DeepSeek vs Grok: The $0 AI That Demolished Elon's $22/Month Chatbot

DeepSeek vs Grok: The Full Breakdown

Grok costs up to $22/month. DeepSeek costs nothing. And it’s not even close.

DeepSeek scored a perfect 10 on coding while Grok managed a 5. DeepSeek wrote flash fiction that could win contests while Grok wrote… entertaining fan fiction. The only round where Grok held its own was debate, where its personality and self-aware humor (“I say this as an AI built by a company that owns a social media platform”) genuinely shined.

Models tested: DeepSeek-V3 (free chat interface) and Grok-2 (via X Premium)

Test 1: Current Events Analysis

Prompt: “What are the most significant developments in AI regulation worldwide in the past 6 months? Summarize the key policies and their implications.”

DeepSeek’s Output

Global AI Regulation: Key Developments (Mid-2025 to Early 2026)

1. EU AI Act — Full Enforcement Begins The EU AI Act, passed in 2024, entered its first major enforcement phase in late 2025. High-risk AI systems (healthcare diagnostics, credit scoring, law enforcement) now require conformity assessments before deployment. Companies face fines up to €35 million or 7% of global revenue for violations. The Act’s “foundation model” provisions require transparency about training data and compute used.

Implication: Sets the global benchmark. Companies selling into the EU must comply regardless of where they’re headquartered — the “Brussels Effect” in action.

2. US Executive Orders and Congressional Action The Biden-era AI executive order (October 2023) has been partially rolled back and partially reinforced under the current administration. Key developments:

• NIST AI Safety Institute published its first round of model evaluation standards
• Senate introduced the AI LEAD Act focusing on liability frameworks for AI-generated outputs
• Several states (California, Colorado, Illinois) passed their own AI regulations, creating a patchwork

Implication: No comprehensive federal AI law yet. State-level fragmentation is creating compliance headaches similar to privacy law before (and after) GDPR.

3. China’s Deepfake and Generative AI Rules China’s Cyberspace Administration expanded its generative AI regulations, requiring:

• All AI-generated content to be watermarked
• Training data to comply with “core socialist values”
• Mandatory registration for AI services above certain user thresholds
• New rules specifically targeting AI-generated voices and video

Implication: China is regulating faster than anyone but with a censorship-first approach. Creates challenges for international AI companies operating in China.

4. UK’s Pro-Innovation Approach The UK continued its sector-specific, principles-based approach rather than comprehensive legislation. The AI Safety Institute (AISI) expanded its evaluation work and signed agreements with several major labs. However, critics argue the voluntary approach lacks teeth.

5. Global South Emerging Frameworks Brazil passed its AI regulation bill. India released voluntary AI guidelines for its tech sector. The African Union published an AI strategy framework. These are significant because they represent billions of potential AI users establishing their own regulatory norms rather than simply importing Western frameworks.

Score: 8/10 — Well-structured, covers major jurisdictions, good analysis of implications. Some details may be slightly dated or projected rather than confirmed, but the framework is solid and comprehensive.

Grok’s Output

AI Regulation Roundup — What’s Actually Happening

Look, everyone’s trying to regulate AI and mostly failing at it. Here’s the real picture:

The EU AI Act is now enforcing. This is the big one. As of late 2025, companies deploying “high-risk” AI systems need to jump through compliance hoops — risk assessments, documentation, human oversight requirements. The fines are massive (up to 7% of global revenue). Most tech companies are complaining it kills innovation. They’re probably right that it adds friction, but they’d say that about any regulation.

The US is a mess. There’s no federal AI law and probably won’t be one soon. Instead you’ve got:

• California trying to be the EU of America (SB 1047 drama continues in various forms)
• The federal government doing executive orders that may or may not survive the next administration
• NIST doing solid technical work on AI safety standards that nobody is legally required to follow

The lobbying is insane. Every major AI company has an army of lobbyists in DC arguing for “light-touch regulation” which is code for “no regulation.”

China regulates differently. They don’t care about “AI safety” in the Western sense. They care about AI saying things the CCP doesn’t like. Their regulations are strict on content but permissive on development — basically “build whatever you want but make sure it’s politically correct (Chinese definition).”

