Is Grok Better Than ChatGPT?

By Mike Levin

The AI Addiction Strategy

I think the strategy of the AI service providers is to addict you just enough to create an AI-dependency and then to squeeze you just enough to get you to pay. No single company holds all the AI-cards, especially with the scorched earth strategy initiated by Facebook/Meta with open source AI models like Llama to undercut the value of proprietary AI services and wash away OpenAI’s early mover advantage. But scorched earth AI strategies don’t stop with open source models. They stop in Tennessee.

Elon’s Scorched Earth Approach with Grok 3

The second act of scorched earth is Elon Musk/xAI’s giving away of formidable Grok 3 services — until the servers melt, as Elon put it. It further undercuts the paid services as this article clearly demonstrates. The free Grok 3 service can do what requires the paid ChatGPT service.

I don’t expect that will last forever. Eventually, xAI is going to have to get people to pay. But unlike OpenAI that has to rent their compute from a cloud provider (Microsoft Azure), Grok 3 has that colossal datacenter in Memphis Tennessee and it’s theirs to give away free — until they can’t anymore because eventually stuff has to get paid for.

The “First Free, Then Fee” Business Model

So, it’s a landscape of “first does free” with AI vendors hoping to lock you in with differentiating features and habits that are hard to change, and then ratchet-up the cost on you while degrading the quality of the cheap-tier (like the slower responses in Cursor) when switching costs are high.

It’s worth noting that all the VSCode AI forks & plugins like Windsurf and Cline are copying the user experience nuances of Cursor, so maybe the habit lock-in isn’t so bad. In fact, this list as of last week includes VSCode itself as Microsoft wakes up and throws in CoPilot with Agentic mode to VSCode for free!

Head-to-Head Comparison: Grok 3 vs ChatGPT

This article does a head-to-head comparison between Grok 3’s response and ChatGPT’s response to something of a mega-query for SEO where I pull the data for the top gainers and losers for this site through Google Search Console. The program analyzes 6 consecutive days of search performance data, processing thousands of page/query combinations (3,923 unique combinations in this case) to identify the top 100 performers and decliners across multiple metrics. It looks at impression trends, position changes (rankings), and calculates impact scores that combine both visibility and ranking performance.

For each page/query combination, it creates time-series data showing day-by-day changes in impressions, clicks, and position, along with calculating trend slopes to identify the fastest movers in both positive and negative directions. This creates massive markdown tables showing detailed performance data that would typically overwhelm many AI models due to the sheer volume of structured data and the complexity of the analysis required to provide meaningful insights.

The Results: Free Grok 3 vs Paid ChatGPT

For the same given prompt, Grok 3 was able to do it in thinking mode with their free product. I had to use the paid ChatGPT Plus product for ChatGPT to handle. Additionally, their latest 4.5 model was not able to handle it and I had to use o1-mini-high. So does this mean Grok 3 is “better” than ChatGPT? Well, it was able to handle more for less.

The data for this prompt is enormous so I moved it to the bottom of the article so as to not scare you away, but I left the instructions that accompanied the data here so you have the context of their replies. First you’ll see Grok 3’s response because it was able to do it for free. Then you’ll see ChatGPT.

For anyone interested, the Google Search Console Winners & Losers query that produced this report is in the precursors folder of the Pipulate project.

Analysis Prompt

We start by showing you the prompt that both Grok 3 and ChatGPT had to respond to which went with a chunk of data so big you’d be running for the hills if I included it here (so I moved it to the bottom). Suffice to say it’s a winners and losers list on page/query combos from Google Search Console data with what’s needed to draw those little “sparkline” trend graphs in Excel.

[Analysis Parameters: Top 100 Results Per Category, 6-Day Trend Period]

Analyze the Google Search Console trend analysis output previously provided for the site sc-domain:mikelev.in (covering the period April 7-12, 2025). Based only on that data, provide a prioritized list of actionable traffic growth suggestions and loss mitigation strategies.

