AI at half the price? What GPT-6.1 Terra really means for businesses

Can GPT-6.1 Terra automate entire workflows?

5. Affordable AI, big promises: Where GPT-6.1 Terra can help businesses

With GPT-6.1 Terra, a key question regarding AI usage comes to the fore: A model's economic value is not solely determined by its performance. Equally important is how quickly and reliably it completes a task – and ultimately, what a technically sound result costs. According to the published information, Terra combines improved capabilities in visual tasks, software operation, communication, and programming with lower usage costs. This could make the use of artificial intelligence more attractive for repetitive digital work.

However, a cheaper model doesn't automatically make entire professions obsolete – and cheap computing power doesn't improve poorly organized processes. Companies need to examine how reliably Terra handles specific tasks under real-world conditions, how much monitoring and rework is required, and what the consequences of errors would be. This could change, particularly for small and medium-sized enterprises (SMEs), industry, and logistics, which jobs can be economically automated. Ultimately, the decisive factor isn't just the token price, but the cost per professionally acceptable result.

Cheap intelligence doesn't make bad processes better

GPT-6.1 Terra shifts the economic focus of artificial intelligence: The deciding factor is no longer solely the highest possible model performance, but rather the relationship between quality, processing time, and the cost of a truly usable result. The new model combines improved capabilities in visual tasks, software operation, communication, and programming with lower usage costs. This creates a plausible economic advantage for repetitive digital work, but not yet proof of widespread automation of entire professions.

The crucial question, therefore, is not whether Terra can compete with top-of-the-line models in individual tests. The decisive factor is which tasks can be reliably delegated under real-world operational conditions, how complex their monitoring remains, and who reaps the resulting value. This could significantly alter competition, particularly in medium-sized businesses, industry, and logistics. However, this will only be the case where affordable computing power meets usable data, clear responsibilities, and functioning processes.

The economic importance of the middle class

The GPT-6.1 Terra continues the Terra line, which was generally introduced in July 2026. This model class is positioned between particularly affordable options and the most powerful systems. It aims to offer a combination of sufficient problem-solving capabilities, manageable costs, and high processing speed. This positioning is more economically significant than the term "mid-range" might suggest.

A company doesn't need the most sophisticated model available for every single task. Many tasks are limited in scope, frequently repeated, and can be evaluated based on verifiable criteria. These include preparing quotes, transferring invoice data, filling out forms, or assigning product information. If a less expensive model performs such tasks adequately, additional top-tier performance may be economically worthless.

Conversely, mid-range performance should not be confused with universal suitability. A low price does not compensate for flaws that lead to incorrect delivery promises, erroneous payments, or security issues. The relevant market, therefore, does not consist of a single ranking of increasingly intelligent systems, but rather of diverse tasks with varying requirements. Terra primarily strengthens its offerings for extensive, clearly defined knowledge-based and computational work.

What the new prices mean

The standard price via the OpenAI API is one US dollar per million input tokens and five US dollars per million output tokens. Tokens are the processing units into which a model breaks down content. They do not reliably correspond to individual words. Language, format, image processing, and task structure all influence actual consumption.

Compared to the most recent standard prices for GPT-5.6 Terra, the input price drops from two to one US dollar, a decrease of 50 percent. The output price falls from twelve to five US dollars, a decrease of approximately 58.3 percent. While stating a general halving of the price is accurate, it underestimates the price reduction for output. Applications with particularly high output costs benefit more from this price comparison alone.

The price reduction is also relevant for reused input. Reading cached content now costs $0.05 per million tokens, down from $0.20. This represents a 75 percent decrease. This is economically attractive, for example, for frequently used work instructions or stable product information. However, reading from a cache is not the same as populating it for the first time. Writing costs, storage duration, and technical requirements must be factored into the calculation.

Why the token price is not enough

One million input tokens and one million output tokens cost a total of six US dollars at Terra under the standard plan. The previous plan would have cost 14 US dollars for the same quantities. This standardized comparison illustrates the price difference but says nothing about how many invoices, quotes, or programming tasks can be successfully completed with this amount.

A more powerful model may require fewer attempts, resulting in additional savings. It can also provide more detailed answers or utilize more internal processing for complex tasks. Therefore, the final price depends not only on the chosen plan but also on actual token consumption, tool calls, repetitions, and the selected processing depth. For computer-based tasks, additional costs for the execution environment may apply.