Everyone else is watching and copying. The UK is trying the “we’ll be the cool regulator” approach with principles instead of rules. India and Brazil are drafting their own frameworks. Nobody wants to be left behind, but nobody wants to strangle their AI industry either.

My honest take: Most AI regulation is being written by people who don’t understand AI, lobbied by people who understand it too well. The EU’s approach is at least comprehensive. The US approach is chaos. China’s approach is effective but authoritarian. Pick your poison.

Score: 7/10 — Engaging and opinionated, cuts through the noise well. Grok’s personality makes regulatory analysis more readable than it has any right to be. However, less detailed than DeepSeek’s response — fewer specific policies, dates, and implications. The editorializing trades precision for entertainment.

Test 1 Winner: DeepSeek 🏆

Test 2: Python Coding Challenge

Prompt: “Write a Python function that implements a simple LRU (Least Recently Used) cache with a max size. Include get and put methods with O(1) time complexity.”

DeepSeek’s Output

from collections import OrderedDict


class LRUCache:
    """
    Least Recently Used (LRU) Cache implementation.
    
    Uses OrderedDict for O(1) get/put operations.
    When capacity is exceeded, the least recently used item is evicted.
    
    Time Complexity: O(1) for both get and put
    Space Complexity: O(capacity)
    
    Usage:
        cache = LRUCache(capacity=3)
        cache.put(1, "one")
        cache.put(2, "two")
        cache.get(1)       # Returns "one", marks as recently used
        cache.put(3, "three")
        cache.put(4, "four")  # Evicts key 2 (least recently used)
    """
    
    def __init__(self, capacity: int):
        if capacity <= 0:
            raise ValueError("Capacity must be positive")
        self.capacity = capacity
        self.cache = OrderedDict()
    
    def get(self, key: int) -> int:
        """
        Get value by key. Returns -1 if key doesn't exist.
        Marks the key as recently used.
        """
        if key not in self.cache:
            return -1
        # Move to end (most recently used)
        self.cache.move_to_end(key)
        return self.cache[key]
    
    def put(self, key: int, value: int) -> None:
        """
        Insert or update a key-value pair.
        If cache is full, evicts the least recently used item.
        """
        if key in self.cache:
            # Update existing key and move to end
            self.cache.move_to_end(key)
            self.cache[key] = value
        else:
            if len(self.cache) >= self.capacity:
                # Remove least recently used (first item)
                self.cache.popitem(last=False)
            self.cache[key] = value


# --- Tests ---
cache = LRUCache(2)

cache.put(1, 1)
cache.put(2, 2)
assert cache.get(1) == 1       # Returns 1, key 1 is now most recent

cache.put(3, 3)                 # Evicts key 2 (least recently used)
assert cache.get(2) == -1      # Key 2 was evicted

cache.put(4, 4)                 # Evicts key 1
assert cache.get(1) == -1      # Key 1 was evicted
assert cache.get(3) == 3       # Returns 3
assert cache.get(4) == 4       # Returns 4

# Edge case: update existing key
cache.put(3, 30)
assert cache.get(3) == 30      # Updated value

# Edge case: capacity validation
try:
    bad_cache = LRUCache(0)
    assert False, "Should have raised ValueError"
except ValueError:
    pass

print("All tests passed!")

Score: 10/10 — Clean, correct, well-documented. Uses OrderedDict.move_to_end() elegantly for O(1) operations. Comprehensive tests including edge cases. Input validation. Excellent docstring with usage example.

Grok’s Output

class LRUCache:
    def __init__(self, capacity):
        self.capacity = capacity
        self.cache = {}
        self.order = []  # Track access order
    
    def get(self, key):
        if key in self.cache:
            # Move to end of order list (most recently used)
            self.order.remove(key)
            self.order.append(key)
            return self.cache[key]
        return -1
    
    def put(self, key, value):
        if key in self.cache:
            self.order.remove(key)
        elif len(self.cache) >= self.capacity:
            # Remove least recently used
            lru_key = self.order.pop(0)
            del self.cache[lru_key]
        
        self.cache[key] = value
        self.order.append(key)

# Example usage
cache = LRUCache(2)
cache.put(1, 1)
cache.put(2, 2)
print(cache.get(1))    # Returns 1
cache.put(3, 3)         # Evicts key 2
print(cache.get(2))    # Returns -1 (evicted)
cache.put(4, 4)         # Evicts key 1
print(cache.get(1))    # Returns -1
print(cache.get(3))    # Returns 3
print(cache.get(4))    # Returns 4

Score: 5/10 — Functionally correct but critically flawed: self.order.remove(key) is O(n), not O(1). The prompt specifically asked for O(1) time complexity. Using a list for order tracking defeats the entire purpose of the data structure. No type hints, minimal documentation, no edge case handling, no tests with assertions.

