Comparing the leading AI models:

Category: Text Generation and Writing

Subcategory: Format Conversion

Contents

1. Introduction
2. Format Conversion
3. Prompts
   1. Bullet List to Paragraph Converter
   2. CSV to Numbered List Formatter
   3. Text to Markdown Table Generator
   4. Paragraph to Bullet Points Converter
   5. Pipe-Delimited Text to Sentence Formatter
   6. Text to JSON Converter
   7. Informal to Formal Text Converter
   8. Outline to Mind Map Converter
   9. Run-on to Dialogue Formatter
   10. Text to Haiku Converter
4. Performance Verdict
5. Budget Verdict
6. Conclusion

Introduction

Comparing AI Models: A Guide to LLM Performance & Value

Looking to compare AI models but unsure which one best suits your needs? This comprehensive guide compares leading large language models (LLMs) side-by-side, helping you make informed decisions about which AI assistant to use for different tasks.

We evaluate two distinct tiers of AI models:

Budget Tier:

• ChatGPT 4.0 Mini
• Gemini 1.5 Flash
• Claude 3.5 Haiku
• Llama 3.1 8B

Performance Tier:

• ChatGPT 4.0
• Claude 3.5 Sonnet
• Gemini 1.5 Pro
• Llama 3.1 70B

By comparing AI models directly, you can better understand their strengths, limitations, and ideal use cases. Our side-by-side comparisons examine response quality, processing speed, and cost-effectiveness across various tasks.

Choose the Budget Tier when:

• Running frequent, simple queries
• Working with basic content generation
• Operating under cost constraints
• Requiring quick response times

Select the Performance Tier for:

• Complex analytical tasks
• Technical content creation
• Professional applications
• Tasks requiring nuanced understanding

Each comparison includes real-world examples and practical applications, enabling you to make data-driven decisions about which AI model best serves your specific needs.

50+ AI models with one subscription. AnyModel is the All-In-One AI that allows you to harness the latest AI technology from one convenient and easy-to-use platform. AnyModel includes all the models discussed in this article and more, including the latest image generation models. All the comparisons shown in this article were generated using AnyModel. Sign up for a free trial here.

Format Conversion

Large Language Models excel at format conversion tasks thanks to their deep understanding of different writing styles, document structures, and content organization patterns. Their ability to recognize and transform text between various formats - whether it's converting bullet points to paragraphs, restructuring content from informal to formal tone, or changing technical documentation into plain language - makes them invaluable tools for content creators and professionals. LLMs can seamlessly handle these conversions while preserving the original meaning and context, saving hours of manual reformatting work. Their natural language processing capabilities allow them to intelligently restructure content based on the target format's requirements, making them particularly effective at tasks like converting markdown to HTML, transforming meeting notes into structured reports, or reformatting citation styles in academic papers.

Prompts

Bullet List to Paragraph Converter

Transforms a bulleted list of activities or items into a natural, flowing paragraph format. Useful for converting notes into narrative text or report writing.

Prompt:

Convert this bulleted list into a paragraph of flowing text: • Went to store • Bought groceries • Made dinner • Fed the cat • Did laundry

Performance Results

nyModel

Convert this bulleted list into a paragraph of flowing text: • Went to store • Bought groceries • Made dinner • Fed the cat • Did laundry

I went to the store, bought groceries, and then made dinner. Afterward, I fed the cat and did the laundry.

After heading to the store, I purchased groceries and returned home to prepare dinner. Once the meal was ready, I made sure to feed the cat, and while dinner was settling, I took care of the laundry. The evening's tasks flowed seamlessly from one to the next, ensuring everything got done efficiently.

I went to the store to pick up groceries, then came home and made dinner. After eating, I fed the cat and then did a load of laundry.

It was a productive day, first starting with a trip to the store to pick up a few essentials. After returning home with the groceries, dinner was the next task on the agenda, and a delicious meal was quickly prepared. Once dinner was out of the way, some attention was given to the family pet, as the cat was fed and taken care of. Finally, the evening was rounded out with a load of laundry, wrapping up the day with a sense of accomplishment and a clean slate.

