DeepSeek-R1: Why This Open-Source AI Model Matters
New AI models emerge almost weekly, making it hard to distinguish between the significant improvements and the minor updates. DeepSeek-R1, however, represents a clear exception.
While its performance matches or slightly exceeds leading proprietary models (like OpenAI’s o1) in many tasks, there are three reasons why this model is important:
Cost Efficiency: Trained for only 5-10% of the cost of comparable models
Open Accessibility: Fully open-source under an MIT licence
Technical Innovation: Novel methods like self-taught reasoning with task-focussed processing
What makes this model so interesting isn’t just its performance but how it’s achieved: its open-source framework and over 90% cost reduction put pressure on closed systems to innovate while enabling businesses to deploy advanced AI affordably.
In summary: its combination of efficiency, transparency, and adaptably set a new benchmark for the industry.
Competitive Performance Without the Premium Price
Independent benchmarks show DeepSeek-R1 performs comparably to closed models in a number of domains: this challenges the assumption that open-source AI lags behind proprietary systems:
Benchmark performance vs state-of-the-art models from OpenAI, Claude, etc. from DeepSeek publication
While it’s marginally behind in general knowledge (e.g., MMLU: 90.8% vs. 91.8%), it has clear advantages in technical tasks: making it particularly suited for software engineering, financial modelling, and scientific research.
Open-Source Design
Closed models require costly API subscriptions, whereas DeepSeek-R1’s MIT licence allows for:
Full customisation: Modify the model for niche applications (e.g., healthcare,, legal contract analysis, etc).
Local deployment: Smaller variants (1.5B–70B parameters) run on consumer-grade GPUs (avoiding cloud fees). In a previous article, I discussed the growing importance of Small Language Models, and some of these models fit neatly into that category).
Transparency: Independent audits of model weights to address bias or safety concerns.
Novel Methods
DeepSeek’s cost and efficiency advantages come within three main areas:
Reinforcement Learning First
Self-taught reasoning: Learns through trial and error problem solving rather than expensive human feedback
Discovery phase: Explores new strategies (e.g., it will attempt to verify its own answers)
Alignment phase: Refines outputs for coherence and accuracy
Predicting Two Steps Ahead
Training: Forecasts the next two tokens at once
Inference: Produces answers faster through parallel token prediction
Sparse, Task-Specialised Processing
Only 5.5% of parameters (37B/671B) are activated per query
Cost Savings
DeepSeek’s pricing changes what businesses can achieve with limited budgets:
Free to use via its web app.
Although for business use cases, these are typically carried out through API calls
Provides API access at a relatively low cost ($0.14 for 1 million input tokens, compared to $7.5 for OpenAI’s o1 model)
To companies with significant usage of LLMs, these differences can add up to thousands of dollars over the course of a month
Implications
Democratisation: Smaller companies can more easily compete with larger businesses.
Pressure on Closed Models: Companies like OpenAI are under pressure to reduce their prices or increase the transparency of their models.
Ethical Trade-Offs: Though open weights help in bias mitigation, unregulated customisation risks misuse.
Conclusion
DeepSeek-R1 proves that AI progress does not have to rely on closed systems or unsustainable compute budgets.
For organisations, this means faster experimentation, lower barriers to entry, and control over AI tools: a combination likely to accelerate innovation across different industries.
While not flawless, its open-source model and technical ingenuity set a new standard for what’s possible in efficient, accessible AI.
If you are looking for expert AI consulting guidance on how Large Language Models (LLMs) can be used within your business, or if you are interested in any other AI consulting services, please get in contact.