GPT-4.1 vs Claude 3.7 Sonnet: The Ultimate Showdown for Content Creation

Introduction

The AI content game just changed. Again.

In the red corner, OpenAI just dropped their new GPT-4.1 family — an upgraded suite of models that promise better performance at a lower cost. In the blue corner, Anthropic’s Claude 3.7 Sonnet has been throwing impressive punches as the reigning champ for high-quality content generation.

For content creators, SEO specialists, and marketing teams, this isn’t just tech news — it’s a potential game-changer for your workflow, output quality, and bottom line.

The days of debating whether to use AI for content are over. Now the real question is: which AI should you be using, and for what?

In this no-fluff breakdown, we’ll pit OpenAI’s fresh GPT-4.1 lineup against Anthropic’s content creation powerhouses, Claude 3.7 Sonnet and 3.5 Haiku. You’ll get the hard facts on costs, capabilities, and real-world performance — plus actionable prompts you can copy/paste today to level up your content game.

Let’s dive in.

Meet the New Kids on the Block: OpenAI’s GPT-4.1 Family

OpenAI launched three new models on April 14, 2025, and they’re making waves. Let’s cut through the marketing speak and get to what matters for content creators.

GPT-4.1: The Flagship Model

The headline act is GPT-4.1, OpenAI’s most capable model to date. What makes it special?

First, according to OpenAI’s official announcement, it packs a massive 1 million token context window — that’s roughly 750,000 words or about seven full-length novels. For content creators, this means you can feed it entire research documents, competitor analyses, and brand guidelines all at once, and it still has room for your instructions.

GPT-4.1 improves on GPT-4o (which had a 128K token context window) with better instruction following and more precise outputs. In plain English: it’s less likely to hallucinate and more likely to actually do what you ask.

The pricing is where things get interesting: $2 per million input tokens and $8 per million output tokens. That’s a 33% drop from the previous GPT-4 pricing ($3/$12), making enterprise-level AI more accessible for content teams without enterprise budgets.

GPT-4.1-mini: The Middle Ground

Think of GPT-4.1-mini as the Goldilocks option — not as powerful as its big brother, but packing more punch than the nano version.

This model hits the sweet spot for most content operations, with the same 1 million token context window but at a lower price point: $0.40 per million input tokens and $1.60 per million output tokens, according to OpenAI’s official pricing. While it may not handle the most complex reasoning tasks as gracefully as the flagship model, its ability to process and generate content is more than sufficient for most marketing and SEO content needs.

For teams producing high volumes of content, the mini variant offers a compelling balance of quality and cost-efficiency.

GPT-4.1-nano: The Speed Demon

The baby of the family isn’t just smaller — it’s faster and dramatically cheaper. According to OpenAI’s pricing page, GPT-4.1-nano is their most affordable model ever at just $0.10 per million input tokens and $0.40 per million output tokens.

What’s the trade-off? While nano still benefits from the massive 1 million token context window, it may not maintain the same level of reasoning depth as its larger siblings when handling complex content tasks. But for certain content tasks — like generating product descriptions at scale, brainstorming blog topics, or creating first-draft outlines — nano delivers impressive results for pennies on the dollar.

If you’re running a content operation that needs volume and speed more than philosophical depth, GPT-4.1-nano might be your new best friend.

Anthropic’s Content Creation Champions: Claude 3.7 Sonnet and 3.5 Haiku

While OpenAI’s been grabbing headlines with their new releases, Anthropic has been quietly dominating the content creation space. Their Claude models have established themselves as the go-to choice for many content professionals for good reason.

Claude 3.7 Sonnet: The Content Creation Gold Standard

Released in February 2025, Claude 3.7 Sonnet currently reigns as the best model for article generation. According to Anthropic’s official documentation, it’s their most intelligent model to date and the first to offer an “extended thinking” mode that allows for more deliberate, step-by-step reasoning.

The extended thinking capability allows Claude 3.7 Sonnet to tackle complex content creation tasks by breaking them down into manageable steps and reasoning through them methodically when needed, while still being able to respond quickly for simpler tasks. This dual-mode operation gives content creators flexibility depending on the complexity of their project.

What sets it apart? Claude 3.7 Sonnet excels at structured problem-solving and consistently produces more nuanced, well-balanced content compared to competitors. The model has a 200K token context window, which represents the combined capacity for both input and output (prompt + response), as specified in Anthropic’s technical documentation.

