Aleph Alpha and Kolibri-1: Germany's AI comeback between efficiency, dependence and a new power bloc

German and English: Kolibri-1 as an economic differentiator

From chatbot to process automation: The development of Kolibri-1

With the launch of Kolibri-1, Aleph Alpha presents an innovative AI model that is not only tailored to the needs of German companies but could also set international standards. Unlike other large language models, which are often dominated by US or Chinese companies, Kolibri-1 aims to provide a controllable and efficient AI solution, particularly suitable for sensitive data and complex documents. Its focus on German and English, as well as its integration into existing business processes, could be crucial for many organizations, especially in a market increasingly seeking solutions that are both high-performing and data-secure. In this context, the question arises whether Kolibri-1 is merely a technological experiment or the cornerstone of a new chapter in European AI development. The challenges and opportunities presented by this model are explored in detail in the following sections.

78 billion parameters – and yet in the end it's not the model that decides, but the infrastructure

A model as an economic policy signal

With Kolibri-1, Aleph Alpha returns to a discipline from which the Heidelberg-based company seemed to have largely withdrawn: the development of large, generally applicable language models. However, the new system is not simply an attempt to replicate American or Chinese models. It is designed as a specialized German-English foundation for businesses, public administrations, and regulated industries. Thus, Kolibri-1 is targeting less the mass market for free chatbots and more a commercially attractive, yet less accessible, market: controllable AI for sensitive data, lengthy documents, internal knowledge bases, industrial processes, and agent-based workflows.

The publication should therefore not be viewed solely as a technical event. It touches upon three key economic questions of the European AI strategy. First, it concerns whether European providers can develop technologically independent basic models without becoming permanently dependent on the major platforms from the US and China. Second, it raises the question of whether a high-performance, openly available model also generates a viable business model. Third, the economic benefit is not determined by the number of parameters, but by whether companies can operate the model securely at reasonable costs, integrate it into existing processes, and keep it continuously updated.

Kolibri-1 is launching in a favorable market environment. German spending on artificial intelligence is expected to rise to €28.7 billion in 2026, representing growth of approximately 48 percent compared to the previous year. Generative AI already accounts for roughly €11.5 billion, or about 40 percent of the total market. At the same time, its practical application in German companies is still significantly less widespread than the high investment sums would suggest. Only a fraction of companies provide their employees with direct access to generative AI applications. Smaller companies, in particular, are hesitant due to costs, data privacy concerns, a lack of skilled workers, and unclear integration pathways.

This creates a market gap. Companies want to use powerful AI, but they don't want to transfer sensitive information to external platforms without verification. They need transparent operating models, controllable data flows, German-language quality, and legally sound deployment concepts. This is precisely where Kolibri-1 comes in. The model is therefore less of an attack on the most popular chatbots and more of an offering to organizations that want to build their own AI infrastructure for regulatory, strategic, or economic reasons.

Economical activation instead of small architecture

Kolibri-1 comprises a total of approximately 78.1 billion parameters. This number initially sounds like a very large and correspondingly expensive model. In fact, however, the system operates with a mixture-of-experts architecture. This means that not all parameters are activated at every processing step. Instead, a router decides which specialized subnetworks are responsible for the respective token. Of the 78.1 billion parameters, only around 3.46 billion are active per token. This corresponds to approximately 4.4 percent of the total capacity.

The architecture consists of 50 transformer layers. Each layer has 384 specialized experts. One common expert is always active, while six others are activated depending on the content of the respective token. The underlying concept is economically attractive: The model possesses the knowledge and specialization capacity of a very large system, yet requires only a fraction of the usual computational operations for a single calculation step. This allows for fundamental improvements in throughput and operating costs compared to a dense model of similar overall size.

This efficiency, however, should not be confused with low memory requirements. All model weights must remain available in memory because the router can activate different experts depending on the input. The FP8 variant requires approximately 78 gigabytes of graphics memory for the weights alone. Additional memory is needed for the context (the KV cache), temporary calculations, parallelization, and the runtime environment. Therefore, the minimum configuration recommended is two A100 GPUs with 80 gigabytes each, two H100 SXM5 GPUs, or a single H200, B200, or B300 GPU. For stable production operation, more powerful or redundant configurations are advisable.