AnyModel: Explore and compare over 50 AI models side-by-side.

Budget Results

nyModel

Convert this bulleted list into a paragraph of flowing text: • Went to store • Bought groceries • Made dinner • Fed the cat • Did laundry

After a trip to the store to buy groceries, I returned home and made dinner. Once the meal was prepared, I took a moment to feed the cat before tackling the laundry.

I went to the store to buy groceries, then came home and made dinner. After dinner, I fed the cat and finally did a load of laundry.

I went to the store and bought groceries, then returned home to make dinner. After preparing my meal, I fed the cat and completed my chores by doing laundry.

I had a busy day that involved running a few errands and tending to some household tasks. I started by going to the store, where I picked up the groceries I needed for the next few days. Once I got home, I decided to make dinner, which I was looking forward to after a morning of shopping. But before I could sit down to eat, I made sure to take care of our feline friend by feeding the cat. After dinner, I tackled a load of laundry, getting everything clean and fresh for the week ahead.

AnyModel: Explore and compare over 50 AI models side-by-side.

Performance Tier

All models successfully converted the list into coherent flowing text, but with notable differences. Llama 3.1 70B provided the most detailed and narrative-rich response while being the fastest (635ms). ChatGPT offered a concise, straightforward conversion (827ms). Claude added natural transitions and efficiency context (2051ms). Gemini's response was clear but basic (2953ms and slowest). Llama excelled at creative elaboration, while ChatGPT demonstrated efficiency. Claude balanced detail with readability, and Gemini focused on essential information. Performance-wise, Llama's combination of speed and quality stands out.

Budget Tier

ChatGPT 4o Mini provided a concise, natural flow but omitted the initial store visit. Gemini 1.5 Flash delivered a straightforward conversion with good temporal connections, though took significantly longer (9.6s). Claude 3.5 Haiku maintained all points while creating smooth transitions. Llama 3.1 8B, despite being the fastest (337ms), produced an overly verbose response with unnecessary elaboration. While all models successfully converted the list to flowing text, Claude struck the best balance between completeness, natural flow, and concision.

CSV to Numbered List Formatter

Converts comma-separated values into a clean, numbered list format. Ideal for creating organized lists from simple data strings.

Prompt:

Transform this comma-separated list into a numbered list: apples, oranges, bananas, pears, grapes

Performance Results

nyModel

Transform this comma-separated list into a numbered list: apples, oranges, bananas, pears, grapes

1. apples
2. oranges
3. bananas
4. pears
5. grapes

1. apples
2. oranges
3. bananas
4. pears
5. grapes

1. apples
2. oranges
3. bananas
4. pears
5. grapes

Here's the list transformed into a numbered list:

1. Apples
2. Oranges
3. Bananas
4. Pears
5. Grapes

AnyModel: Explore and compare over 50 AI models side-by-side.

Budget Results

nyModel

Transform this comma-separated list into a numbered list: apples, oranges, bananas, pears, grapes

1. Apples
2. Oranges
3. Bananas
4. Pears
5. Grapes

1. Apples
2. Oranges
3. Bananas
4. Pears
5. Grapes

1. apples
2. oranges
3. bananas
4. pears
5. grapes

Here's the list of fruits transformed from a comma-separated list into a numbered list:

1. Apples
2. Oranges
3. Bananas
4. Pears
5. Grapes

AnyModel: Explore and compare over 50 AI models side-by-side.

Performance Tier

All models successfully completed the basic list transformation task with identical formatting and accuracy. Claude 3.5 Sonnet and Gemini 1.5 Pro produced identical, clean outputs. ChatGPT 4o added extra spacing between lines. Llama 3.1 70B included an unnecessary introductory line and capitalized the items, but was notably the fastest at 390ms. Claude was the second fastest at 808ms, followed by Gemini at 963ms and ChatGPT at 1020ms. For this simple task, Llama provided the best balance of speed and accuracy.

