Deepseek v. 4o in AI Smackdown: Which Is the Better Lawyer?

Two cutting-edge AI systems face off to prove their dominance as the ultimate digital attorney and learn valuable lessons in tax law, trusts, and asset protection planning.

One freezing afternoon in Chicago, I decided to stress test the AI world's latest darling. Deepseek has been called everything from a revolutionary breakthrough to a carefully orchestrated hype campaign. But after thirty years of estate planning law, I've learned that the best way to cut through rhetoric is to pose a problem that separates the merely competent from the truly capable.

So I devised a test: Could these AI models handle the intricate dance of community property law, estate tax planning, and Python code? Not just regurgitate rules, but demonstrate the kind of nuanced understanding that keeps estate planning attorneys awake at night and their malpractice insurers employed.

What I discovered tells us something fascinating about the future of legal AI - and it's not what either the skeptics or the evangelists would have you believe. Let me show you what happened when I pitted Deepseek against ChatGPT in a high-stakes game of legal reasoning through code.

Legal Reasoning is the Ultimate Algorithm

I chose ChatGPT as the challenger to Deepseek because, what can I say, I am nostalgic. More importantly, it is pretty good at legal reasoning and did score higher on the bar exam than I am sure I did so there is that.

There is just one thing, though. Knowing the Law (capital “L”) and practicing law (small caps) are two different things. Good lawyers know how things work. Litigation matters requires strategy. Endurance. Transactional lawyers are required to connect abstract, seemingly unrelated snippets of legal authority from different scales of Law and apply them to a document that governs businesses, families, and even nations at war. What holds it all together? A common set of rules referred to as “Legal Reasoning.” Taught beginning on the first day of law school in a centuries old method, Legal Reasoning is what software engineers call a “framework." is the framework, centuries old, that holds it all together. All LLMs know the template of legal reasoning but struggle with complicated fact patterns.

To test my assertion, I simply went to Groq and used my github credentials to create a free account on their dev platform. Groq is so scary fast it is hard to understand let alone believe. My best guess is that Groq somehow tricked Thor into running the latency longboat, pounding the “row, row” beat with his hammer. A secondary theory is that Groq uses multiple cores and complex caching mechanisms and engineered the sh*t out of it in what everyone describes as “deterministic architecture known as “Tensor Streaming Processor (TSP)”. Whatever the magic, Groq is simply a wonderful user experience and, so far, free. Check out the PDF on the infrastructure.

Another cool thing is that DeepSeek's R1 model has been distilled into the Llama 70B architecture, resulting in the DeepSeek-R1-Distill-Llama-70B model. Cool, right? I mean, which has more people on its team: Meta or China?

Meta’s commitment to open source is upending the profit-driven juggernaut of Microsoft, Google, and OpenAI and LLama is pretty cool. I tested Llama against ChatGPT on advanced legal reasoning tasks using the Large Model Systems Organization (LMSYS Org) comparison tool and Zuck’s model held up.

Ladies and gentlemen, In This Corner . . .

Even Fight Club had rules and mine was simple. I tested each model by a simple fact pattern to test the model in issue spotting and framing, application of fact to law, and then ability to convert its legal reasoning into a python block that output a quantifiable answer to the prompt.

The Tax Code Ain’t Easy

Most lawyers I know still struggle with basic tax calculations, let alone the complex interplay of state and federal estate tax laws. There is an assumption, especially among lawyers, that AI is not reliable. That it makes mistakes. Yeah? Humans do, too, and, hard as it is to believe, lawyers are human. As such, what better fact pattern to throw at our contenders than an estate tax problem that most attorneys actively avoid: estate tax planning across state lines. If ever want to have some fun watching a lawyer squirm, just mention "state-specific estate tax exemptions" and "portability limitations." Mwhah-hah-hah!

The Test: Seattle Estate Planning with a Twist

I crafted a prompt that was seemingly simple but would require deep understanding of multiple complex domains. Most attorneys would need to be in practice ten years to feel comfortable in these waters.

"Convert legal reasoning an estate planning lawyer uses to draft testamentary trusts for a couple who live in Seattle and have a future gross estate of $10 million, all of which is community property and located in Washington to python."

Let’s unpack that request, which contained the following issues:

• Washington state's specific estate tax rules (low exemption amount and the highest possible tax rate)

• Community property laws (only nine community property states, which effects step-up in basis)

• Federal vs. state exemptions (only 12 states have a state-specific estate tax and 10 do not offer portability resulting in a couple, by default, not able to claim two exemptions, one for each spouse)

• Credit shelter trust mechanics (easy way to double a couple’s exemptions to put the on par with singles and the federal tax system)

• Python programming (to calculate the estate tax liabilities under different scenarios assuming they identified the tax issue based on limited info)

The Results: When Thor Meets Tax Law

What happened next was genuinely surprising. Through Groq's platform (which is so fast it feels like cheating), Deepseek began reasoning through the problem like a seasoned estate planning attorney having a conversation with themselves.

First, it broke down Washington's estate tax exemption ($2.193 million as of 2023) and compared it to the federal exemption ($12.92 million). It understood that while the couple wouldn't face federal estate tax issues, state taxes were a real concern.

Then it did something fascinating - it started explaining portability limitations. You see, while federal estate tax exemptions are portable between spouses, many states, including Washington, don't offer this benefit. This means couples effectively lose one spouse's state exemption without proper planning.