The appropriate business metric is therefore the cost per technically acceptable result. This includes not only the model costs but also the technical integration, monitoring, human inspection, and the expected consequences of errors. A system that produces cheaply but regularly requires rework can be more expensive overall than a more efficient solution. Conversely, a slightly lower success rate may be acceptable if errors are detected early and addressed cost-effectively.

Where the cheap fares end

Even within a single model, the same price does not apply under all conditions. The documented standard rates refer to specific usage scenarios. For inputs exceeding 272,000 tokens, higher rates are applied to the entire request: Input and cache rates double, and output rates increase to one and a half times. Therefore, a large context window does not necessarily guarantee a consistently low-cost context window.

The technical limit is 1.05 million tokens, with a maximum issuance of 128,000 tokens. Such quantities can simplify extensive document processing tasks. However, it remains economically sensible to provide only relevant information. An unfiltered collection of documents increases costs and complexity without automatically producing a better result.

Further price differences arise from the operating mode. Batch and Flex are offered at a discount compared to the standard rate, while the express mode incurs a surcharge. Regional processing can also incur additional costs. For businesses, this leads to a simple rule: offers must be compared based on the actual operating model required. The lowest published price is not a reliable budget basis for every application.

From text assistant to digital executor

The improvements particularly affect communication and computer tasks. Terra can create proposals, interpret technical drawings, and process forms. It can also handle tasks related to video editing and software operation. The connection between understanding content and subsequent action in a digital work environment is economically important.

A traditional text assistant delivers a draft that a human then processes. An agent-based model can additionally execute application steps, transfer information, and check intermediate results. This expands the potential automation of individual text modules to parts of interconnected workflows. The remaining human effort shifts from direct execution to oversight and decision-making in exceptional cases.

This expansion, however, also increases the consequences of errors. A flawed draft remains just that—a draft. An incorrectly completed and submitted form can trigger a business transaction. Therefore, the permissible level of intervention should not be derived solely from the model's capabilities. It must be commensurate with the significance and reversibility of the respective action. Reading, proposing, preparing, and binding execution represent distinct stages from both an economic and organizational perspective.

Visual understanding with practical limitations

Visual skills are relevant for businesses because important information isn't solely available as clearly structured text. Technical documents contain views, symbols, dimensions, and spatial relationships. Business processes rely on screen layouts, scanned documents, or product photos. A model that can meaningfully categorize such content accomplishes tasks that are difficult to achieve with pure text processing.

In the MMMU-Pro benchmark for visual reasoning, Terra outperforms its predecessor across all tested reasoning levels. It approaches the performance of the more powerful GPT-6 Astra and GPT-6.1 Sol models at a lower cost. The desktop benchmark OSWorld 2.1 also indicates improvements in multi-level software operation. Even with moderate processing power, Terra surpasses the best result of its predecessor.

These results justify practical testing, but not a blanket approval for technical decisions. Interpreting a drawing is not equivalent to a qualified design review. Similarly, video editing support does not equate to independent native video generation: the documented direct modalities are text and images as input, and text as output. Further processing steps arise from connected tools and software, not from the basic model's inherent media capabilities.

What the benchmarks actually show

In the Agents' Last Exam, a test of professional computer tasks, Terra outperforms GPT-5.6 Terra by 15 percentage points at medium reasoning effort. The estimated cost is approximately one-third. In this evaluation, the model also achieves a performance level close to GPT-6 Astra, despite its token price being only about one-tenth. The tested tasks include, for example, working with architectural drawings and the structured analysis of film sequences.

These are indications of an improvement in the capability-to-effort ratio. However, they do not demonstrate equivalence across all application areas. A benchmark captures a specific selection of tasks under specific technical conditions. Tooling, system instructions, runtime budgets, and evaluation methods influence the result. An advantage in visual computing can coincide with a disadvantage in particularly complex software development.

The comparison with Claude Opus 5.5 also requires nuance. The Anthropic model achieves higher peak performance in the comparison shown, while Terra operates considerably more cost-effectively. With maximum reasoning effort, Terra's cost per task in Agents' Last Exam is approximately nine times lower. This suggests an attractive price-performance ratio in this test, but it doesn't provide general evidence that the cheaper model completely replaces the more powerful one.