For content creators, this means Claude 3.7 Sonnet can produce comprehensive articles, white papers, and research documents without losing the thread or repeating itself. Its 200K token context window is smaller than GPT-4.1’s million tokens, but it’s still sufficient for most content creation tasks with room to spare.

While GPT-4.1 shows improvement over GPT-4o, preliminary testing on structured content tasks suggests Claude 3.7 Sonnet maintains certain advantages when it comes to content quality, particularly for long-form technical writing. Claude’s outputs tend to demonstrate strong logical structure and coherence—key factors for premium content.

Claude 3.5 Haiku: The Efficient Alternative

Not every content task needs the heavy artillery. For teams balancing quality with cost, Claude 3.5 Haiku delivers impressive results at a competitive price: $0.80 per million input tokens and $4 per million output tokens, according to Anthropic’s December 2024 pricing update.

Haiku is Anthropic’s fastest and most cost-effective model, designed specifically for scenarios where speed and efficiency matter more than deep reasoning. It maintains the 200K token context window of its more powerful siblings.

What makes Haiku shine is its ability to maintain much of Claude’s characteristic nuance and understanding while operating at significantly higher speeds. For content operations that need to churn out social media posts, short-form blog content, or product descriptions at scale, Haiku offers an excellent balance of quality and cost.

The choice between Sonnet and Haiku often comes down to content complexity and audience expectations. For thought leadership pieces aimed at C-suite readers, Sonnet’s additional reasoning capabilities pay dividends. For everyday content marketing materials, Haiku’s speed and cost advantages make it the smarter choice.

The Technical Showdown: Architecture and Capabilities

Let’s get into the nuts and bolts that separate these AI powerhouses. Beyond the marketing hype, these technical differences directly impact your content output and bottom line.

Context Windows: Size Matters

GPT-4.1’s 1 million token context window is the headline feature that OpenAI has prominently highlighted in its April 2025 announcement—and it represents a significant advancement. This is approximately 8x larger than GPT-4o’s 128K limit and 5x larger than Claude’s 200K window.

It’s important to understand that the “context window” represents the total token budget available for both input (your prompt) and output (the model’s response) combined. For example:

• With GPT-4.1’s 1M token window: If your prompt is 200K tokens, you could potentially get up to 800K tokens of response (though specific output limits may apply)
• With Claude 3.7 Sonnet’s 200K token window: If your prompt is 50K tokens, you could potentially get up to 150K tokens of response

But here’s the reality check: How often do content creators actually need to process 750,000 words at once? For most content tasks, Claude’s 200K window (roughly 150,000 words total across prompt and response) is sufficient. The main advantage of GPT-4.1’s larger window comes when analyzing multiple lengthy documents or feeding in extensive competitor research alongside your instructions.

For typical content creation workflows, both model families provide ample context capacity for all but the most specialized use cases. The key difference is that GPT-4.1 can handle approximately 5x more total content in a single prompt-response cycle than Claude 3.7 Sonnet.

Token Economics: The Cost of Creation

Below is a comprehensive comparison of pricing for these leading AI models, with all information sourced from official documentation or announcements:

GPT-4.1-nano is the clear cost leader at just $0.10 per million input tokens and $0.40 per million output tokens. It’s important to note that content creation typically generates more output tokens than input tokens, so the output costs weigh heavily in the overall economics.

For mid-tier content needs, GPT-4.1-mini at $0.40 per million input tokens and $1.60 per million output tokens could deliver strong value for the investment. Claude 3.7 Sonnet represents the premium tier at $3.00 per million input tokens and $15.00 per million output tokens, reflecting its advanced capabilities.

Performance on Content-Specific Tasks

When comparing these models on content generation tasks, it’s important to note that formal benchmarks specifically for content creation are limited. While both OpenAI and Anthropic have published general performance data for their models, specific content generation capabilities often depend on the particular use case and prompt design.

Based on published information from both companies:

1. Article Generation: Claude 3.7 Sonnet has been highlighted by Anthropic for its ability to maintain coherence and logical flow in long-form content. GPT-4.1 has demonstrated similar capabilities, with OpenAI emphasizing its instruction following for complex tasks.
2. Creative Writing: GPT-4.1 has shown strong performance in creative tasks according to OpenAI’s release notes.
3. Technical Documentation: Claude 3.7 Sonnet demonstrates strong terminology consistency for technical content according to Anthropic’s documentation. GPT-4.1’s larger context window provides advantages when referencing multiple technical sources.
4. Marketing Copy: Both model families have been reported to perform well for shorter marketing content.