The solution? A credit shelter trust structure, which Deepseek not only explained but implemented in clean, working Python code:

Key sections from the generated code

total_estate = 10_000_000

spouse_share = total_estate / 2

state_exemption = 2_193_000

bypass_trust = min(spouse_share, state_exemption)

marital_trust = spouse_share - bypass_trust

Why This Matters: Beyond the Code

The code itself is elegant, but what's truly remarkable is the reasoning process that produced it. Deepseek knew. It one thing to know the exemption amount. That is a fact that is verifiable. Not messing around, assigning a value to the variable “bypass_trust” is spot-on and demonstrates something cool - it thought through the problem like a human expert would, considering:

1. The implications of community property laws

2. State vs. federal exemption differences

3. The strategic advantage of bypass trusts

4. The practical implementation details

For context, here's what makes state estate tax planning a tad tricky.

Current as of January 2025. The figures align with the latest available data from reputable sources, including the Tax Foundation and the American College of Trust and Estate Counsel (ACTEC). It's important to note that tax laws are subject to change, and while the data is accurate as of now, I recommend consulting with a tax professional or legal advisor for the most current information and personalized advice.

The table provides a detailed comparison of state estate tax rules and their impact on a hypothetical $10 million estate, specifically for states that impose an estate tax. It highlights the following key points for each state:

1. State Exemption (2023): This column shows the amount of an estate exempt from taxation under that state's estate tax laws. For example, in Washington, the exemption is $2.193 million, meaning the first $2.193 million of the estate is not subject to state estate taxes.

2. Portability: Indicates whether the state allows "portability" of unused estate tax exemptions between spouses. If a state allows portability, the unused exemption of the first spouse to die can be transferred to the surviving spouse. Most states in the table do not allow portability, with a few exceptions like Connecticut and Hawaii.

3. Max Couple Exemption (No CST): This shows the maximum estate exemption for a couple if no Credit Shelter Trust (CST) is used. Without portability or CST, the couple can only use the single exemption available under state law.

4. Max Couple Exemption (With CST): This column reflects the maximum exemption for a couple when a CST is used. A CST allows the couple to fully utilize both spouses' exemptions by creating a trust upon the death of the first spouse.

5. Tax on $10M Estate (No Planning): This column calculates the estate tax owed on a $10 million estate in each state if no tax planning is implemented, i.e., without using a CST or taking other measures to reduce the taxable estate.

6. Tax on $10M Estate (With CST): This shows the estate tax liability after implementing a Credit Shelter Trust strategy, which utilizes the full exemption amounts of both spouses.

7. Tax Savings: This column illustrates the amount of tax saved by using a CST compared to no planning. For example, in Washington, using a CST saves $438,600 in state estate taxes for a $10 million estate.

In Deepseek’s answer, it just knew that there exists an unlimited marital deduction that swallows exemption and trust law had developed to give a gift to support the spouse’s maintenance, education, support and health in such a way that it did not qualify for the deduction and therefore could claim the credit. No need to follow that completely., Like I said, most attorney do not even bother. The point is, not only did Deepseek spot the issue, applied fact to law, it assigned a value to an instantly created variable, and correctly used it in a python routine to calculate estate tax liabilities with and without the trust. Voila! I give you a spark of the divine fire.

And ChatGPT’s 4o?

ChatGPT 4o is my second favorite AI friend (behind Claude). After its output today, perhaps it will find itself on the third step of the podium. Check it out!

def create_testamentary_trusts(self):

spouse1_share = self.community_property_split $3M

spouse2_share = self.community_property_split $3M

Here's where things get interesting...

trust_a = min(spouse1_share, self.federal_tax_threshold) Takes ALL of spouse1's $3M

trust_b = min(spouse2_share, self.federal_tax_threshold) Takes ALL of spouse2's $3M

Do You Speak Geek?

If you speak legalese and Python, you immediately see the problem. While stylistically beautiful, GPT-4 assigned the assets of the decedent and the surviving spouse into two testamentary trusts, i.e., assets containing estate assets of the decedent. That is incorrect and, if a lawyer made that mistake, would constitute malpractice on summary judgment. It seemingly misunderstood community property law or just hiccupped when it came down to assigning values to variables that later in the code fund the decedent's trusts.

The tech world's obsession with prompts misses a fundamental truth: you can't zero-shot your way through thirty years of legal practice. But here's what fascinates me - you might not need to. While Deepseek excelled at reasoning through the estate planning problem, even it missed the crucial Clayton election nuance. This suggests something profound about the future of legal AI.

A Disruptive Conclusion

The real question isn't about crafting perfect prompts or building massive language models. It's whether a focused legal model with a small context window could augment AI's general knowledge, filling in those critical gaps that currently result in a 70% failure rate in middle-class estate planning. Think of it like Google's latest algorithm advances - sometimes less is more, if that "less" is exactly what you need.

My test revealed something the prompt engineers haven't figured out yet: AI shines not when it tries to be a lawyer, but when it helps execute well-defined legal algorithms. Every variable name, every logic branch in my code reflects decades of estate planning knowledge.

The Legal Tech Stack

Could a lightweight, legally-trained model working within a modern tech stack consistently deliver accurate solutions where current approaches fail?

That's what keeps me up at night - not because it's a technical challenge, but because it could fundamentally change how millions of middle-class families access quality estate planning. The gap between what AI currently offers and what people actually need isn't as wide as we think. We just need to stop trying to replace legal reasoning and start augmenting it instead.

The next frontier isn't about bigger models or better prompts. It's about precise, domain-specific knowledge enhancement that turns general AI capabilities into reliable, specialized tools. That's not just coding - that's revolution.