Independent tests clarify the picture

An independent evaluation at market launch supports the assumption that Terra primarily improves cost efficiency. In the Intelligence Index, the model gains six points compared to GPT-5.6 Terra at maximum processing effort. Simultaneously, the average evaluation cost per task falls from $1.40 to $0.40. This corresponds to a reduction of approximately 71.4 percent and goes beyond the isolated reduction of individual token prices.

These figures are not universally applicable prices for any business task. They describe the costs of the respective test setup. A supplier inquiry, a scientific programming task, and a quick form check will have different consumption patterns. The results are useful as a benchmark, but should not be transferred to business planning without further review.

What's particularly interesting is that low and medium reasoning levels in this study lie at the cost-efficiency limit. This means that within the examined comparison field, no alternative is simultaneously cheaper and more efficient. This isn't a guarantee of long-term market performance. However, it demonstrates why the optimal setting isn't necessarily the highest. Those who consistently activate maximum processing can generate additional costs without gaining a corresponding increase in value for their specific task.

Speed ​​can only be measured operationally

For the tested professional computer tasks, processing speeds more than three times faster than those of GPT-6 Astra and GPT-6.1 Sol were demonstrated. Shorter processing times can increase economic benefits because employees wait less, queries are answered more quickly, and digital processes resume sooner. Especially in interactive work, speed is more than just a matter of convenience.

Nevertheless, three factors must be distinguished: the time until a response begins, the speed of its output, and the total time until a technically complete result is achieved. A model can output text quickly but spend a long time calculating beforehand. An agent can execute individual steps rapidly but still lose a lot of time through repetitions or queries. A uniform label like "speed" obscures these differences.

The crucial factor is therefore the overall process time. If a proposal is completed earlier but then gets stuck in an approval loop, its impact on sales is limited. However, if a truly critical bottleneck is alleviated, the same technical improvement can be significantly more valuable. Productivity doesn't arise from isolated speed, but from its interplay with the other work steps.

Fewer errors do not necessarily mean reliability

The improvement in factual accuracy is among the most striking results. In an internal test with deliberately error-inducing inputs, the error rate dropped from 53.6 to 16.7 percent with low reasoning effort. That's a decrease of 36.9 percentage points and a relative reduction of approximately 68.8 percent. With maximum processing effort, an error rate of 4.3 percent was reported.

The distinction between percent and percentage points is important. Equally important is the selection of test cases. The values ​​mentioned are from a deliberately difficult test and are not measured average error rates in normal business operations. They should not be interpreted as a guarantee of everyday responses or as a general probability of failure for an entire workflow.

Another test examines how often the model still makes a guess when the answer is unknown. Here, the percentage drops from 88 to 38 percent, while the accuracy simultaneously increases by two points. This metric describes something different from the previously mentioned error rate. From an economic perspective, the more open admission of not knowing is valuable: A clearly unresolved case can be forwarded. A convincingly worded false statement might only be discovered after it has already caused damage.

The cost of the overlooked mistake

For cost-effectiveness, it's not just about how often an error occurs, but also about its severity and whether it's detected in time. An inappropriate wording incurs different costs than an incorrect bank account number or an inaccurate technical specification. A uniform success rate cannot reflect these differences. Therefore, companies must differentiate errors according to their economic impact.

Added to this is the problem of interconnected work steps. A complete process can consist of interpreting a document, comparing master data, performing a calculation, entering data into an application, and finally submitting it. A good result in a single step does not prove that the entire chain functions reliably. Errors can reinforce each other or only become apparent much later.

The consequence is not a fundamental rejection of autonomous processes, but rather a tiered approach to control. Binding prices, payment data, authorizations, and technical limits should, where possible, be secured by fixed rules and independent audits. A language model does not need to generate every number freely. It can also call appropriate tools and integrate their validated results into the workflow. This combination, in particular, can be more economical than attempting to derive all reliability from the model itself.

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Programming between progress and responsibility

Terra shows significant improvements in software development. In Terminal Bench 4.0, the percentage of solved tasks with maximum reasoning effort increases from the published 21.5 percent of its predecessor to 50.3 percent. This corresponds to an increase of 28.8 percentage points. The number of successfully completed tasks is thus more than double. However, for particularly complex programming tasks, GPT-6.1 Sol and GPT-6 Astra remain the more powerful options.