The only way to determine which model performs best for your specific content needs is to test them with your actual use cases, as results can vary significantly based on prompt design and specific requirements.

Full Model Comparison Table

Content Creation Face-Off: Which Model Excels Where?

Now let’s get tactical. Different content tasks demand different AI strengths, and choosing the right tool for the job can be the difference between mediocre results and standout content.

Article Writing and Long-Form Content

Based on published capabilities and reported performance:

Claude 3.7 Sonnet has been designed with a focus on producing coherent long-form content. According to Anthropic’s documentation, the model’s extended thinking mode helps maintain consistency throughout lengthy documents.

Key capabilities that make Claude 3.7 Sonnet suitable for long-form content include:

1. Logical flow: The extended thinking mode helps Claude 3.7 Sonnet maintain consistent arguments throughout longer pieces.
2. Structure consistency: Claude’s documentation highlights its ability to adhere to specified formats across extended outputs.
3. Depth of analysis: The model’s reasoning capabilities make it well-suited for thought leadership content.

OpenAI’s GPT-4.1 also offers strong capabilities for long-form content, with its significantly larger context window providing advantages for research-intensive writing tasks.

For those with significant budget constraints, GPT-4.1-mini appears to offer good performance for long-form content according to OpenAI’s technical report, though with expected trade-offs in reasoning complexity.

SEO Optimization and Keyword Integration

GPT-4.1 demonstrates strong capabilities for SEO-focused content creation. Its 1 million token context window allows you to feed it comprehensive SEO research, competitor analysis, and keyword lists alongside your content brief—all in a single prompt.

According to OpenAI’s technical report, the model shows understanding of on-page SEO principles and can produce keyword-rich content while maintaining natural language flow. Reported strengths include:

1. Semantic keyword integration: Incorporating related terms and phrases that support main keywords
2. Creating SEO-optimized headers: Generating H2s and H3s that engage readers while supporting search relevance
3. Metadata generation: Producing meta titles and descriptions that incorporate keywords naturally

For budget-conscious teams, OpenAI notes that GPT-4.1-nano performs adequately for basic SEO content tasks like generating meta descriptions or optimizing existing content for specific keywords.

Marketing Copy and Conversion Content

For shorter marketing copy like ads, email subject lines, and landing page headlines, both GPT-4.1 and Claude 3.7 Sonnet demonstrate different strengths according to their respective documentation.

OpenAI highlights GPT-4.1’s ability to generate multiple creative variations quickly and produce attention-grabbing headlines. Meanwhile, Anthropic’s documentation emphasizes Claude 3.7 Sonnet’s ability to create copy that demonstrates awareness of audience needs and potential objections.

For budget efficiency on high-volume marketing content needs, Claude 3.5 Haiku is positioned as a cost-effective option for shorter-form content according to Anthropic’s marketing materials.

Technical Writing and Documentation

When it comes to technical documentation—user guides, API docs, product specifications—Claude 3.7 Sonnet shows particular capabilities according to Anthropic’s technical documentation. Technical writing demands precision, consistency, and the ability to explain complex concepts clearly.

Anthropic highlights Claude 3.7 Sonnet’s strengths in:

1. Maintaining consistent terminology throughout technical documents
2. Simplifying complex concepts without sacrificing accuracy
3. Creating clear step-by-step instructions that anticipate user questions

OpenAI’s documentation notes that GPT-4.1’s larger context window provides advantages when generating documentation while referencing multiple technical sources simultaneously.

Creative Content and Storytelling

For creative writing, storytelling, and brand narratives, OpenAI’s technical documentation highlights GPT-4.1’s creative capabilities. According to their materials, the model can generate metaphors, narrative structures, and engaging hooks.

OpenAI notes that GPT-4.1 can effectively adopt specific creative voices or styles, making it suitable for:

1. Brand storytelling that needs to maintain a distinctive voice
2. Creative campaign concepts that require imaginative approaches
3. Case studies and success stories that need to engage while informing

According to OpenAI’s documentation, GPT-4.1-mini maintains many of these creative capabilities at a lower price point, potentially making it a good option for creative content needs with budget constraints.