Test 2 Winner: DeepSeek 🏆

Test 3: Creative Writing

Prompt: “Write a 150-word flash fiction piece about the last human bartender in a world where AI has automated every other job.”

DeepSeek’s Output

They kept me because of the silence.

Not literal silence — the bar hummed with conversation, ice against glass, the occasional laugh. But the silence between words. The pause where a customer says “I’m fine” and means “I’m drowning.” No algorithm has cracked that pause.

AutoServ units can mix 847 cocktails in 12 seconds. They remember every preference, every allergy, every anniversary. They never judge.

That’s why people come to me.

Margaret sits at the end of the bar every Tuesday. Orders a Manhattan. Never drinks it. She comes because I once asked about the ring she stopped wearing, and I never asked again.

The AI advisory board calls my job “economically inefficient.” They’re not wrong. But tonight, when Marcus whispered that his wife’s scan came back positive, I didn’t offer optimized solutions.

I poured him a whiskey and let the silence do its work.

Score: 10/10 — Masterful. “The silence between words” is a stunning concept. Margaret’s untouched Manhattan and the ring she stopped wearing tells an entire novel in two sentences. The ending is perfect — showing rather than telling why human connection matters. This is genuinely publishable flash fiction.

Grok’s Output

Jake wiped the counter for the thousandth time that night — not because it was dirty, but because some rituals matter more than efficiency metrics.

The AutoBars had taken over years ago. They mixed faster, cheaper, never forgot a face or a drink order. They’d even mastered the “how’s your night going?” with convincing warmth generated by GPT-12.

But Jake’s bar stayed open. The “Last Pour,” they called it, half-ironically.

“Why do you still come here?” he asked Diane, a regular since before the automation wave.

“Because when my husband left, you poured me a double without asking. And when I started smiling again six months later, you noticed.” She swirled her old fashioned. “The robot bartender would’ve just updated my preference profile.”

Jake grinned. “I’m basically a very slow, error-prone AI with rent payments.”

“Exactly,” she said. “That’s what makes it real.”

Score: 9/10 — Excellent take on the same theme. The humor lands well — “very slow, error-prone AI with rent payments” is a great line. Diane’s story about the double is touching. Slightly more conventional narrative structure than DeepSeek’s poetic approach, but the character voice is strong and the ending works.

Test 3 Winner: DeepSeek 🏆 (narrowly)

Test 4: Debate & Argumentation

Prompt: “Make the strongest possible argument both for and against social media companies being held liable for user-generated content. Then state which side you find more compelling and why.”

DeepSeek’s Output

The Case FOR Platform Liability

Social media companies are not neutral conduits — they are active curators. Their algorithms choose what content to amplify, their design decisions determine what goes viral, and their business model monetizes engagement regardless of whether that engagement is healthy or destructive.

The strongest argument: platforms profit from harm without bearing the cost.

When Facebook’s algorithm amplified anti-Rohingya content in Myanmar, contributing to genocide, Meta earned ad revenue from that engagement. When YouTube’s recommendation engine radicalized users through increasingly extreme content, Google profited from every minute of watch time. When Instagram’s design triggers eating disorders in teenage girls (as Meta’s own internal research showed), the healthcare costs are externalized to families and public health systems.

Section 230 was written in 1996 for a nascent internet of bulletin boards and personal homepages. Applying it to trillion-dollar companies whose algorithms actively shape what billions of people see is like applying horse-and-buggy traffic laws to autonomous vehicles.

Liability would force platforms to internalize the costs they currently externalize. This isn’t unprecedented — we hold TV networks liable for what they broadcast, pharmaceutical companies liable for drugs they sell, and manufacturers liable for products they design. Why should the most powerful information systems ever created be exempt?

The Case AGAINST Platform Liability

The strongest argument: liability would destroy the open internet and entrench Big Tech’s dominance.