Budget Tier

All models successfully converted the list format, producing identical numbered sequences. Gemini 1.5 Flash and ChatGPT 4o Mini maintained consistent capitalization. Claude 3.5 Haiku used lowercase throughout. Llama 3.1 8B added an unnecessary introductory sentence but had the fastest response time at 258ms. Gemini was second fastest at 482ms, followed by Claude at 535ms and ChatGPT 4o Mini at 639ms. The formatting was clean across all models, with slight variations in spacing and line breaks.

Text to Markdown Table Generator

Creates a properly formatted markdown table from plain text data. Helpful for documentation and README files in software projects.

Prompt:

Convert this plain text into a markdown table: Name: John Age: 30 City: London | Name: Mary Age: 25 City: Paris

Performance Results

nyModel

Convert this plain text into a markdown table: Name: John Age: 30 City: London | Name: Mary Age: 25 City: Paris

Certainly! Here's the data formatted as a markdown table:

| Name | Age | City  |
|------|-----|-------|
| John | 30  | London|
| Mary | 25  | Paris |

| Name | Age | City | |------|-----|------| | John | 30 | London | | Mary | 25 | Paris |

| Name | Age | City | |---|---|---| | John | 30 | London | | Mary | 25 | Paris |

| Name | Age | City | |------|-----|------| | John | 30 | London| | Mary | 25 | Paris |

AnyModel: Explore and compare over 50 AI models side-by-side.

Budget Results

nyModel

Convert this plain text into a markdown table: Name: John Age: 30 City: London | Name: Mary Age: 25 City: Paris

Here’s the plain text converted into a markdown table:

| Name  | Age | City   |
|-------|-----|--------|
| John  | 30  | London |
| Mary  | 25  | Paris  |

| Name | Age | City |
|---|---|---|
| John | 30 | London |
| Mary | 25 | Paris |

| Name | Age | City | |------|-----|------| | John | 30 | London | | Mary | 25 | Paris |

Here is the conversion of the plain text into a markdown table:

| Name | Age | City | |-----------|-----|----------| | John | 30 | London | | Mary | 25 | Paris |

Alternatively, if you want to mark the headers, it would be:

| Name | Age | City | |---------------|---------|--------------| | John | 30 | London | | Mary | 25 | Paris |

You can also format the cells with pipes and dashes for a more robust table:

| Name | Age | City | | :------- | :-: | :----: | | John | 30 | London | | Mary | 25 | Paris |

AnyModel: Explore and compare over 50 AI models side-by-side.

Performance Tier

All models successfully converted the text into a properly formatted markdown table. Llama 3.1 70B was notably fastest at 319ms, while others took 3-4x longer. ChatGPT 4.0 added unnecessary explanatory text and markdown code fencing. Claude, Gemini and Llama provided clean, minimal output with correct table syntax. Gemini used shorter divider lines. All models maintained proper alignment and spacing. For this simple table conversion task, Llama 3.1 70B emerges as the most efficient choice, delivering clean output with the fastest response time.

Budget Tier

All models successfully converted the text to valid markdown tables, but with varying approaches and response times. Claude Haiku was most efficient at 785ms, producing a clean, minimal table. ChatGPT Mini (1446ms) and Gemini Flash (10284ms) produced similar straightforward tables with proper formatting. Llama 3.1 8B took just 407ms but was perhaps overly verbose, providing three different table variations. While all tables are functionally correct, Claude achieved the best balance of speed and clean output, making it ideal for this specific task.

Paragraph to Bullet Points Converter

Transforms a descriptive paragraph into organized bullet points. Useful for creating presentations or summarizing information.

Prompt:

Change this paragraph into a series of bullet points: The three primary colors are red, blue, and yellow. Each of these colors can be mixed to create secondary colors. Red and blue make purple, blue and yellow make green, and red and yellow make orange.