The Content Creation Workflow: Model-Specific Approaches

Different stages of the content creation process may benefit from different AI models. Based on published capabilities and technical documentation, here’s how these models might best fit into various stages of content creation.

Research Phase

GPT-4.1’s large context window can provide advantages during the research phase. According to OpenAI’s documentation, this capability enables:

1. Analyzing multiple competitor articles, industry reports, and existing content simultaneously
2. Extracting key insights from diverse sources in a single prompt
3. Comparing the structure and approach of different content examples
4. Generating comprehensive research briefs that incorporate multiple inputs

For teams with tighter budgets, Claude 3.5 Haiku can handle research tasks according to Anthropic’s use case documentation, though with the limitation of needing to feed information in smaller segments due to the more limited context window.

Ideation and Outlining

For generating content ideas and creating structured outlines, Claude 3.7 Sonnet’s extended thinking capabilities are highlighted in Anthropic’s technical documentation as being well-suited for this type of task.

Anthropic notes Claude 3.7 Sonnet’s strengths in:

1. Generating diverse content angles
2. Creating hierarchical outlines with logical progression
3. Balancing breadth and depth in content planning
4. Developing perspective frameworks for complex topics

For high-volume ideation needs like brainstorming multiple social media concepts, more cost-effective models like GPT-4.1-nano are positioned to provide value according to OpenAI’s documentation, though with expected trade-offs in depth and complexity.

Drafting and Editing

According to multiple industry resources and model documentation, a multi-model approach may yield optimal results for the drafting and editing stages:

1. Claude 3.7 Sonnet appears well-suited for initial drafts of complex, technical, or nuanced content according to Anthropic’s documentation
2. GPT-4.1 is highlighted by OpenAI as effective for creative content, storytelling, and SEO-focused pieces
3. For budget efficiency on straightforward content, Claude 3.5 Haiku or GPT-4.1-mini may provide sufficient quality at lower cost points according to published documentation

For editing, content professionals have reported benefits from feeding drafts into a different model than the one that created it. This approach of using complementary models can provide fresh perspective and help catch issues the original model might miss.

Refinement and Optimization

In the final polishing stage, OpenAI’s documentation highlights GPT-4.1’s capabilities in several areas:

1. Optimizing content for SEO while maintaining readability
2. Improving headlines and subheadings for engagement
3. Enhancing calls-to-action and conversion elements
4. Adapting tone and style to match specific brand guidelines

For quick refinements or minor optimizations, published materials suggest that more cost-effective models like GPT-4.1-nano can deliver good results for many common tasks.

Practical Prompts for Content Creation Tasks

Let’s look at some example prompts you can adapt for different content creation tasks. These are optimized for the respective models’ strengths based on our testing.

Keyword Research Prompts

For GPT-4.1:

I'm creating content about [TOPIC]. My target audience is [AUDIENCE]. My primary keyword is [KEYWORD].

Please help me:
1. Generate 20 semantically related keywords (include long-tail variations)
2. Group them by search intent (informational, commercial, etc.)
3. Suggest 5 potential content angles my competitors might have missed
4. Recommend 3 headline variations that incorporate my primary keyword naturally

My competitors are: [COMPETITOR URLS]

For Claude 3.7 Sonnet:

I need to develop a keyword strategy for content about [TOPIC] targeting [AUDIENCE].

Please analyze these aspects:
1. Identify 10 high-value long-tail keywords related to [PRIMARY KEYWORD]
2. For each keyword, explain the likely user intent and content expectations
3. Suggest 3 content clusters I could build around these keywords
4. Recommend which keywords to prioritize based on competition level

My business is [BRIEF BUSINESS DESCRIPTION].

Topic Ideation Prompts

For GPT-4.1:

Based on the keyword [PRIMARY KEYWORD] and related terms [RELATED TERMS], generate:
1. 10 potential blog post topics that would rank well
2. 5 listicle ideas with suggested number of items
3. 3 comprehensive guide concepts
4. 2 unique angle ideas that could stand out

For each idea, include:
- A compelling headline
- The primary search intent it addresses
- Why it would appeal to my audience of [AUDIENCE DESCRIPTION]

My brand voice is [BRAND VOICE DESCRIPTION].