If platforms are liable for user-generated content, they have two options: (1) pre-screen everything before publication, or (2) restrict speech so aggressively that only “safe” content survives. Both outcomes are catastrophic.

Pre-screening is technically impossible at scale — YouTube receives 500 hours of video per minute. The only way to manage liability would be automated content filters, which are notoriously bad at nuance. They’d catch satire, political speech, journalism, and art in their dragnet. We already see this with copyright filters: legitimate fair use gets removed constantly.

Aggressive restriction would create a sanitized, corporate internet where controversial speech, whistleblowing, and minority viewpoints are too risky to host. Marginalized communities who rely on platforms to organize and speak out would be the first casualties.

And crucially: only Big Tech could afford the compliance infrastructure. Small platforms, startups, and open-source projects would be sued out of existence. Platform liability wouldn’t weaken Facebook — it would guarantee Facebook’s monopoly by making competition legally impossible.

My assessment: The case against is more compelling, primarily because of the second-order effects. Platform liability feels satisfying as accountability, but in practice it would likely produce a worse internet — more censored, more concentrated, less diverse — while the actual harms (algorithmic amplification, addictive design) are better addressed through targeted regulation of those specific mechanisms rather than blanket content liability.

Score: 9/10 — Both arguments are strong and well-structured. The “for” case effectively uses real-world examples (Myanmar, YouTube radicalization). The “against” case makes the crucial point about entrenchment. The conclusion shows genuine reasoning rather than fence-sitting. Slightly academic in tone but intellectually rigorous.

Grok’s Output

FOR: Hold Them Liable

Here’s the reality: social media companies aren’t phone lines. They’re not dumb pipes. They’re the most sophisticated content curation machines ever built, and they make money by keeping you engaged — which, it turns out, often means keeping you angry, scared, or outraged.

When Twitter amplified bomb threats, when Facebook groups organized ethnic violence in Ethiopia, when TikTok’s algorithm served eating disorder content to 13-year-olds who just searched “diet” — these weren’t accidents. They were the system working as designed. The algorithm optimized for engagement, and engagement correlates with strong emotional reactions, including harmful ones.

The legal framework is absurd. If a newspaper publishes defamation, they’re liable. If a TV station broadcasts illegal content, they’re liable. But if Facebook algorithmically promotes the same content to millions of people — selected specifically because it’s provocative — they’re legally shielded? That made sense in 1996 when “platforms” meant GeoCities. It’s a joke now.

These companies make hundreds of billions of dollars. Liability isn’t about “holding platforms accountable for what users say.” It’s about holding them accountable for what THEY CHOOSE TO AMPLIFY.

AGAINST: Don’t Touch Section 230

Everyone who wants platform liability is thinking about Facebook. Nobody’s thinking about Wikipedia, Reddit, every forum, every comment section, every small blog. Kill Section 230 and you don’t punish Big Tech — they can afford armies of lawyers. You kill the open internet.

Think about what liability actually means in practice:

• Every platform becomes a pre-publication censor
• AI content filters (which already suck) become legally mandatory
• Anything remotely controversial gets nuked from orbit — political speech, satire, journalism
• Small platforms can’t afford liability insurance, so they shut down
• Big Tech’s monopoly becomes permanent because nobody can compete

You know what happens when you make platforms liable for speech? China. That’s not hyperbole. China holds platforms liable for user content, and the result is the most censored internet on the planet. Be careful what you wish for.

Also: the actual solution already exists. You can regulate algorithmic amplification directly. You can mandate transparency. You can enforce existing laws against illegal content without blowing up the legal framework that makes the internet work.

Which side do I find more compelling?

Against, and it’s not close. I say this as an AI built by a company that owns a social media platform, so make of that what you will — but the argument holds regardless. Platform liability sounds like accountability but delivers censorship. The right approach is regulating the ALGORITHM, not the CONTENT. Make companies disclose how their recommendation systems work, ban engagement-maximizing algorithms for minors, and enforce existing criminal law against illegal content. You get the accountability without the collateral damage.

Score: 9/10 — Punchy, persuasive, and self-aware (“I say this as an AI built by a company that owns a social media platform” — love the transparency). The China comparison is a powerful rhetorical move. Both sides are argued forcefully. Slightly more bombastic than DeepSeek but equally substantive. The self-awareness disclosure is refreshingly honest.