This highlights the central tension inherent in Kolibri-1. The computational effort per token is comparatively low, but access to the necessary hardware remains capital-intensive. For a company with existing GPU infrastructure, the model can be economically attractive. However, for a medium-sized enterprise without its own AI cluster, direct hosting is rarely the most cost-effective solution. Here, managed hosting services, sovereign cloud services, or industry-specific platforms are likely to play the decisive role.

The economic advantage of an expert architecture is therefore not automatic. It depends on high utilization. An expensive GPU that only processes occasional requests remains uneconomical even with an efficient model. The more users, document processes, and agent-based applications are bundled on a shared infrastructure, the better the fixed costs can be distributed. For data center operators, cloud providers, and large corporations, Kolibri-1 is therefore significantly more attractive than for individual small businesses with low utilization.

One million tokens is not a free pass

A key feature of Kolibri-1 is its ability to process very long inputs. Initial training was conducted with sequences of 16,384 tokens. In a subsequent training phase, the length was increased to 65,536 tokens. The final long-context phase operated with 262,144 tokens. This length is considered the model's native context. Technically, the system can be extended beyond this without subsequent positional scaling. The quality and availability were verified up to 1,048,576 tokens.

Depending on language, format, and tokenization, one million tokens can represent several thousand pages of text. This opens up applications for companies that are only possible to a limited extent with traditional chatbots. These include the analysis of extensive contracts, technical manuals, maintenance documentation, legal collections, research archives, supplier documents, and internal knowledge bases. Complex due diligence reviews, tender analyses, compliance comparisons, and the evaluation of lengthy case files are also made easier in principle.

However, what is economically relevant is not just the maximum context length, but the cost per piece of meaningfully processed information. A very long context increases the memory consumption of the computational logic (CLU) cache, lengthens response times, and can significantly reduce throughput. Furthermore, a large context window does not guarantee that the model will reliably find all relevant information, weight it correctly, and process it consistently. The longer the input, the greater the risk that crucial details will be lost among irrelevant passages.

Therefore, the economically viable range of use is often below the technical maximum. For complex tasks and latency-critical applications, a limit of no more than 262,144 tokens is recommended. In many enterprise applications, a significantly smaller context, combined with retrieval-augmented generation, remains more efficient. In this approach, not all available documents are loaded into the model simultaneously. A search system first selects the presumably relevant sections and passes only these to the language model.

Kolibri-1 does not replace RAG. Rather, its large context window expands the scope for hybrid architectures. For example, a company can first semantically search thousands of documents, then load several hundred relevant pages into the context, and have the model generate a reasoned analysis from this. The added value arises from the combination of search, document understanding, source attribution, structured output, and human review. Conversely, indiscriminately pushing entire data spaces into the model risks high costs with limited insights gained.

German is becoming an economic differentiator

The true strategic value of Kolibri-1 lies in its consistent focus on German and English. Approximately 62.5 percent of the basic training material consists of English content, about 23.9 percent of German texts, and roughly 13.6 percent of program code. In later training phases, instruction data, reasoning tasks, agent-based processes, subject-matter content, and long documents were additionally integrated. The pre-training phase comprised a total of 20 trillion tokens. A further 3.44 trillion tokens were used in the middle training phase, and 201 billion tokens were invested in expanding the context window.

The German component is significant compared to other countries. While many global models can generate fluent German, they were predominantly trained on English data. This has a particular impact on legal nuances, administrative language, compound technical terms, regional expressions, and industry-specific text types. Kolibri-1 uses its own tokenizer with 128,000 entries, which takes the morphology and compound formation of the German language more fully into account. This allows German text to be processed more compactly and efficiently.

This technical feature has direct economic implications. Fewer tokens per German document can reduce storage requirements, processing time, and therefore costs. However, the quality in specialized applications is even more important. Minor linguistic weaknesses are often tolerable in a customer service chat. In contrast, imprecise terminology in a contract analysis, a technical security directive, or an administrative process can lead to significant additional costs.

Germany possesses vast repositories of valuable but difficult-to-access technical documents. Industrial companies have archives of design specifications, maintenance reports, quality notifications, standards, parts list comments, and supplier correspondence that have grown over decades. Public authorities manage extensive files, regulations, official notices, and procedural documentation. Insurance companies, banks, energy providers, and healthcare organizations work with highly regulated text repositories. A model that better captures this content linguistically and structurally can generate productivity gains where general chatbots reach their limits.