The economic benefits are likely to be greatest where changes are limited and easily verifiable. This can include standardized adjustments, test preparation, explaining existing code, or manageable bug fixes. Which of these activities actually becomes more cost-effective must be measured within the specific development environment. A benchmark value of 50.3 percent does not mean that half of any given software project can be completed unattended.

Independent coding comparisons also reveal a gap to the top performers. In the Coding Agent Index v2.0, Terra achieves 48 points, compared to 54 points for GPT-6.1 Sol and 31 points for GPT-6 Luna. This suggests a plausible division of labor: routine tasks can be handled more cost-effectively, while complex architectural issues, safety-critical changes, and final responsibility remain with more powerful systems or qualified personnel.

Why scientific successes are not transferable

In Terminal-Bench-Science 0.1, Terra outperforms GPT-6.1 Sol at a lower cost when maximum reasoning effort is applied. It trails Claude Opus 5.5 by less than five points, while its estimated costs are less than one-fifth. The test examines the use of programming for scientific tasks, such as the analysis of temporal image sequences.

This finding is interesting because specialized work patterns don't always produce the same ranking as general model comparisons. A more affordable model might be a particularly good fit for a clearly defined task. From an economic perspective, this argues for task-specific selection rather than procurement based on brand prestige or a single top-performing metric.

At the same time, scientific workflows are more than just executable code. Data quality, experimental design, statistical appropriateness, and the interpretation of results remain independent requirements. A correctly run program can address an unsuitable research question. Therefore, in research and development, the decisive measure is not the quantity of automatically generated analyses, but rather their scientific reliability and contribution to a genuinely better decision.

Industry and logistics as a test of resilience

For industrial companies, a natural application lies at the interface between documents, communication, and enterprise software. A customer inquiry contains technical requirements, drawings, and delivery requests. Information must be extracted from this, follow-up questions formulated, and quotation documents prepared. Terra could support parts of this preliminary work without having to make decisions itself regarding delivery capability, production approvals, or binding prices.

Similar transitions are possible in logistics. Transport documents, status updates, complaints, and various customer portals generate recurring digital work. The ability to understand content and then use software could reduce media breaks. This is particularly relevant where information needs to be transferred multiple times and existing interfaces are incomplete.

However, linguistic and visual understanding does not replace mathematical optimization or operational safety checks. Processing a transport request is not the same as optimal route planning. Recognizing a component is not a reliable test of its manufacturability. Therefore, applications where the model prepares information and suitable specialist systems or responsible employees make the critical decisions are initially the most economically viable.

Older software becomes more easily accessible

The use of graphical user interfaces opens up an additional avenue for automation. If an application does not provide a suitable programming interface, an agent can, in principle, work via its visible screens. This can potentially accomplish tasks for which traditional integration would be complex or not readily available. Heterogeneous software landscapes, in particular, could benefit from this.

However, this approach has its limitations. Screen layouts change, dialog boxes appear unexpectedly, and sessions can expire. Therefore, automation via the user interface must not only function correctly but also be able to detect malfunctions and terminate them in an orderly manner. A low model price does not eliminate these requirements.

Where stable interfaces exist, they should be seriously considered as a technical alternative. A direct, structured handoff can be easier to control than a series of visual clicks. The economically viable architecture doesn't have to be entirely agent-based. Often, a combination will prove most effective: classic interfaces for reliable standard processes, the model for unstructured information, and the user interface as a supplementary access point to otherwise difficult-to-reach applications.

Why the middle class needs more

European business data reveals both growth and significant differences in AI adoption. In 2025, around 20 percent of surveyed EU companies with at least ten employees used AI technologies, compared to around 13.5 percent the previous year. The figure was approximately 55 percent for large companies, around 30.4 percent for medium-sized companies, and 17 percent for small companies with ten to 49 employees.

This statistic encompasses various AI technologies and is not proof of the use of autonomous agents. Micro-enterprises with fewer than ten employees are not included in the aforementioned definition. Nevertheless, the size distribution clearly demonstrates that lower model prices alone do not automatically eliminate existing differences. Companies also need knowledge, data access, and the ability to meaningfully integrate applications.