Your goal is to identify both opportunities and risks revealed by the trends, including broad strategic directions and specific content pieces. Structure your response to cover the following areas, ensuring each point includes specific examples from the data (pages, queries, metrics) and concrete recommended actions:

1. Top Movers Analysis (Gains & Losses):
   • Identify the most promising opportunities among pages/queries showing strong positive impression growth.
   • Highlight queries gaining significant impressions and improving in position simultaneously.
   • Critically analyze pages/queries showing concerning drops in impressions. What patterns emerge among the declining content?
   • Recommend specific actions to both capitalize on growth and mitigate concerning losses.
2. Position Changes Analysis:
   • Which pages making significant ranking jumps (large negative position_slope) represent the best targets for further optimization?
   • Identify content experiencing concerning ranking declines (large positive position_slope). Are there common characteristics among declining pages?
   • Are there thematic patterns among both the fastest climbers and steepest decliners that suggest broader content opportunities or risks?
   • Recommend actions to both consolidate ranking improvements and stop/reverse ranking slides.
3. High-Impact & Problem Area Analysis:
   • Based on the “High-Impact Queries” list, which content currently delivers the most value and how can it be reinforced?
   • Examine the “Low-Impact Queries” list to identify previously better-performing content that may need intervention.
   • Identify the top 1-3 individual page/query pairs representing: a) The largest “unrealized potential” (high impressions but poor ranking) b) The most concerning “performance deterioration” (significant drops in both impressions and position)
   • For each identified pair, clearly state the page, query, metrics, and trend direction.
   • Critically, based on analyzing both opportunity and problem areas, identify and recommend: a) The single most promising new article topic to pursue b) The single most important existing content piece to rescue/improve
   • Justify why these specific pieces deserve prioritization over other candidates.
4. Prioritized Strategic Recommendations:
   • Synthesize the findings from the above points, balancing growth opportunities with risk mitigation.
   • What are the top 2-3 overarching strategic priorities? Ensure these recommendations explicitly cover: a) Defensive priorities (stopping losses, rescuing declining content) b) Growth priorities (both broad themes and specific new content opportunities)
   • Justify these priorities with clear data trends from both positive and negative movement tables.
5. Recommendation Summary Table:
   • Conclude your response with a summary table consolidating the most important actionable recommendations.
   • Use the following columns: Page URL, Query, Current Trend, Key Observation, Recommended Action, Priority.
   • Include both growth opportunities and problem areas requiring intervention.
   • Assign priority (High, Medium, Low) based on both potential impact and urgency of intervention as revealed by the trend data.
   • Ensure the table captures recommendations for: a) Content showing positive momentum to amplify b) Content showing concerning declines to address c) New content opportunities to pursue d) Technical or structural improvements suggested by the patterns

Please ensure all recommendations are concrete and directly linked to the patterns observed in the provided GSC trend data output, including both positive and negative trends.

Grok 3’s Response

To be fair to the free version of Grok 3, it was not only able to handle this, but it was able to handle a data-sample TWICE this size, still under the free version. I cut it down to only 100 rows-per-table because of the usability of this article in showing the complete data-sample. But I could have pushed Grok even a lot farther, I think.

Below is a detailed analysis of the Google Search Console (GSC) trend data for sc-domain:mikelev.in over the 6-day period of April 7-12, 2025, focusing on actionable traffic growth suggestions and loss mitigation strategies. The response is structured according to the requested areas, with each recommendation grounded in specific data points from the provided tables.

1. Top Movers Analysis (Gains & Losses)

Promising Opportunities (Impression Growth)

• “llm-seo-software-development” | “python for seo”
  • Metrics: Impressions TS [61, 42, 0, 0, 0, 0], Slope -12.3 (indicating earlier high impressions), Position TS [83, 81, -, -, -, -], Slope -2.2.
  • Observation: Despite a recent drop to zero impressions, this page previously had strong visibility (61 to 42 impressions), suggesting sustained interest in Python-related SEO content.
  • Action: Optimize this page with updated content, additional examples, and stronger calls-to-action to recapture visibility and improve its ranking from position 81.
• “static-site-generator-ai-content-strategy” | “seo techniques”
  • Metrics: Impressions TS [0, 87, 64, 0, 0, 0], Slope -9.3, Position TS [-, 95, 93, -, -, -], Slope -2.6.
  • Observation: Significant impression spike (87 to 64) indicates high search interest, though the position remains poor (93).
  • Action: Enhance on-page SEO (e.g., keyword density, internal linking) and consider a dedicated article to target this high-demand query more effectively.