The bilingual focus is also a limitation. International corporations often require unified systems for dozens of languages. In such cases, a broadly multilingual model can be more economically advantageous, despite weaker German specialization. Kolibri-1 is therefore particularly compelling where German and English constitute the majority of value-adding communication. The deliberate limitation to two languages ​​is not a universal solution, but rather a strategic focus on a clearly defined market.

From chatbot to digital process worker

Kolibri-1 supports an explicit reasoning mode. Users or applications can set the reasoning effort to low, medium, or high, or disable the additional reasoning entirely. This gradation is more important for enterprise use than it initially appears. Not every task requires lengthy internal processing. Simple classification, quick extraction, or text rewording should be done quickly and cost-effectively. In contrast, complex technical analysis or multi-stage planning can benefit from a higher level of reasoning.

This allows computing power to be controlled based on the economic value of a task. A company can execute routine processes with low cognitive effort and only allocate additional computing time for difficult cases. This is similar to risk-based resource management: the greater the potential consequences of an incorrect answer, the higher the effort allowed for verification, retrieval, and reasoning. However, this requires that the application selects this level of complexity appropriately and monitors the results.

Even more significant is its ability to perform tool calling. The model can generate structured function calls, retrieve information via programming interfaces, access search systems, execute code, and output data in predefined formats. This transforms a simple text generator into a building block for agent-based workflows. For example, an assistant can understand a query, search for relevant documents, retrieve data from an ERP system, perform calculations, and create a draft decision template.

The economic leverage lies not in automated text generation, but in combining previously separate process steps. When a service technician no longer has to search multiple systems for part numbers, maintenance histories, and contract terms, processing time decreases. When a purchasing department automatically structures supplier documents, identifies risks, and prepares comparison tables, capacity increases without a proportional increase in staff. When a government agency extracts file contents and creates standardized drafts, specialists can dedicate more time to legal assessment and citizen engagement.

However, fully autonomous decision-making cannot be derived from this. Kolibri-1 is designed for systems in which humans review expenditures before taking action. In decision-making processes, the model should gather evidence, formulate options, and highlight contradictions, but not make binding decisions without oversight. This applies particularly to personnel, lending, medicine, public services, security issues, and other areas with significant individual or societal consequences.

Openly available weights alter bargaining power

Kolibri-1 was released under the Apache 2.0 license. Companies can download the weights, run them on their own infrastructure, customize them, and, in principle, use them commercially. Compared to proprietary programming interfaces, this reduces dependence on a single vendor. Pricing, terms of service, or product strategies of an external platform operator can change a company's own system less abruptly when the model operates within the company's own sphere of responsibility.

This openness strengthens customers' negotiating position with cloud and software providers. A company can compare different hosting partners, switch infrastructure, or run particularly sensitive applications entirely in-house. Industry-specific customizations are also possible. Consulting firms, integrators, and software companies can develop their own solutions based on the model without having to pay a proprietary model fee for each request.

Open Weight, however, does not mean that the entire system is open source in the strictest sense. Model weights and configuration files are available. Training data, complete training code, architecture development, and all production tools are not automatically disclosed. The published license grants broad usage rights to the provided components but does not permit a complete reproduction of the development process.

Even free downloadable weights don't eliminate operating costs. Companies have to finance hardware, energy, data center space, monitoring, security, updates, and qualified personnel. Added to this are expenses for data preparation, vector databases, access controls, evaluation, logging, and application protection. In many cases, the model license is only a small part of the total cost.

The economic decision, therefore, is not a choice between a free model and a paid API. It's a choice between fixed costs and variable costs. Proprietary APIs typically charge a price per processed token or per request. In-house hosting incurs high initial and setup costs, but can become more cost-effective with large and consistent usage. Companies with fluctuating or low volumes often benefit from cloud billing based on usage. Organizations with consistently high utilization, strict data requirements, or strategic adaptation needs can gain advantages from in-house hosting.

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The real calculation begins after the download

A sound economic analysis for Kolibri-1 must extend far beyond the GPU purchase. On the capital side, this includes procurement, financing, network technology, storage systems, cooling, and potentially necessary structural modifications. On the operational side, costs arise for electricity, maintenance, personnel, software operation, and reliability. Redundant systems are often required for production applications. A single server might suffice for testing, but it does not constitute a reliable enterprise service.

