact with business systems with greater autonomy than conventional chatbot applications. Deloitte’s Zora AI platform incorporates agentic capabilities built around NVIDIA technologies.

The expansion of agent-based systems could further increase demand for flexible AI architectures. Different business agents may require different combinations of reasoning capability, speed, cost, security and specialization. Organizations could therefore use multiple models for different workloads rather than relying on a single model across all applications.

However, adopting open models also creates additional responsibilities for enterprises. Organizations must address issues including security, model evaluation, governance, reliability, intellectual property, bias, infrastructure requirements and ongoing monitoring. Greater control over model customization and deployment also requires technical expertise to manage the resulting systems.

Deloitte’s move comes amid a broader evolution in enterprise AI strategies. Businesses are increasingly assessing AI architectures based not only on model performance but also on cost, security, flexibility, data control and deployment requirements.

The development suggests that enterprise AI adoption is moving beyond simply accessing individual models through applications or APIs. Increasingly, organizations are focusing on how different models, data sources, infrastructure and AI agents can be integrated into reliable systems capable of operating at scale.

September 4: Krishna Janmashtami is being observed across India with prayers, devotional programmes and traditional celebrations marking the birth of Lord Krishna, one of the most widely revered figures in Hindu tradition.

Observed on the eighth day of the dark fortnight of the Hindu month of Bhadrapada, Janmashtami is associated with the birth of Krishna in Mathura. Devotees traditionally observe fasting, offer prayers and participate in religious programmes leading up to celebrations at midnight, the time traditionally associated with Krishna’s birth.

Temples and religious institutions across the country organise special prayers, devotional singing, scripture readings and cultural programmes on the occasion. Many devotees decorate temples and homes with flowers, lights and images associated with Krishna.

The festival also has strong regional traditions. In Maharashtra and parts of western India, celebrations include Dahi Handi, in which groups form human pyramids to reach and break a pot containing curd or other offerings. The tradition is associated with stories of Krishna’s childhood and his fondness for butter and curd.

In several parts of northern India, particularly Mathura and Vrindavan, Janmashtami is marked through elaborate temple ceremonies, devotional performances and programmes based on episodes from Krishna’s life. The two towns hold particular religious significance because of their association with Krishna’s childhood and early life.

Janmashtami is also observed in different forms across other parts of the country, with local customs, music, dance and religious practices adding to the cultural diversity of the festival.

Beyond its religious significance, the festival has become an important part of India’s cultural traditions. Stories associated with Krishna, including those concerning his childhood, his teachings in the Bhagavad Gita, and his role in the Mahabharata, continue to influence Indian literature, performing arts, music and popular culture.

The Bhagavad Gita, traditionally presented as a dialogue between Krishna and Arjuna, places emphasis on duty, ethical action and spiritual understanding. These themes remain central to the religious and philosophical significance associated with Krishna.

Janmashtami celebrations also bring communities and families together through shared prayers, cultural programmes and traditional activities. For many devotees, the occasion represents an opportunity for reflection on values associated with Krishna, including compassion, duty, wisdom and devotion.

As devotees observe the festival across the country, Janmashtami continues to combine religious observance with India’s diverse cultural traditions, connecting communities through celebrations that have been passed down across generations.

September 2: India’s real GDP grew by 7.8% in the first quarter of FY2026-27, exceeding expectations and improving on the 6.9% growth recorded in the same quarter a year earlier. The numbers point to strong economic momentum, supported by domestic consumption, investment, manufacturing and services.

However, the headline growth rate does not capture the full picture. The more important question for India is whether this expansion can be sustained while creating productive employment, raising incomes and ensuring that growth reaches sectors and regions that remain more vulnerable to economic shocks.

One of the encouraging features of the latest data is the resilience of domestic demand. Household consumption grew by 7.1%, indicating continued spending by consumers despite uncertainty in the global economy. Strong consumption supports businesses and production, but it cannot remain the sole engine of expansion if incomes and productivity do not rise alongside it.

Investment provides a more significant indicator of future capacity. Gross fixed investment grew by 11.9% during the quarter. Sustained investment in factories, infrastructure, technology and machinery can expand productive capacity and potentially create a cycle in which higher capacity supports production, employment and household incomes.

Manufacturing also recorded strong growth of around 9.2%. This is particularly relevant to India’s long-term development objectives. While services remain a major strength, manufacturing can help expand employment, increase exports and connect Indian businesses more deeply with global supply chains.

The services economy continues to provide another source of strength. The sector grew by around 10%, while financial, real-estate and professional services expanded by 12.1%. The performance reflects continued activity across urban and corporate India and reinforces the importance of services to the country’s economic structure.

Credit growth adds another dimension to the expansion. Businesses and households continue to access financing, supporting economic activity. The quality of that credit will matter, however. Lending that finances productive investment can contribute to future economic capacity, while credit used primarily to support short-term consumption or leverage carries different implications for long-term growth.

The data also highlight areas of weakness. Agriculture grew by only 3.6%, while mining and quarrying contracted by 2.4%. These figures underline the uneven nature of India’s economic expansion.

GDP combines the performance of very different sectors into a single number. For households, however, economic conditions can vary considerably depending on employment, location and income source. Strong financial services and manufacturing growth do not necessarily translate immediately into improved conditions for rural households or young people entering the labour market.

This makes employment and income growth important measures of the quality of India’s expansion. The challenge is not simply to produce more goods and services, but to ensure that rising productivity creates productive jobs, stronger wages and broader economic opportunities.

Manufacturing could play an important role in that process. A sustained expansion of manufacturing clusters and supply chains could create opportunities across logistics, transport, finance, technology and smaller businesses. But achieving this will require continued improvements in infrastructure, productivity, skills and the overall cost of doing business.

India also remains exposed to external risks. Geopolitical tensions, trade restrictions, supply-chain disruptions and fluctuations in global crude prices can affect domestic economic conditions. India’s dependence on imported oil means a sustained rise in crude prices could increase transportation and production costs, put pressure on the rupee and create inflationary risks.

The monsoon remains another important variable. Agriculture’s direct contribution to GDP may be smaller than that of services, but rural incomes influence consumption, food prices and demand for consumer goods. A weak agricultural season can therefore affect the wider economy.

Monetary policy will also require careful balancing if strong growth continues. Robust domestic demand provides room for economic expansion, but policymakers must ensure that inflation remains contained. Higher energy and imported-input costs could complicate that task if external price pressures intensify.

Strong GDP growth should also not be confused with guaranteed financial-market gains. Equity markets and other financial assets respond to a broader set of factors, including interest rates, corporate earnings, valuations, capital flows, currency movements and global risk sentiment. The real economy and financial markets are closely connected, but their performance does not always move together.

Despite these caveats, the latest GDP data contain several positive signals. Consumption remains resilient, investment is expanding, manufacturing is growing strongly and services continue to provide substantial support. Overall growth has also remained robust despite an uncertain international environment.

The next challenge is to convert that momentum into durable and broad-based economic progress.

India will need to translate investment into productive capacity, manufacturing growth into employment, productivity gains into higher incomes and services growth into opportunities for a wider section of the workforce. Strengthening rural incomes and improving the resilience of agriculture will also remain important.

The 7.8% growth rate is therefore a strong starting point rather than the final measure of India’s economic performance. Sustained growth will depend on whether the economy can maintain investment, improve productivity, generate productive employment and withstand external shocks.

India has demonstrated that it can grow rapidly. The more important test now is whether that growth can become more inclusive, productive and durable.