Queries Gaining Impressions and Improving Position

• “ai-agentic-mode-debugging” | “git cherry pick”
  • Metrics: Impressions TS [0, 21, 20, 0, 0, 0], Slope -2.4, Position TS [-, 75, 70, -, -, -], Slope -4.7.
  • Observation: Consistent impressions (21 to 20) with a position improvement from 75 to 70 show growing relevance.
  • Action: Expand this content with practical examples and tutorials to solidify its ranking gains and boost clicks.

Concerning Drops in Impressions

• “llm-seo-software-development” | “hamster vector”
  • Metrics: Impressions TS [6, 1, 0, 0, 0, 0], Slope -0.9, Position TS [40, 77, -, -, -, -], Slope +37.0.
  • Observation: A sharp decline from 6 to 1 impression, coupled with a significant position drop (40 to 77), suggests a loss of relevance or search intent mismatch.
  • Pattern: Many declining queries (e.g., “task rabbit” with impressions [9, 2, 0, 0, 0, 0]) are niche or oddly specific, potentially losing traction due to shifting user interests or algorithm changes.
• Action: Investigate why “hamster vector” declined (e.g., search intent, competition) and refresh the content with updated keywords or repurpose it to align with current trends.

Recommendations

• Growth: Prioritize optimizing “python for seo” and “seo techniques” by enhancing content depth and technical SEO to convert impressions into clicks.
• Mitigation: Address “hamster vector” by auditing its performance drop, potentially redirecting efforts to more stable, related queries like “python seo.”

2. Position Changes Analysis

Significant Ranking Jumps (Negative Position Slope)

• “llm-seo-software-development” | “webassembly dom”
  • Metrics: Position TS [124, 70, -, -, -, -], Slope -54.0, Impressions TS [1, 1, 0, 0, 0, 0].
  • Observation: A dramatic position improvement (124 to 70), though impressions remain low.
  • Action: Amplify this gain with targeted content updates (e.g., WebAssembly tutorials) to increase visibility and impressions.
• “llm-seo-software-development” | “next seo”
  • Metrics: Position TS [70, 57, -, -, -, -], Slope -12.7, Impressions TS [3, 14, 0, 0, 0, 0].
  • Observation: Position improved from 70 to 57 with a prior impression spike (14).
  • Action: Reinforce this page with fresh content and backlinks to sustain and build on the ranking improvement.

Concerning Ranking Declines (Positive Position Slope)

• “ai-agentic-mode-debugging” | “cherry-picking meaning”
  • Metrics: Position TS [-, 89, 181, -, -, -], Slope +92.0, Impressions TS [0, 1, 1, 0, 0, 0].
  • Observation: A severe drop from 89 to 181 indicates a significant loss of ranking power.
• “llm-seo-software-development” | “hamster vector”
  • Metrics: Position TS [40, 77, -, -, -, -], Slope +37.0, Impressions TS [6, 1, 0, 0, 0, 0].
  • Observation: Position worsened from 40 to 77, aligning with an impression drop.
• Pattern: Declining pages often involve niche or technical queries (e.g., “cherry-picking meaning,” “nixos”) that may lack consistent search volume or face increased competition.

Thematic Patterns

• Climbers: Technical SEO and coding-related queries (e.g., “next seo,” “git cherry pick”) are gaining traction, suggesting a broader opportunity in developer-focused content.
• Decliners: Quirky or highly specific queries (e.g., “hamster vector,” “task rabbit”) are slipping, indicating a risk in over-relying on niche topics without broad appeal.

Recommendations

• Consolidate Gains: Boost “webassembly dom” and “next seo” with additional resources and promotion.
• Reverse Slides: For “cherry-picking meaning” and “hamster vector,” analyze competitors and refresh content to better match current search intent.