Utilization is the most important cost factor. An internally operated model can theoretically achieve low marginal costs per additional request. In practice, however, the infrastructure must also be available during periods of low demand. If it is only used at ten or twenty percent capacity, the fixed costs are spread across a small number of tokens. A specialized hosting provider can aggregate the loads of different customers and thus operate much more efficiently. This speaks in favor of sovereign managed AI offerings, where data storage, model operation, and governance are combined in a controlled European infrastructure.

Long-term contexts also alter the cost structure. Processing a large document ties up storage and processing time for an extended period. When multiple users simultaneously submit hundreds of thousands of tokens, the number of requests that can be handled in parallel decreases. Therefore, companies must model not only the average request but also peak loads, response time requirements, and priorities. A system for nightly batch processing can operate significantly more cost-effectively with the same hardware than an interactive assistant with guaranteed response times.

Adapting to company data incurs additional costs. Often, full fine-tuning isn't necessary. A good RAG system, clean metadata, and a robust authorization concept often deliver greater benefits. Fine-tuning can be useful when recurring formats, business languages, or workflows need to be reliably learned. However, it increases the effort required for data quality, testing, versioning, and re-verification after model updates.

The biggest hidden cost factor is quality assurance. A language model can produce convincing results and still be wrong. Therefore, productive systems require business-related test cases, defined error tolerances, and continuous evaluation. For every critical application, companies must measure how often the model finds correct information, when it generates unfounded conclusions, whether it adheres to access rights, and how it reacts to manipulated documents. Without these controls, a low token cost is economically worthless because even a single error can cause significant damage.

950 megawatt hours and the question of efficiency

The energy consumption for pre-training, mid-training, and long-context phases is estimated at approximately 950 megawatt-hours. This figure includes the power consumption of the compute nodes and the data center overhead. It does not include, among other things, supervised fine-tuning, reinforcement learning, auxiliary models, test series, or idle and peak states. The actual total energy consumption of the development program is therefore higher than the published figure.

The pre-training phase ran for 21 days on 768 Nvidia B200 GPUs distributed across 96 HGX systems. This resulted in approximately 392,000 GPU hours. The subsequent medium training phase lasted about five days and required roughly 90,000 GPU hours. The long-term context phase added another 10,000 GPU hours. The reported computational effort is 6.4 x 10^23 floating-point operations.

The figure of 950 megawatt-hours seems high, but it must be put into economic terms. Training is a one-time investment in a reusable digital asset. The crucial factor is how intensively the model is subsequently used and what productive effects it generates. If a model is used in thousands of applications over several years, the training effort is distributed across a large amount of economic output. Conversely, if it remains a technological demonstration project with low usage, the resource productivity is weak.

For the environmental impact assessment, ongoing operation often becomes more significant in the long run than a one-time training session. Every query consumes energy, and large context windows increase this demand. At the same time, an efficient expert model may require fewer computations per token than a dense model with comparable total capacity. The crucial comparison, therefore, is not high absolute consumption versus no consumption, but rather energy per usable result.

Europe has a strategic incentive for efficiency. High electricity prices and limited data center capacity make wasteful architectures particularly expensive. Models like Kolibri-1, which only activate a small subset of their parameters, fit a European strategy of combining technological performance with resource productivity. However, this advantage is partially offset by the high storage requirements and the necessary high-performance hardware.

Benchmarks show strength, but no clear winner

The published comparative data positions Kolibri-1 as a high-performing model in its size and efficiency class. It achieves an overall average of 75.5 points in English and 70.8 points in German. Its performance is particularly strong in mathematics and multi-stage reasoning. It achieves 96.0 percent in the English AIME-2026 test and 90.0 percent in the German version. In the scientifically demanding GPQA Diamond test, the model achieves 84.3 percent in English and 81.3 percent in German.

Kolibri-1 also demonstrates competitive results in programming tasks, agent-based processes, and industrial RAG scenarios. Particularly noteworthy are its high scores in areas such as automotive supply, semiconductor technology, the German public sector, and industrial drive technology. This supports its positioning as a model for knowledge-intensive business processes. Equally significant is the improvement compared to a previous model baseline: Post-training has considerably strengthened reasoning, tool usage, and instruction adherence.

