What Is Behind the DeepSeek LLM?
What Is Its Capability and Why Did It Cost So Little to Train?

Part II: DeepSeek vs. the Titans — GPT-4.0, Claude 3.5, and LLaMA

From Our Knowledge Base

If Part I was about the how, this one’s all about the can it hang?

DeepSeek made noise by showing you can train a seemingly top-tier LLM for under $10 million. But now we face the real question: How does it actually perform against the best in the game? No hype, no headlines—just real benchmarks, capability comparisons, and what it means for the future of generative AI.

Let’s size up DeepSeek against the current AI elite:
GPT-4.0, Claude 3.5, and LLaMA.

Benchmark Showdown:

Language, Logic, and Code

DeepSeek has posted competitive scores on widely accepted benchmarks like:

  • MMLU (Massive Multitask Language Understanding)
  • GSM8K (grade school math reasoning)
  • HumanEval (code generation)
  • ARC (AI2 Reasoning Challenge)

And while it may not top the leaderboard, DeepSeek consistently places in the upper-middle tier, outperforming many earlier-generation models and occasionally nipping at the heels of GPT-3.5 or Claude 2.

Key takeaway: DeepSeek trades a bit of precision for massive efficiency gains. It’s no GPT-4—but it beats expectations hard at its cost level.

Reasoning & Comprehension:

The Real Test

Where DeepSeek still has ground to cover:

  • Complex multi-hop reasoning can trip it up.
  • Ambiguity handling is weaker compared to Claude 3.5.
  • Memory and contextual threading across long conversations is still being tuned.

But here’s the kicker: For many real-world use cases—like summarization, Q&A, and task automation—DeepSeek is already “good enough.”

It’s the classic 80/20: 80% of the capability for 10% of the cost.

Multimodal Capabilities:

Not Yet

Unlike GPT-4.0 and Claude 3.5, DeepSeek is not multimodal—yet.

  • No vision.
  • No audio.
  • No tool-use orchestration or plug-ins.

This limits its use cases in areas like accessibility, visual search, or cross-modal analysis. But it also explains its lean training cost. Staying text-only is a cost-saving choice, not a technical failure.

Prediction: If/when DeepSeek goes multimodal, expect another low-cost surprise—optimized from the jump.

Inference Efficiency:

The Quiet Power Move

Training is flashy. Inference is where models live.

DeepSeek’s architecture was clearly designed with inference cost in mind:

  • Fast decoding times on lower-cost hardware.
  • Reduced memory load, enabling broader deployment.
  • Potential support for quantized deployment without heavy performance loss.

This makes DeepSeek a serious contender for businesses, researchers, and governments looking to run models without renting a hyperscaler for the month.

Open Source & Customization:

A Big Win

DeepSeek’s open-source release (under a relatively permissive license) opens the door for:

  • Fine-tuning and domain specialization.
  • Integration with smaller-scale private data.
  • Forking for research, regional, or industry-specific use.

In a world where GPT-4.0 remains closed and Claude is invitation-only, DeepSeek is showing up for the open community.

What It Signals:

The Future Is Fractal, Not Monolithic

DeepSeek may not beat GPT-4.0 or Claude 3.5—but that’s not the point.

It’s not a generalist-for-everything. It’s a surgical strike against the high-cost status quo. And it’s working.

Here’s what DeepSeek signals:

  • The era of “bigger is better” may be fading. Smarter is winning.
  • Budget models can—and will—compete, especially in focused use cases.
  • Future AI landscapes will be fragmented, modular, and efficient, not monolithic and overbuilt.

And if DeepSeek is any indication, the next wave of LLMs won’t just be cheaper to train—they’ll be better tuned to perform with purpose.

Final Word:

DeepSeek Didn’t Just Show Up. It Showed the Way.

Big models dominated the first chapter of AI.

Lean, smart, purpose-built models like DeepSeek might write the next.

Part I:
Architecture, Approach, and the Price Tag Surprise

When the DeepSeek LLM entered the scene, it didn’t just arrive quietly—it barged in with a stat that made even seasoned AI insiders blink: a full training run for under $10 million.

In a world where flagship LLMs can cost upwards of $100 million or more just to train—DeepSeek looked like a rounding error.

So how did a lesser-known team pull off this budget-defying model? Let’s break it down.

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