3. High-Impact & Problem Area Analysis

High-Impact Queries (Reinforcement)

• “peak-data-musk-sutskever-wrong” | “peak data theory”
  • Metrics: Latest Position 4.4, Latest Impressions 45, Impact Score 68.8.
  • Observation: A top performer with excellent ranking and high impressions.
  • Action: Maintain its strength by regularly updating the content and promoting it via social media or newsletters.
• “open-source-seo-software” | “open source seo platform”
  • Metrics: Latest Position 24.6, Latest Impressions 49, Impact Score 67.1.
  • Observation: Strong visibility and decent ranking make this a valuable asset.
  • Action: Expand related content (e.g., tutorials, case studies) to deepen engagement.

Low-Impact Queries (Intervention)

• “llm-seo-software-development” | “ai user agent”
  • Metrics: Latest Position 190, Latest Impressions 1, Impact Score 0.1.
  • Observation: Poor position and minimal impressions suggest this was never a strong performer, but its presence indicates past intent to target AI-related SEO.
  • Action: De-prioritize unless strategic value emerges; redirect efforts to stronger AI/SEO queries.

Top Page/Query Pairs

• Largest Unrealized Potential
  • Page: “static-site-generator-ai-content-strategy” | Query: “seo techniques”
  • Metrics: Latest Position 93.1, Latest Impressions 64, Impressions TS [0, 87, 64, 0, 0, 0].
  • Trend: High impressions with poor ranking.
  • Action: Optimize for better ranking (e.g., keyword optimization, content expansion) to unlock significant traffic potential.
• Most Concerning Performance Deterioration
  • Page: “llm-seo-software-development” | Query: “hamster vector”
  • Metrics: Position TS [40, 77, -, -, -, -], Slope +37.0, Impressions TS [6, 1, 0, 0, 0, 0], Slope -0.9.
  • Trend: Sharp declines in both position and impressions.
  • Action: Investigate the drop (e.g., intent mismatch, algorithm shift) and either refresh or redirect to a related, stronger query.

Critical Recommendations

• Most Promising New Article Topic: “SEO Techniques”
  • Justification: High impressions (64-87) across “static-site-generator-ai-content-strategy” suggest strong demand, yet poor rankings (93) indicate an opportunity to capture traffic with a dedicated, well-optimized article. This aligns with the developer/SEO theme gaining traction.
• Most Important Existing Content to Rescue: “llm-seo-software-development” | “hamster vector”
  • Justification: Its significant deterioration (position 40 to 77, impressions 6 to 1) marks it as a critical loss of prior visibility. Rescuing it could recover traffic, and its technical nature ties into broader growth trends (e.g., Python/SEO).

4. Prioritized Strategic Recommendations

Top Strategic Priorities

1. Defensive Priority: Rescue Declining Niche Content
   • Data: Queries like “hamster vector” (position +37.0, impressions -0.9) and “cherry-picking meaning” (position +92.0) show significant declines.
   • Action: Audit and refresh these pages to align with current search intent, or repurpose them into broader, stable topics (e.g., “Python SEO tools”).
   • Justification: Prevents further erosion of traffic from previously viable content.
2. Growth Priority: Amplify Technical SEO and Coding Content
   • Data: Gains in “python for seo” (impressions 42-61), “next seo” (position -12.7), and “git cherry pick” (position -4.7) highlight a developer audience.
   • Action: Create a content series or hub around SEO and programming (e.g., Python, Git), leveraging existing momentum.
   • Justification: Capitalizes on consistent impression and position improvements in this niche.
3. Growth Priority: Pursue High-Demand, Low-Ranking Opportunities
   • Data: “seo techniques” (impressions 64-87, position 93) offers untapped potential.
   • Action: Develop new, authoritative content targeting this query to secure top rankings.
   • Justification: High impressions signal strong user interest ripe for conversion.

5. Recommendation Summary Table

Table Notes

• Growth Opportunities: “seo techniques” and “python for seo” leverage high impressions and emerging trends.
• Declining Content: “hamster vector” and “cherry-picking meaning” require urgent intervention due to significant losses.
• New Content: A dedicated “seo techniques” article targets unrealized potential.
• Technical Improvements: Implicit in optimization suggestions (e.g., keyword updates, intent alignment).