Nevertheless, Kolibri-1 is not the leader in every discipline. Denser and larger competing models achieve better results in several knowledge, code, and long-context tests. Other expert models also outperform in individual tool-calling or retrieval tasks. The German results are strong, but not consistently superior. Therefore, a clear economic advantage cannot be derived solely from a benchmark table.

Benchmarks only reflect defined test conditions. In real-world companies, document quality, permissions, format diversity, technical jargon, and the consequences of errors are significantly more complex. A model might perform well in a standardized test but still fail due to poorly scanned PDFs, conflicting legacy data, or internal abbreviations. Conversely, a model with a slightly lower average score might be superior in a well-configured enterprise application.

For procurement and architecture decisions, in-house testing is therefore essential. Companies should use representative documents, challenging edge cases, and real-world process data. Measurements must encompass not only response accuracy but also cost, latency, stability, source reference, failure behavior, and maintainability. Kolibri-1, with its open weights, is well-suited for such controlled evaluations. This is a significant advantage over systems whose internal versions and operating conditions can change without customer intervention.

Security is a systemic task

The training of Kolibri-1 includes measures against malicious content, personal data, and hallucinations. Personally identifiable information was partially removed or replaced during pre-training. For supervised fine-tuning, data was added to defend against attacks on personal information. Furthermore, the model was trained to refrain from responding when faced with missing or contradictory information.

These measures reduce risks, but do not eliminate them. Language models can generate false information, adopt political or social biases, and react to manipulated input. Indirect prompt injection attacks are particularly problematic. In these attacks, a retrieved document contains hidden instructions designed to trick the model into performing unwanted actions. The more extensively tool calling and automated processes are used, the greater the potential damage from such an attack.

Secure enterprise systems therefore require multiple layers of protection. Access must be granted according to the principle of least privilege. Tool calls must be validated, sensitive actions require approval, and output must be logged. The model alone must not decide which data it is allowed to see or which transactions it is allowed to execute. Its permissions must be technically limited outside of the model.

The extended context also creates new risks. Large volumes of documents can contain confidential information, personal data, or conflicting instructions. Anyone managing entire data rooms must ensure that users only receive information they are authorized to access. A central language model across different departments without consistent access separation can unintentionally break down existing information silos.

Therefore, deployment in regulated industries requires more than a model developed in Europe. It demands documented risk assessments, data classification, human oversight, technical protocols, and clear lines of responsibility. Sovereignty doesn't simply mean that data resides in a specific data center. It means that an organization controls the entire technical and legal decision-making landscape.

The transatlantic power bloc behind the model

The release of Kolibri-1 coincides with a period of profound corporate restructuring. Aleph Alpha and the Canadian AI company Cohere have entered into a binding agreement to merge. The transaction is still subject to final regulatory approvals in early October 2026. The combined company will operate globally under the Cohere brand, with headquarters in Toronto and Berlin. The Heidelberg site will be retained as a research center.

This constellation changes the classification of Kolibri-1. Although the model was developed in Germany, its economic future is increasingly being decided within a transatlantic organization. The merger combines Aleph Alpha's research, German-language expertise, and access to the public sector with Cohere's international reach, corporate products, and sales capacity. This can accelerate commercialization but diminishes the notion of a completely independent German AI champion.

Politically, the partnership is portrayed as an alternative from Germany and Canada to the dominant American platform companies. This description is understandable, but only partially sufficient. While Canada is a close partner of Europe, it is subject to different legal, capital market, and security structures. Digital sovereignty does not automatically arise from the nationality of the companies involved. Crucial factors include ownership structures, governance, data access, infrastructure control, and the ability of European customers to switch providers and locations.

The Schwarz Group plays a key role in this. It was already a significant investor in Aleph Alpha and is investing further in the combined company. Through Schwarz Digits and the STACKIT cloud platform, it will provide computing capacity for sovereign AI offerings. This creates a more vertically integrated structure encompassing model development, enterprise software, cloud infrastructure, and data center capacity.

