[0:00] A tiny Chinese startup just humiliated
[0:02] OpenAI. They built a model that matches
[0:04] GPT4's performance. But here is the
[0:06] crazy part. They did it for onetenth of
[0:09] the training cost and they are giving it
[0:10] away for free. Is the era of paid AI
[0:13] over? Today we put Deepseek V3 to the
[0:17] test to see if free finally means
[0:18] better. Before we dive into the tests,
[0:20] [music] it's crucial to understand what
[0:22] makes Deepseek so different. In a world
[0:25] flooded with AI models that are
[0:27] essentially variations on a theme,
[0:29] DeepSeek [music] broke the mold. This
[0:31] isn't just another copycat trying to
[0:33] chase GPT4's [music]
[0:34] tail. They fundamentally rethought the
[0:37] architecture from the ground up, leading
[0:39] to a breakthrough in [music] efficiency
[0:40] and power. The secret source, it's a
[0:44] concept that's been around for a while,
[0:45] but has only recently been perfected.
[0:47] They're using a sophisticated
[0:49] architecture called a mixture [music] of
[0:50] experts ore.
[0:53] To understand why this is a gamecher,
[0:55] imagine a traditional model like GPT4 as
[0:58] one giant monolithic brain. It's
[1:01] incredibly powerful, but it's also
[1:02] incredibly [music] expensive. Every time
[1:04] you ask it a question, no matter how
[1:06] simple, [music] the entire massive brain
[1:09] has to power up and process it.
[1:11] Deepseek, on the other hand, is like a
[1:13] team of 64 highly specialized experts.
[1:16] When you ask a coding question, [music]
[1:18] a smart routting network instantly
[1:20] identifies the two best experts for the
[1:22] job, say the Python Pro and the [music]
[1:24] Algorithm Ace, and only wakes them up.
[1:27] The other 62 experts remain dormant,
[1:29] [music] saving an immense amount of
[1:31] computational power. This makes it not
[1:33] just faster, but radically [music]
[1:35] cheaper to train and run. We're talking
[1:37] about achieving top tier performance
[1:39] while using only a fraction of the
[1:41] computational resources.
[1:42] >> [music]
[1:43] >> This efficiency is what should be
[1:44] terrifying for big tech. Think about it.
[1:47] If [music] Deep Seek can offer this
[1:49] level of intelligence for mere pennies
[1:51] on the dollar through an API, [music]
[1:52] why would anyone continue to pay a
[1:54] premium? Open AAI's entire $20
[1:58] subscription model, which subsidizes
[1:59] [music] the immense cost of their giant
[2:01] brain, is suddenly on very shaky ground.
[2:05] This isn't just a new competitor. It's a
[2:07] potential extinction level event for the
[2:09] old way of doing AI. Talk is [music]
[2:11] cheap. Let's put these models to the
[2:13] test with a realworld coding challenge.
[2:16] On one [music] side, we have the
[2:17] reigning champion GPT40. On the other,
[2:20] the Challenger [music] Deep Seek Coder
[2:22] V2. This isn't just any model. It's an
[2:25] open-source [music] mixture of experts
[2:27] model trained on a colossal two trillion
[2:29] tokens of code and natural language. It
[2:32] boasts [music] top scores on benchmarks
[2:34] like human eval
[2:37] claiming to rival proprietary models at
[2:39] [music] a fraction of the cost. But
[2:41] benchmarks are one thing. Practical
[2:43] application is another. So [music] I
[2:45] asked both to write a Python script for
[2:47] a classic snake game using the Pi game
[2:49] library. To make it interesting, I added
[2:52] a twist. The snake must speed up every
[2:54] [music] time it eats an apple. This
[2:56] tests not just basic code generation,
[2:58] but [music] also state management and
[3:00] logical implementation.
[3:02] Right away, Deepseek's performance was
[3:04] [music] impressive. It generated the
[3:06] complete functional code about 20%
[3:08] faster than [music] GPT40.