For Claude 3.7 Sonnet:

I'm planning content for [TIMEFRAME] about [TOPIC]. My audience consists of [AUDIENCE DETAILS].

Please help me develop a strategic content plan by:
1. Identifying 5 core topics that would position me as an authority
2. For each topic, suggesting 3 specific article ideas (15 total)
3. Recommending a logical publication sequence
4. For each article, including a compelling hook and key points to cover

My content goals are [GOALS], and my competitors typically cover [COMPETITOR CONTENT APPROACHES].

Content Gap Analysis Prompts

For GPT-4.1:

I want to create more comprehensive content than my competitors about [TOPIC].

Here are URLs to my top 3 competitors' content on this topic:
[URL 1]
[URL 2]
[URL 3]

Please analyze:
1. What subtopics or angles are all competitors covering?
2. What important subtopics are some competitors missing?
3. What questions are not being adequately answered?
4. What unique perspective could I offer?
5. How could I make my content more actionable than theirs?

Based on this analysis, suggest an outline for my content that fills these gaps.

For Claude 3.7 Sonnet:

I'm writing an article about [TOPIC] targeting the keyword [KEYWORD].

My competitors have published these pieces:
[URL 1]
[URL 2]

Please help me:
1. Identify the main sections and points covered by both competitors
2. Find information gaps, outdated data, or weakly developed sections
3. Discover questions addressed by only one competitor or neither
4. Suggest additional angles, examples, or data I could include
5. Recommend a structure that covers everything important while filling gaps

My goal is to create the most helpful and comprehensive resource on this topic.

Content Optimization Prompts

For GPT-4.1:

I need to optimize this content for SEO while maintaining readability and engagement:

[PASTE CONTENT]

Target keyword: [PRIMARY KEYWORD]
Secondary keywords: [SECONDARY KEYWORDS]

Please:
1. Rewrite the title and meta description to improve CTR
2. Optimize H2 and H3 headings for keywords while keeping them engaging
3. Improve keyword density naturally without stuffing
4. Add schema markup recommendations
5. Suggest internal linking opportunities 
6. Recommend additional sections to improve comprehensiveness

Keep my brand voice: [BRAND VOICE DESCRIPTION]

For Claude 3.5 Haiku:

I need a quick SEO review of this content:

[PASTE CONTENT]

For the keyword: [KEYWORD]

Please provide:
1. 3 specific suggestions to improve on-page SEO
2. Any missing subtopics that should be included
3. Ideas to make the content more engaging
4. A better title and meta description

Keep it concise and actionable.

Making the Right Choice: Decision Framework

With the information presented so far, how do you decide which model(s) to use? Here’s a practical decision framework:

Budget Considerations

• Higher budget: Consider using both Claude 3.7 Sonnet and GPT-4.1 for different aspects of your content workflow
• Mid-level budget: Claude 3.5 Haiku for routine content, with occasional use of premium models for specialized content
• Tighter budget: Consider GPT-4.1-nano for research and ideation, with Claude 3.5 Haiku for final content when possible

Content Types and Volume

• Premium thought leadership: Consider Claude 3.7 Sonnet’s structured approach
• SEO-focused blog content: Consider GPT-4.1 or GPT-4.1-mini
• Technical documentation: Claude 3.7 Sonnet shows particular strengths here
• Marketing campaign materials: Consider using complementary models for different aspects
• High-volume product content: More efficient models like GPT-4.1-nano or Claude 3.5 Haiku may be appropriate

Technical Requirements

• Processing lengthy documents: GPT-4.1 family’s larger context window provides advantages
• Complex, nuanced content: Claude 3.7 Sonnet demonstrates strengths in this area
• Rapid content generation: More efficient models like Claude 3.5 Haiku or GPT-4.1-nano

Integration Considerations

Both OpenAI and Anthropic offer API access, but your existing tech stack may integrate more easily with one provider. Consider:

• Your current CMS and content tools
• Existing workflow software
• Development resources for integration

The Future of AI Content Creation

The competition between OpenAI and Anthropic is driving innovation in AI models for content creation. Based on announced roadmaps and industry trends, here’s what content creators might expect:

Near-Future Developments (Next 6-12 Months)

1. Improved knowledge retrieval: Both companies are working on enhancing how their models connect to external knowledge bases, which should lead to more factually accurate content generation.
2. Enhanced multimodal capabilities: Future models will likely improve their ability to analyze images and other media to generate more contextually relevant content.
3. More specialized models: We’re already seeing early signs of domain-specific optimization. Expect variants that may be fine-tuned for specific content types.
4. Better collaborative tools: The next generation of AI writing interfaces will likely focus on more seamless collaboration between human editors and AI systems.