From an economic perspective, this consolidation makes sense. A single model alone does not generate market power. Value creation arises from sales, integration, hosting, data platforms, and long-term customer contracts. The transatlantic alliance can cover these levels better than Aleph Alpha alone. At the same time, this increases dependence on a few large partners. Should STACKIT become the preferred technical foundation, the openness of the model weights could be complemented in practice by a strong commitment to a specific operating ecosystem.

Europe's sovereignty needs scaling, not symbolism

Europe is investing heavily in AI infrastructure. The network of European AI Factories is growing, and billions have been committed to AI Factories and related structures. In addition, several AI Gigafactories are planned. The goal is to provide European companies, research institutions, and public authorities with access to high-performance computing capacity. This infrastructure policy is necessary because modern models cannot be developed or operated economically without large GPU clusters.

However, Kolibri-1 demonstrates that hardware alone is not enough. Europe needs a complete industrial ecosystem of models, data centers, data rooms, integrators, specialized software, security services, and skilled users. Public investment in computing power is only effective if it leads to marketable applications. Otherwise, Europe subsidizes expensive infrastructure while the profitable software and platform businesses remain outside the continent.

Demand is fundamentally present. A large majority of German companies prefer AI solutions from Germany. However, in actual usage, American services clearly dominate. This discrepancy is not due to a lack of interest in sovereignty, but rather to convenience, product maturity, ecosystems, and existing contracts. Companies rarely choose a model based on its origin. They select a solution if it is quick to integrate, reliable, transparently priced, and organizationally manageable.

Kolibri-1 must therefore offer more than just a promise of European origin. It needs competitive tools, stable hosting options, good documentation, partner networks, and measurable advantages in German business processes. The Apache 2.0 approach facilitates the development of such an ecosystem. Software companies and research groups can test the model, adapt it, and integrate it into their own products. This increases the likelihood that innovation will not be confined to a single vendor.

At the same time, there is a risk that open weights will be exploited by global platforms without a corresponding share of the added value remaining in Germany. Openness is therefore no substitute for a sound business strategy. Aleph Alpha and its partners must sell services, secure operating models, industry-specific solutions, and institutional trust. The model serves as a technological foundation and a gateway, not as the sole source of revenue.

A realistic assessment of Hummingbird-1

Kolibri-1 is neither proof of complete European AI independence nor an insignificant niche model. Its importance lies between these extremes. Technically, it combines large overall capacity with a comparatively low active computational load, an unusually strong German training component, very long contexts, reasoning, and tool calling. The open license creates genuine scope for action for companies, integrators, and public institutions.

Economically, this model is particularly attractive where sensitive data, German technical terminology, and high usage volumes converge. Large industrial companies, government agencies, banks, insurance companies, energy suppliers, research organizations, and specialized software providers are among the obvious target groups. For small businesses without their own infrastructure, direct hosting is usually less practical. They will more likely use Kolibri-1 via managed services, industry-specific solutions, or sovereign cloud offerings.

The greatest advantage lies not in 78 billion parameters, but in the ability to combine control and adaptability with competitive performance. The greatest disadvantage is that this control must be paid for and managed organizationally. Whoever operates the model themselves assumes responsibility for infrastructure, security, evaluation, and updates. This is strategic freedom, but not freedom without cost.

The partnership with Cohere and Schwarz Digits could transform Kolibri-1 into an internationally marketable business product. It could bring together research, capital, sales, and computing power. At the same time, it necessitates a sober reassessment of the concept of digital sovereignty. A transatlantic corporation with German research and European cloud infrastructure could be a viable alternative to the major US platforms. However, it is not automatically a purely German or entirely European-controlled system.

The clear outlook is therefore this: Kolibri-1 is a serious building block for a European AI economy, but it's not a sure thing. Its success will not be determined by benchmark tables, but by data centers, procurement budgets, and concrete business processes. If the German language advantage, the open license, and the efficient expert architecture can be translated into robust applications, the model can capture a commercially significant market. If hosting, integration, and distribution remain too expensive or too complex, technological quality alone will not suffice.

Kolibri-1 thus illustrates both Europe's opportunity and its problem. The continent can develop powerful AI, has demanding industrial customers, and is expanding its infrastructure. What is often lacking is the rapid scaling from research model to widely adopted standard product. The hummingbird may appear light and use only a small portion of its wing power at any one time. But for it to fly economically, it needs a solid foundation of capital, energy, computing power, trust, and distribution.

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