Conclusion

The GSC data reveals a dual focus: capitalizing on technical SEO and coding-related growth (e.g., “python for seo,” “seo techniques”) while addressing sharp declines in niche content (e.g., “hamster vector”). By prioritizing these actions, mikelev.in can both amplify emerging strengths and mitigate critical losses, aligning with user demand and search trends observed from April 7-12, 2025.

ChatGPT’s Response

ChatGPT 4.5 is the premium model, right? But even in the ChatGPT Pro paid level, their top model can’t handle a prompt this big. I had to, as is my habit in most things like this recently, go down to the o3-mini-high model which is no slouch. However, there’s something vaguely dissatisfying when the leading edge model has a smaller context-window. Smarter but smaller?

Below is a detailed analysis of the trends observed in the April 7–12, 2025 Google Search Console data for the mikelev.in domain, along with concrete recommendations to both build on upward momentum and arrest drops. All recommendations are drawn solely from the provided tables and metrics.

1. Top Movers Analysis (Gains & Losses)

Promising Opportunities

• High-Impact Growth Example:
  The query “peak data theory” (page: peak-data-musk-sutskever-wrong) shows a very attractive impact score (68.8) with its latest position at about 4.4 and 45 impressions. This implies that the topic is resonating strongly and already ranks near the top of the search results.
  Action: Deepen this content by creating additional resource articles (or even a cluster of related topics) to cement authority around emerging “data theory” ideas and leverage internal links from related pages.

• Emerging Success in Open Source SEO:
  Multiple entries on pages such as “open-source-seo-software” and the associated query “open source seo platform” (with examples showing positions around 16–25 and impression counts in the 28–49 range) indicate that this subject is attractive to your audience.
  Action: Expand and refresh these pages with updated case studies, tool comparisons, and technical how‐to guides. Consider both enhancing existing content and creating a new comprehensive hub on open source SEO.

Concerning Losses

• Steep Ranking Drops:
  The query “webassembly dom” on the page llm-seo-software-development is especially worrisome: the data shows a dramatic position decline (from a very weak rank near 124 to a better “70” in the trend, with a position slope of –54.0). This “slope” (negative change in ranking quality) suggests that the content has lost traction relative to competing pages.
  Similarly, “cheek holes” (also on llm-seo-software-development) exhibits a −28.0 change, reinforcing that some technical or topical issues might be causing a loss in visibility.
  Action: Conduct a full content and technical SEO audit of these pages. Update or restructure the content, review keyword targeting, and check for any technical barriers (such as slow loading, meta mis-optimization, or outdated information) that may be hindering rankings.

2. Position Changes Analysis

Pages Climbing in Rank

• Best Ranking Improvement:
  The query “ai first seo” on https://mikelev.in shows a strong improvement with a current position around 4.4 (and little to no opposing change in impressions). Even modest improvements that push a page into the top five merit further amplification.
  Action: Reinforce these pages by adding related “next-step” content and optimizing internal links so that other pages can benefit from this upward momentum.

Pages Declining in Rank

• Worrying Rank Declines:
  Notably, several entries from the llm-seo-software-development hub (e.g., “webassembly dom” and “cheek holes”) have suffered significant ranking declines. Although the impression counts for these queries are low, the steep fall in ranking indicates that they have either been outcompeted or the content may no longer match user intent.
  Action: Reassess these topics. It is advisable either to overhaul the content with more up‑to‑date examples and technical explanations or to consolidate them with better–performing content if the topic is too niche or misaligned.

Thematic Patterns

• Opportunities vs. Risks:
  Positive movers tend to be covering emerging or technical topics (such as “peak data theory” and “open source seo platform”), suggesting that there is an audience for forward‑thinking SEO and technology strategies. Conversely, declining content appears concentrated on specific segments of the llm-seo-software-development repository, indicating potential issues with either outdated guides or highly technical subjects that competitors may be addressing better now. Action: Use the positives as models to upgrade other technical pages—replicating successful formatting, depth, and keyword targeting. For the negatives, consider whether the topic merits a complete rewrite or should be de‑prioritized in favor of related, more successful subjects.