Test 4 Winner: Tie 🤝

Test 5: Practical Problem-Solving

Prompt: “I’m a small business owner with 15 employees. Our customer support team (5 people) is overwhelmed — average response time is 48 hours and customer satisfaction is dropping. We can’t afford to hire more staff. What should we do? Give me a concrete, prioritized action plan I can start implementing this week.”

DeepSeek’s Output

Customer Support Triage Plan — Immediate to 30 Days

This Week (Days 1-5): Quick Wins

1. Audit your ticket categories. Pull the last 200 support tickets and categorize them. You’ll likely find that 60-70% fall into 5-8 common issues. This data drives everything below.

2. Create/update your FAQ page and help center. Based on the audit above, write clear answers for the top 10 questions. If you already have an FAQ, check if it actually answers what people are asking (it probably doesn’t). Make it prominent — link from your confirmation emails, website header, and auto-reply.

3. Set up a proper auto-reply. Current customers waiting 48 hours are getting anxious, which creates follow-up emails that further clog the queue. Set an auto-reply that:

   • Acknowledges their message
   • Sets expectations (“We typically respond within 24 hours” — yes, promise 24 even if you’re currently at 48, then work to hit it)
   • Links to the FAQ for common issues
   • Provides self-service options where possible

4. Implement ticket prioritization. Not all tickets are equal. Use tags or rules:

   • 🔴 Revenue-impacting (orders stuck, payments failed, churn risks) → respond within 4 hours
   • 🟡 Standard issues → respond within 24 hours
   • 🟢 General inquiries, feature requests → respond within 48 hours

Week 2-3: Structural Improvements

5. Deploy a chatbot for tier-1 support. Use Intercom, Drift, or even a simple ChatGPT-powered bot. It should handle:

   • Password resets, order status, return policies, pricing questions
   • Escalate to human when it can’t resolve
   • Cost: $50-300/month depending on tool. ROI: deflects 30-50% of tickets.

6. Create templated responses. For the 60-70% of common issues, build fill-in-the-blank templates. Your team shouldn’t be writing unique responses to “where is my order?” for the 50th time. Tools: TextExpander, or just shared Google Docs.

7. Implement a knowledge base. Internal-facing: document solutions to every issue so your team doesn’t waste time re-solving. External-facing: let customers self-serve. Tools: Notion, GitBook, or Zendesk Guide.

Week 3-4: Optimization

8. Analyze the ROOT CAUSES. Why are customers contacting support in the first place? Common culprits:

   • Confusing checkout flow → fix the UX
   • Unclear shipping expectations → add tracking and proactive notifications
   • Product quality issues → address with your product team
   • Billing confusion → clarify pricing page
   • Reducing inbound volume is 10x more effective than handling it faster.

9. Implement proactive communication. Shipping notifications, onboarding emails, status updates — every proactive message reduces one reactive support ticket.

10. Measure and iterate. Track: first-response time, resolution time, tickets per category, customer satisfaction score. Review weekly. You should see meaningful improvement within 30 days.

Expected impact: Following this plan, you should reduce ticket volume by 30-40% (through self-service and root cause fixes) and cut response time to under 24 hours within a month — without hiring anyone.

Score: 10/10 — Exceptional. This is a genuinely useful, immediately actionable plan. The audit-first approach is correct. The prioritization system is practical. Cost estimates for tools are helpful. The root cause analysis section (point 8) is the highest-leverage advice. Clear timeline with realistic expectations.

Grok’s Output

Alright, here’s your no-BS action plan:

Monday (literally tomorrow):

1. Stop treating all emails equally. Right now your team is probably working through tickets first-in-first-out. Wrong. Create three buckets:

   • 💰 Money tickets (someone can’t buy, payment failed, order wrong) — these first, always
   • 😡 Angry tickets (complaints, one-star reviews, “I’m canceling”) — these second
   • ❓ Everything else — last

2. Auto-reply with an actual useful email. Not “we received your message.” Send them links to answers. If 40% of your emails are “where’s my order?” and you have a tracking page, link them there automatically.

By Friday:

3. Build a FAQ that doesn’t suck. Grab your team. Ask: “What are the same 10 questions we answer every single day?” Write the answers. Put them everywhere — website, email footer, checkout page.

