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The headline is overstated: in January 2025, former Intel CEO Pat Gelsinger said his startup, Gloo, had decided not to adopt and pay for OpenAI’s o1 model for a particular product, Kallm. Gloo engineers were testing DeepSeek-R1, and Gelsinger said the plan was to rebuild Kallm around an open-model foundation. That is a product decision—not evidence that Gelsinger or Gloo rejected OpenAI across the board.
What Gelsinger said—and what the headline leaves out
TechCrunch reported Gelsinger’s comments on January 27, 2025. Gelsinger, then chairman of Gloo, said its engineers were already running DeepSeek-R1 and that Gloo had decided not to adopt and pay for OpenAI’s o1 for Kallm. He described a plan to rebuild Kallm “from scratch” using Gloo’s own open-source foundational model. That was a reported intention, not confirmation that the rebuild was completed. TechCrunch’s account does not establish a company-wide ban on OpenAI or a permanent personal break by Gelsinger.
It also does not say Gloo simply replaced OpenAI’s API with DeepSeek’s hosted API. Running R1 and building around open weights could mean local deployment, adaptation, or another architecture; the account does not specify which approach Gloo ultimately used.
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Who Gelsinger was in this story
Gelsinger was Intel’s CEO for about four years and left the company in December 2024. His relevance here is as a veteran semiconductor executive and Gloo chairman—not as a current Intel executive or an independent authority on AI benchmark testing. TechCrunch described Gloo as a messaging and engagement platform for churches.
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What DeepSeek-R1 offered
DeepSeek released R1 in January 2025 as a reasoning-focused large language model. Reasoning models spend additional computation working through some problems before returning an answer. DeepSeek offered the model through an API and released weights, along with smaller distilled variants. Its release documentation identifies the API model as deepseek-reasoner; the R1 research paper describes performance comparable to OpenAI-o1-1217 on certain tasks.
The full R1 model was reported at approximately 671 billion parameters. Distilled versions ranged from approximately 1.5 billion to 70 billion parameters, with very different hardware demands. The smaller variants make local experimentation more plausible on constrained systems; that does not mean the full model is laptop-sized. R1’s weights were reported as available under the MIT license, but an open-weight model is not the same thing as a managed hosted service: buyers still need to assess the license, deployment, and operational responsibilities.
Why the model drew attention
Selected benchmark results, not a universal win
DeepSeek reported that R1 matched or exceeded OpenAI o1 on selected benchmarks, including AIME, MATH-500, and SWE-bench Verified. These were company-reported results on particular tests, not proof that R1 was better for every task or production environment. Benchmark scores do not settle questions about reliability, latency, long-context performance, tool use, safety, support, or uptime. Contemporary coverage of the claims is best read with that scope in mind.
Lower reported API prices
At launch, contemporary reporting described R1’s API as roughly 90%–95% cheaper than OpenAI o1’s API. That is a historical comparison, not a current price quote or a guaranteed saving for a particular workload. Actual costs depend on input and output mix, caching, reasoning-token use, traffic, and whether the model is self-hosted. Current prices should be checked directly with providers before a purchasing decision.
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Open weights and deployment choice
Access to weights offered developers options beyond a closed API: they could investigate local or private deployment, adapt a model, or reduce reliance on one provider. Those options require infrastructure and expertise. A downloadable model shifts work to the operator, including hosting, security, monitoring, updates, abuse prevention, and compliance review.
A challenge to assumptions about AI economics
Gelsinger argued that cheaper computation could expand AI use rather than simply reduce incumbents’ revenue. He pointed to three lessons: lower computing costs can grow demand, constraints can prompt ingenuity, and open ecosystems can speed progress. He also envisioned capable AI reaching more devices, including phones, vehicles, wearables, hearing aids, and embedded systems. These are Gelsinger’s interpretations of the moment, not established outcomes.
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What the cost figures do—and do not—show
Gelsinger told TechCrunch that available evidence suggested DeepSeek’s training was 10–50 times cheaper than OpenAI o1’s. That is his estimate, not an independently verified, like-for-like accounting comparison. A frequently cited figure of approximately $5.5 million referred to a reported DeepSeek training run under specified conditions. It does not represent the total cost of building DeepSeek, its prior research and infrastructure, its data work, or every model in its product family. Contemporary reporting on the figure provides context, but it does not establish that DeepSeek built an equivalent frontier-AI company for $5.5 million.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsComparisons of training costs can also omit earlier experiments, hardware access, engineering, and deployment. For a buyer, the relevant number is usually the cost of delivering the required service at acceptable quality—not a headline estimate for one training run.
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Risks a benchmark or price comparison cannot answer
- Hardware and cost transparency: Observers questioned whether published hardware and cost details captured all relevant training and infrastructure. TechCrunch reported suspicions that DeepSeek may have used more advanced hardware than disclosed, but the cited reporting did not establish those claims as fact.
- Privacy and data governance: Data handling, processing location, retention, and contractual protections matter independently of model quality. DeepSeek’s Chinese ownership is a consideration for some organizations’ procurement and risk reviews; it is not itself proof of a particular security outcome.
- Content behavior and compliance: Moderation and responses to politically sensitive or regulated topics can make a model unsuitable for a market or use case even when its technical results are strong.
- Operational burden: Self-hosting brings responsibility for infrastructure, security, updates, monitoring, abuse controls, and incident response. Open weights do not supply enterprise support or a service-level commitment by themselves.
How a company should evaluate a similar switch
Gloo’s reported choice was specific to Kallm. Another organization should test its own workloads and compare the complete operating model rather than selecting from a headline benchmark or launch price.
- Test the actual tasks. Evaluate the exact prompts, tools, and edge cases the product will use. Include accuracy, consistency, latency, and failure handling.
- Calculate total cost. Include API usage or, for self-hosting, GPUs, power, storage, networking, engineering, monitoring, and support. Model realistic usage patterns and peaks.
- Choose the deployment boundary. Compare a hosted API, private cloud, on-premises hosting, and a hybrid design against data-residency and access-control requirements.
- Review license and contracts. Confirm rights and obligations for commercial use, redistribution, and fine-tuning, and review provider terms for data processing and service commitments.
- Test safety and governance. Assess moderation, sensitive-content behavior, logging, privacy, and compliance requirements for the jurisdictions and users involved.
- Plan for ownership over time. Decide who handles model updates, security patches, regression testing, incidents, and fallback if the model or provider becomes unavailable.
- Check capacity against model size. A smaller distilled model and the full 671-billion-parameter R1 are not interchangeable infrastructure choices. Confirm hardware and serving capacity before committing.
A hosted proprietary API can reduce operational work and provide vendor-managed infrastructure, but it brings usage fees, provider dependence, and less control over the underlying model. Self-hosted open weights can offer deployment control and customization, but require capital and ongoing operational capability. Neither route is inherently cheaper or safer for every organization.
Where to verify provider details
For model and availability details, consult DeepSeek’s official R1 release documentation and OpenAI’s o1 model documentation. The pages identify provider offerings; they do not establish a current price comparison or determine which service fits a particular buyer.
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