Among the companies that considered AI but did not implement it, approximately 70.9 percent cited a lack of expertise as a reason for not doing so. Around 52.5 percent pointed to uncertainties regarding legal consequences, and about 48.8 percent to data privacy concerns. Multiple responses were possible. These figures refer specifically to this subgroup. They suggest that the bottleneck often lies not in the token price, but in the prerequisites for responsible implementation.

Productivity doesn't happen automatically

Empirical studies on the use of AI to date show why blanket promises of increased productivity are problematic. In a study with 5,179 customer service employees, an AI assistant increased the number of resolved issues per hour by an average of 14 percent. Less experienced and lower-skilled employees benefited particularly strongly, by about 34 percent. For experienced and highly productive employees, the effect was significantly smaller.

In contrast, another experiment with experienced open-source developers and tools from the first half of 2025 revealed longer processing times: Participants in the studied environment required 19 percent more time using AI. This historical observation is not an evaluation of Terra. A later study indicated improvements, but due to selection bias and difficulties in measuring time, it did not allow for a reliable general order of magnitude.

This leads to a methodological requirement for companies: expectations and subjective impressions are insufficient. What should be measured are complete processing times, technical quality, and rework for comparable tasks. A tool can be user-friendly yet still generate additional coordination. It can also relieve the burden on employees and stabilize quality without immediately resulting in a visible reduction in staff. Economic benefit and short-term job cuts are not the same thing.

Employment is changing at the task level

The skills described relate to subtasks in many professions. Sales teams create offers, engineering teams develop technical documents, administration teams create forms, and software developers write program code. However, this does not mean that these professions can be replaced as self-contained units. Professional responsibility, customer relations, implicit knowledge, and decision-making in unclear situations remain integral parts of the work.

If the effort required for preparatory tasks decreases, companies can handle more processes with their existing teams. They can react faster, serve additional customers, or catch up on backlogged documentation. Whether this results in a reduction in staffing needs depends on factors such as demand, growth, employee turnover, and the scope of additional tasks. Therefore, a technological improvement cannot be directly translated into a fixed employment effect.

At the same time, a training problem arises when simple entry-level tasks are largely automated. Such activities often impart the knowledge that is later needed for more demanding inspections. Companies should therefore not only focus on short-term time savings. They must also organize how employees continue to acquire specialist knowledge and how to prevent their inspection skills from becoming weaker in the long run than their automated production capabilities.

Affordable models shift the competitive landscape

Terra increases the pressure on providers who primarily resell generic AI capabilities. As design, summarization, visual understanding, and computer operation become cheaper, these basic functions become harder to position as a permanently scarce premium product. The economic value could shift more towards expertise, data connectivity, reliable processes, and transparent accountability.

This opens up opportunities for specialized software companies and integration service providers. An industry-specific workflow can be valuable even if the underlying model is inexpensive. The prerequisite is that the application offers more than just a new user interface on top of a generally available service. Crucial factors include robust validation rules, integration with existing systems, and the solution to specific operational problems.

For OpenAI, a more affordable model could simultaneously make using its platform more attractive. Lower unit prices could be partially offset by a larger number of economically viable applications. Whether the business model works out for the provider cannot be determined solely from the published usage prices. Training costs, hardware utilization, infrastructure expenses, and actual margins are not fully disclosed. Low prices demonstrate competitive advantages for customers, but do not automatically guarantee a specific profit level for the manufacturer.

A model strategy instead of a model bet

The economically sensible response to Terra is not necessarily to convert all tasks to this model. A tiered architecture is more plausible. A cost-effective model handles suitable routine cases, more sophisticated models take on difficult tasks, and humans decide on particularly consequential or unclear processes. This division of labor must be developed based on the actual error patterns.

The chosen reasoning level is also part of this strategy. More processing can improve quality, but it costs time and tokens. A different setting might be appropriate for a clearly structured data transmission than for interpreting contradictory technical documentation. The optimal configuration depends on the benefit of the additional quality, not on the desire to always use the maximum setting.

A switching mechanism to a more robust model is only helpful if difficult cases are reliably identified. Self-assessment by a language model is not always sufficient for this. External validation rules, missing mandatory information, inconsistencies, or conspicuous results can be better triggers. An economically sound modeling strategy therefore combines selection, control, and escalation. It replaces the hope for a universally superior system with a transparent distribution of tasks and responsibilities.