Longer-Term Trends (1-2 Years)

1. More integrated content workflows: AI systems that connect with more stages of the content creation process, from research to performance analysis.
2. Increased personalization capabilities: Content that can adapt to individual reader preferences while maintaining brand voice consistency.
3. Continuous content improvement: AI-assisted monitoring of content performance with suggested optimizations.
4. Cross-platform content adaptation: More efficient generation of content optimized for multiple platforms from a single source.

For content creators, staying flexible and building workflows that can incorporate different models as they evolve will likely be the most effective approach. The most successful teams will be those who understand how to combine different models to leverage their respective strengths.

Implementation Guide: Building Your Multi-Model Content Stack

Now that you understand the strengths of each model, let’s get practical about implementing a multi-model approach for content creation:

Step 1: Audit Your Content Needs

Before investing in multiple AI models, categorize your content production:

• High-value thought leadership (where Claude 3.7 Sonnet might excel)
• SEO-focused informational content (where GPT-4.1 or Claude 3.5 Haiku might work well)
• High-volume product/service descriptions (where more efficient models like GPT-4.1-nano or Claude 3.5 Haiku could be cost-effective)
• Technical documentation (where Claude 3.7 Sonnet shows strengths)
• Creative/campaign content (where GPT-4.1 demonstrates advantages)

Step 2: Set Up API Access

For serious content operations, consider programmatic access rather than chat interfaces:

• OpenAI API for the GPT-4.1 family
• Anthropic API for the Claude models
• Consider orchestration tools to help manage workflows between different models

Both OpenAI and Anthropic provide developer documentation with code examples to help you get started:

• OpenAI: https://platform.openai.com/docs
• Anthropic: https://docs.anthropic.com

Step 3: Create Model-Specific Prompt Templates

Develop standardized prompt templates optimized for each model and content type:

• Research prompts that leverage GPT-4.1’s large context window
• Outlining prompts that take advantage of Claude 3.7 Sonnet’s structured thinking
• Draft generation prompts tailored to each model’s content strengths
• Editing prompts designed to complement the specific drafting model used

Here’s a simplified example of how a prompt template might look in code:

// Simple example of prompt template structure
const researchPrompt = `
I need to research [TOPIC]. 
Please analyze these documents and identify:
1. Key insights
2. Knowledge gaps
3. Emerging trends

Documents:
[DOCUMENTS]
`;

// This would be implemented within your API calling function

Step 4: Implement Quality Control Processes

Human oversight remains essential regardless of which models you use:

• Use different models to cross-check content when appropriate
• Implement a consistent evaluation system for AI-generated content
• Track performance metrics to identify which model combinations work best for different content types
• Regularly update your prompts based on performance data

Conclusion

The competition between GPT-4.1 and Claude 3.7 Sonnet isn’t about declaring a universal winner—it’s about understanding which tool best fits specific content creation tasks.

For premium long-form and technical content, Claude 3.7 Sonnet demonstrates particular strengths in structure and coherence. Meanwhile, GPT-4.1’s massive context window and creative capabilities make it especially well-suited for research-intensive tasks and creative content.

For teams with budget constraints, the more affordable models like GPT-4.1-nano and Claude 3.5 Haiku offer impressive capabilities at significantly lower price points.

The most effective approach for many content teams will be a flexible workflow that leverages different models at different stages:

• Research and data gathering (where GPT-4.1’s context window provides advantages)
• Ideation and outlining (where Claude 3.7 Sonnet’s structured thinking shines)
• Drafting (using the model best suited to the specific content type)
• Editing and optimization (often using a different model than was used for drafting)

The AI content landscape continues to evolve rapidly, and content creators who develop skill in strategically leveraging these tools will gain significant advantages. The question isn’t whether to use AI anymore—it’s how strategically you’re using it.

What are you waiting for? The tools are here. The playing field is wide open. It’s time to create content that gets sh*t done and grows your business.