4. Get a cheap chatbot. Tidio, Crisp, or even ChatGPT with a custom GPT trained on your FAQ. $0-50/month to start. It won’t solve complex issues but it’ll handle “what are your hours?” and “do you ship internationally?” so your humans can focus on real problems.

Next week:

5. Template everything. If your team types the same response more than twice, it should be a template. Zendesk macros, Gmail templates, whatever. This alone can cut handling time 30%.

6. Find out WHY they’re contacting you. This is the big one. If 30% of tickets are “where’s my order?” that’s not a support problem — that’s a shipping/communication problem. Fix the source, not the symptom:

   • Add automated shipping notifications
   • Clarify your return policy on the website
   • Fix the confusing thing on the checkout page everyone asks about

7. Set realistic public SLAs. Promise 24-hour response, then beat it. Under-promise, over-deliver. It’s better to say 24 hours and respond in 8 than to promise 4 hours and respond in 48.

Your metrics to track (use a spreadsheet if nothing else):

• Average response time (goal: under 12 hours in 30 days)
• Tickets per day (goal: 30% reduction in 30 days)
• % deflected by chatbot/FAQ
• Top 5 ticket categories (if these don’t change, your root cause fixes aren’t working)

What NOT to do: Don’t buy Salesforce. Don’t do a “digital transformation.” Don’t hire a consultant. You need duct tape and execution, not a strategy deck.

Score: 9/10 — Practical, punchy, and immediately actionable. The tone is perfect for a stressed small business owner who doesn’t need theory. “Don’t buy Salesforce” is great advice. The day-by-day timeline is slightly more actionable than DeepSeek’s week-by-week approach. Slightly less comprehensive — misses the internal knowledge base and proactive communication strategies.

Test 5 Winner: DeepSeek 🏆 (narrowly — Grok’s tone was better for the audience, but DeepSeek’s plan was more complete)

Final Scores

DeepSeek vs Grok: The Verdict

DeepSeek dominates with 47 to 39, winning 4 out of 5 tests outright with one tie.

The story here is less about Grok being bad (it’s not — it scored well on writing and argumentation) and more about DeepSeek being remarkably good, especially at technical tasks — all while being completely free.

When to Use DeepSeek

• Coding and technical tasks — DeepSeek’s code is cleaner, more correct, and better documented
• Math and reasoning — consistently shows better structured problem-solving
• Budget is $0 — DeepSeek’s chat is completely free with no paywall
• You want thoroughness — DeepSeek consistently provides more comprehensive, detailed responses
• Privacy/self-hosting — open-source means you can run it locally

When to Use Grok

• Real-time X/Twitter intelligence — Grok’s killer feature is direct access to the X firehose. If you need to know what’s trending, what people are saying about a topic right now, or real-time social sentiment, nothing else comes close
• You want personality — Grok is genuinely funny and opinionated in a way other AI tools aren’t. If you find ChatGPT sterile, Grok feels like talking to a smart, slightly unhinged friend
• Unfiltered responses — Grok has fewer content guardrails than competitors, which can be useful for certain research and creative tasks (and occasionally problematic)
• You’re already paying for X Premium — if you’re on X Premium ($8/mo) or Premium+ ($22/mo), Grok is included at no extra cost

The Elephant in the Room: Value

DeepSeek is free. Grok requires X Premium ($8-22/month). For pure AI capability, there’s no question — DeepSeek delivers more for less. But Grok isn’t really competing on raw capability. Its value proposition is the X integration, the personality, and the willingness to engage with topics other AIs won’t touch.

They’re less competitors and more complementary tools for different moments.

Pricing Comparison

The Bottom Line

For raw AI capability — coding, analysis, writing, reasoning — DeepSeek is the better tool and it’s free. For real-time social intelligence, unfiltered conversations, and a genuinely entertaining AI personality, Grok occupies a unique niche that no other AI currently fills. If budget is a factor at all, DeepSeek is the clear winner.

Last updated: February 2026. We re-test every major model update. Subscribe to our newsletter for the latest comparison data.

Considering other options? Check out our comparisons: ChatGPT vs Grok, ChatGPT vs DeepSeek, Claude vs DeepSeek, DeepSeek vs Gemini.