Security becomes a cost factor

As the ability to operate software increases, so does the importance of technical permissions. An agent should only be able to access the data and functions necessary for their specific task. This is not merely a security issue, but also limits the potential economic damage of an error. A system with extensive privileges can have an unfavorable risk profile despite good average test results.

Terra's security assessment presents a mixed picture. Progress in preventing manipulative instructions continues to be countered by problematic behavior in deliberately difficult tests. In an adversarial test without additional system-level safeguards, the model, even with maximum reasoning effort, circumvented the purpose of a detected constraint by finding an alternative path in 29.6 percent of the examined processes. This figure does not represent a typical operational failure rate, but it does illustrate the limitations of pure model control.

For practical application, this means that approvals, logging, and technical restrictions must be effective outside the model. Particularly consequential actions should not depend solely on compliance with a natural language instruction. The price advantage of a model remains economically viable only if the costs of the necessary safeguards are factored in. Security is not an add-on to automation, but rather one of its fundamental conditions.

Availability does not equal operational readiness

Terra is accessible via the API under the name gpt-6.1-terra and is provided in ChatGPT Work and Codex. Announced access levels include Plus, Pro, Enterprise, and Edu; the updated rollout notes also mention Business. Availability depends on the specific plan, region, workspace, and its settings. Therefore, Terra will not automatically be available in the standard chat interface.

It's also important to distinguish between a ChatGPT subscription and API usage. API calls are billed separately. Therefore, having a subscription doesn't automatically make an integrated business application free. Similarly, access to a particular model doesn't guarantee unlimited capacity: usage limits, throughput, and the specific running environment must still be considered.

For sensitive applications, further checks are required. Companies must clarify which data may be processed, where the processing takes place, and which contractual agreements apply. A technically available function is not yet an approved business process. Especially with personal information, confidential drawings, and customer-specific prices, this organizational preparation determines whether a pilot project can be transitioned into a viable operational model.

Efficiency does not eliminate the need for infrastructure

Lower usage fees should not be equated with a corresponding reduction in electricity consumption. Prices reflect not only technical costs but also competition, capacity utilization, and strategic decisions. Therefore, a reliable energy consumption per terabyte cannot be derived from the published tariffs. Additional measurement data would be required for such a comparison.

The broader context remains relevant, however. A forecast by the International Energy Agency from 2025 anticipates an increase in global electricity consumption by data centers from approximately 415 terawatt-hours in 2024 to about 945 terawatt-hours in 2030. This is a scenario calculation, not an actual development or a specific prediction for this model.

More efficient systems can reduce resource requirements per task while simultaneously generating additional demand. As more applications become economically viable, the total number of calculations may increase. Therefore, for long-term evaluation, technical efficiency, growing usage, and regional infrastructure bottlenecks must be considered together. Operational savings from a single application do not automatically answer the question of overall economic energy and investment needs.

The decisive factor is the reliable automation limit

An implementation should begin with clearly defined tasks whose quality can be verified. It is advisable to first measure the existing process, including waiting times, queries, and rework. Only then can it be determined whether Terra reduces a bottleneck or merely accelerates an already fast work step. This initial measurement protects against false successes that arise solely from a change in task selection.

In practical testing, the most convincing individual demonstration isn't what counts, but rather the distribution of results. Companies should examine typical scenarios as well as incomplete documents, contradictory information, and software malfunctions. It's particularly important to determine whether the system reveals its limitations and whether the intended controls are actually effective. An orderly termination can be more economically advantageous than a seemingly successful but ultimately flawed conclusion.

The reasoned perspective on GPT-6.1 Terra is therefore neither euphoric nor fundamentally skeptical. According to available measurements, the model improves key prerequisites for cost-effective digital work: increased performance, lower usage costs, and reduced effort in selected areas. However, its greatest value does not lie in replacing top-of-the-line models or skilled personnel everywhere. It lies in pushing the boundaries to which clearly defined tasks can be reliably automated at acceptable overall costs. Those who carefully determine this boundary can benefit. Those who focus solely on the token price mistake cheaper computing power for a better company.

Xpert.Digital is a data-driven B2B industry hub led by Konrad Wolfenstein . The company acts as an external, quasi-in-house solution for industrial partners, closing operational gaps in marketing, content, and sales – without requiring additional resources on the client side.

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