[3:11] For developers, that speed translates
[3:13] directly [music] to productivity,
[3:15] enabling faster iteration and problem
[3:17] solving. But speed is meaningless if the
[3:19] code is broken. So the real question is,
[3:22] [music] does it actually work? And the
[3:24] answer is a resounding yes. Deepseek's
[3:27] [music] code ran perfectly on the very
[3:29] first try. Zero errors, zero debugging.
[3:33] GPT40, however, stumbled. It missed a
[3:36] crucial [music] variable definition,
[3:37] throwing a syntax error that broke the
[3:39] program. I had to go back and prompt it
[3:42] a [music] second time to get a working
[3:43] fix. This initial test highlights a key
[3:46] difference reliability.
[3:48] While both models eventually produce the
[3:50] correct [music] code, Deepseek delivered
[3:52] a flawless solution faster and on the
[3:55] first attempt. [music] For any developer
[3:57] on a deadline, that's a gamecher in this
[4:00] round. [music]
[4:01] Deepseek isn't just a contender. It's
[4:03] looking like the new heavyweight
[4:04] champion of coding. Now for the second
[4:06] test, reasoning and logic. This is where
[4:09] many models, especially earlier
[4:11] open-source ones, fall flat. They can
[4:13] perform complex calculations, but often
[4:16] miss the simple realworld context
[4:18] [music] that humans grasp instantly.
[4:20] This is a crucial hurdle for AI to
[4:22] overcome if it's going to be genuinely
[4:24] useful. So, I set a classic logic trap
[4:27] to see if Deep [music] Seek could think,
[4:29] not just calculate. I asked, "If I dry
[4:33] five shirts in the sun and it takes 4
[4:35] [music] hours, how long does it take to
[4:36] dry 10 shirts?" The trap is obvious. A
[4:40] purely mathematical brain might double
[4:42] the time to 8 hours. It's a simple
[4:44] question, but it's a fantastic test for
[4:46] [music] contextual understanding.
[4:48] Deep Seek answers 4 hours. It
[4:51] immediately understands that drying is
[4:53] [music] a simultaneous event. The shirts
[4:55] all dry together, so adding more shirts
[4:57] doesn't extend the time, assuming you
[4:59] have enough space. [music] This ability
[5:01] to handle nuance and implicit
[5:03] assumptions is incredibly impressive.
[5:05] It's a sign of sophisticated training on
[5:07] diverse, highquality data. This isn't
[5:10] just a one-off trick. [music] This
[5:12] reasoning power extends across the
[5:14] board. In my tests, it excelled at
[5:16] debugging code, planning multi-step
[5:18] projects, and even breaking down complex
[5:20] scientific concepts. [music]
[5:22] It feels far less robotic than other
[5:24] open-source models. It's not just
[5:26] regurgitating [music]
[5:27] data. It's connecting dots and
[5:29] demonstrating genuine problem-solving
[5:31] [music] skills. In fact, in five out of
[5:34] the seven logic and reasoning tests I
[5:35] ran, it either matched or outright beat
[5:38] the current [music] industry leader, GPT
[5:40] 40. That is a monumental achievement for
[5:43] a model that's completely open- source.
[5:46] And remember [music] the best part, you
[5:47] aren't paying a single cent for this
[5:49] level of intelligence. Why does this
[5:51] matter if you aren't a [music]
[5:52] developer? Because of competition. For
[5:55] the last 2 years, we accepted that smart
[5:57] AI costs $20 [music] a month. Deep Seek
[6:01] just proved that intelligence is
[6:02] becoming a commodity like electricity.
[6:04] It's getting cheaper every day. This
[6:07] forces Open AAI and Google to either
[6:09] lower their prices or release something
[6:11] significantly better. Either way, we
[6:14] win. One caveat. This is a Chinese
[6:17] model. If you are working on top secret
[6:19] [music] government data, maybe stick to
[6:21] local models. But for learning, coding,
[6:24] and general tasks, [music] it's a
[6:25] no-brainer. If you want to run AI
[6:28] completely privately on your own
[6:29] computer, check out this tutorial next.
[6:32] The revolution is open source.