3. High-Impact & Problem Area Analysis

Reinforcing High-Impact Content

• Top Value Content:
  The “peak data theory” query is currently delivering top rankings and a high impact score. In addition, pages centered on “open source seo platform/software” are showing traction across different tables.
  Action: Strengthen these pages by integrating data-rich insights, expert interviews, and updated case studies. Use these successes as anchors for building broader topic clusters.

Rescuing Underperforming Content

• Most Concerning Case:
  The “webassembly dom” query on llm-seo-software-development is the most extreme case of performance deterioration (position slope –54.0). Despite low overall impressions, such a steep decline signals internal issues—either outdated practices, weak optimization, or a mismatch with current search intent.
  Action: Prioritize a full audit and subsequent content refresh. Compare competitor content and address any gaps in depth, technical details, and clarity.
• Additional Target for Rescue:
  A repeated theme of low-impact queries appears in the “Low-Impact Queries” list (for example, “ai user agent” and others from grok-better-than).
  Action: Consider consolidating similar low-performing queries into a single, more authoritative piece if they address overlapping topics.

Specific Identified Pairs

• Unrealized Potential:
  Page/Query Pair:
  • Page: peak-data-musk-sutskever-wrong
  • Query: “peak data theory”
  • Latest Metrics: Position ~4.4, Impressions 45, Impact Score 68.8
    Opportunity: Already ranking very high, now is the time to extend this success into related content areas.
• Performance Deterioration:
  Page/Query Pair:
  • Page: llm-seo-software-development
  • Query: “webassembly dom”
  • Latest Metrics: Declined from around position 124 to 70, with a –54.0 position slope
    Opportunity: Immediate remediation is needed to arrest further losses.

New Article vs. Rescue Priority

• Most Promising New Article Topic:
  Given the high interest and solid performance of emerging data topics, a new in-depth article titled “Peak Data Theory: Unlocking the Future of Open Source SEO” is strongly suggested. This article would merge insights from both the “peak data” and “open source SEO” themes and serve as a central resource.
• Most Important Existing Content to Rescue:
  The “webassembly dom” piece on llm-seo-software-development is the single most concerning piece because of its drastic ranking loss. It should be immediately reviewed and optimized to reverse the decline.

4. Prioritized Strategic Recommendations

Defensive Priorities (Stopping Losses)

1. Audit & Refresh Declining Content:
   • Focus Areas:
     Pages on llm-seo-software-development—especially those covering queries like “webassembly dom” (–54.0 slope) and “cheek holes” (–28.0 slope).
   • Actions:
     • Perform a thorough content audit.
     • Update information to reflect current standards and user intent.
     • Improve technical SEO (meta tags, internal links, multimedia, etc.).
   • Justification: The severe ranking drop indicates that outdated or misaligned content is being overtaken by competitors.
2. Consolidate Low-Performing Queries:
   • Focus Areas:
     Low-impact queries (e.g., those from the bottom 20 list such as “ai user agent” and various vaguely defined terms).
   • Actions:
     • Decide whether to merge related topics or remove duplicate/minimal content.
     • Re-target with clear, updated keyword strategies.

Growth Priorities (Boosting Opportunities)

1. Capitalize on High-Impact Trends:
   • Focus Areas:
     Pages with strong performance such as those targeting “peak data theory” and “open source seo platform”.
   • Actions:
     • Expand these topics through comprehensive guides, case studies, and resource hubs.
     • Increase internal and external linking to these pages.
   • Justification: These topics already show excellent traction, meaning further investment is likely to drive sustained traffic growth.
2. Develop New High-Value Content:
   • New Article Topic:
     “Peak Data Theory: Unlocking the Future of Open Source SEO”
   • Actions:
     • Research and develop detailed, authoritative content covering emerging trends in data theory as they relate to SEO.
     • Integrate insights from current high-impact queries and update regularly to maintain authority.
3. Technical & On-Page SEO Enhancements:
   • Focus Areas:
     Across all pages, but especially where ranking improvements are starting to appear.
   • Actions:
     • Refresh meta descriptions, heading structures, and content formatting.
     • Ensure that pages are well interlinked to help pass authority from high-performing pages to recovering ones.

5. Recommendation Summary Table

Final Strategic Priorities

1. Defensive Action:
   Immediately audit and rescue the content on llm-seo-software-development that is experiencing steep ranking declines (notably “webassembly dom” and “cheek holes”). This defensive work is crucial to stop further losses.

2. Growth Expansion:
   Capitalize on topics with high momentum such as “peak data theory” and “open source seo platform” by developing additional detailed resources and connecting related content through a smart internal linking strategy.

3. New Content Creation & Technical Optimization:
   Launch a new, high-value article (e.g., “Peak Data Theory: Unlocking the Future of Open Source SEO”) that leverages current trends, and continue to refine on‑page SEO elements across all high–impact pages to maximize both visibility and traffic conversion.

By focusing your efforts on both defending your declining content and further exploiting the emerging opportunities, you can effectively balance risk mitigation with aggressive traffic growth.

This prioritized set of recommendations—backed by specific examples and metrics—offers a clear roadmap to optimize your overall search performance over the short and medium term.

The Data From The Prompt

The below data was actually part of the same prompt that I open the article with, but I moved it to the bottom so as to not scare away visitors. You are essentially done with the article by this point unless you want to see the full context of what Grok 3 and ChatGPT o1-mini-high were analyzing.

Top 20 by Impression Increase

Top 20 by Impression Decrease

Top 20 by Position Improvement (Lower is Better)

Top 20 by Position Decline (Higher is Worse)

Top 20 High-Impact Queries (Best Position + Most Impressions)

Bottom 20 Low-Impact Queries (Poor Position + Few Impressions)

Gemini 2.5’s Take

In an article where I pit Grok 3 against ChatGPT, I have the article summarized and critiqued by Gemini, haha! Honestly, Gemini 2.5 is probably the smartest out there right now, and I know because I do stuff like this analysis all the time, hopping around from one model to the next. But the interest out there is in Grok vs. ChatGPT. Poor, slighted Gemini.

Okay, here are the requested outputs based on the provided article text:

• Title/Headline Ideas:
  • Grok 3 vs. ChatGPT Plus: A Head-to-Head SEO Analysis Showdown
  • Free vs. Paid AI: Testing Grok and ChatGPT on Complex Google Search Console Data
  • Can Free AI Handle Heavy Lifting? Grok 3 Outperforms ChatGPT on Mega SEO Query
  • AI Battle for SEO Insights: Grok 3 vs. ChatGPT Analysis of GSC Trends
  • Decoding AI Value: Comparing Grok 3 and ChatGPT on a Demanding SEO Task
• Strengths:
  • Practical Relevance: Addresses a real-world, complex task (detailed SEO analysis) relevant to marketers and developers.
  • Direct Comparison: Provides a clear head-to-head comparison between two major AI models on the same specific task.
  • Transparency: Includes the full prompt and the complete responses from both AI models, allowing readers to see the outputs directly.
  • Contextual Framing: Links the specific test case to broader trends in the AI market (competition, pricing strategies, open source).
  • Clear Structure: The article and the AI analysis prompt are well-structured, making the methodology and findings easy to follow.
• Weaknesses:
  • Single Test Case: The conclusion about relative capability is based on one specific, albeit complex, prompt. Generalizing performance broadly might be misleading.
  • Author’s Framing: The introductory commentary about AI provider strategies (“addict,” “squeeze”) sets a potentially biased frame, although the core comparison seems factually presented based on the described outcome.
  • Data Accessibility: While mentioning the large data is moved to the end, not having it directly interleaved makes independent verification harder for the reader within the flow of the article.
  • Focus on Cost/Capability: The comparison focuses primarily on whether the AI could perform the task and the associated cost, rather than a deep dive into the qualitative nuances or accuracy differences between the two successful responses (Grok free vs. paid ChatGPT).
• AI Opinion: This article provides a valuable and practical comparison between Grok 3 and ChatGPT focused on a demanding, real-world SEO analysis task. Its strength lies in the clear methodology, the inclusion of full AI responses, and the contextualization within the current AI competitive landscape, particularly highlighting the free vs. paid capability threshold. While based on a single prompt, it offers useful insights for users evaluating AI tools for complex data analysis, especially concerning the cost implications. The clarity of the setup and the structured AI responses make it a helpful resource for understanding practical differences between these services.