Performance Results

nyModel

Change this paragraph into a series of bullet points: The three primary colors are red, blue, and yellow. Each of these colors can be mixed to create secondary colors. Red and blue make purple, blue and yellow make green, and red and yellow make orange.

• The three primary colors are:
• Red
• Blue
• Yellow
• Mixing primary colors creates secondary colors:
• Red and blue make purple
• Blue and yellow make green
• Red and yellow make orange

• The three primary colors are:

• Red

• Blue

• Yellow

• Primary colors can be mixed to create secondary colors

• Secondary color combinations:

• Red + Blue = Purple

• Blue + Yellow = Green

• Red + Yellow = Orange

• Primary Colors: Red, Blue, Yellow
• Secondary Colors (created by mixing primary colors):
  • Red + Blue = Purple
  • Blue + Yellow = Green
  • Red + Yellow = Orange

Here's the information in a series of bullet points:

• The three primary colors are:

• Red
• Blue
• Yellow • Mixing primary colors creates secondary colors:
• Red + Blue = Purple
• Blue + Yellow = Green
• Red + Yellow = Orange

AnyModel: Explore and compare over 50 AI models side-by-side.

Budget Results

nyModel

Change this paragraph into a series of bullet points: The three primary colors are red, blue, and yellow. Each of these colors can be mixed to create secondary colors. Red and blue make purple, blue and yellow make green, and red and yellow make orange.

• The three primary colors are:
• Red
• Blue
• Yellow
• Primary colors can be mixed to create secondary colors:
• Red + Blue = Purple
• Blue + Yellow = Green
• Red + Yellow = Orange

• Primary colors: red, blue, yellow
• Secondary colors are created by mixing primary colors:
  • Red + Blue = Purple
  • Blue + Yellow = Green
  • Red + Yellow = Orange

Here's the paragraph converted to bullet points:

• The three primary colors are:
• Red
• Blue
• Yellow
• These primary colors can be mixed to create secondary colors:
• Red + Blue = Purple
• Blue + Yellow = Green
• Red + Yellow = Orange

Here's the rewritten paragraph in a series of bullet points:

• The three primary colors are red, blue, and yellow.
• Red and blue mixed together create the secondary color purple.
• Blue and yellow mixed together create the secondary color green.
• Red and yellow mixed together create the secondary color orange.

AnyModel: Explore and compare over 50 AI models side-by-side.

Performance Tier

All models successfully converted the paragraph into clear bullet points, maintaining key information about primary and secondary colors. ChatGPT 4o and Claude provided the most structured hierarchical formatting. Gemini utilized bold text for enhanced readability. Llama was notably the fastest at 548ms, while delivering clean formatting. Claude took longest at 1435ms but offered excellent nesting structure. Performance differences were minimal, with each model effectively organizing the content into logical groupings of primary colors and their mixing combinations.

Budget Tier

All models successfully converted the paragraph into bullet points, with subtle formatting differences. ChatGPT 4o Mini and Claude 3.5 Haiku produced nearly identical hierarchical structures with nested bullets. Gemini 1.5 Flash used asterisks and indentation, while Llama 3.1 8B opted for a flatter bullet structure. Llama was notably fastest at 283ms, while Gemini was slowest at 7805ms. Claude and ChatGPT had similar mid-range response times. In terms of clarity and organization, ChatGPT and Claude's nested structure was most effective for presenting the information hierarchically.

Pipe-Delimited Text to Sentence Formatter

Converts pipe-separated values into grammatically correct sentences. Helpful for transforming structured data into readable text.

Prompt:

Convert these pipe-separated values into a properly formatted sentence: John|walked|to|the|store|yesterday|afternoon

Performance Results

nyModel

Convert these pipe-separated values into a properly formatted sentence: John|walked|to|the|store|yesterday|afternoon

John walked to the store yesterday afternoon.

John walked to the store yesterday afternoon.

John walked to the store yesterday afternoon.

John walked to the store yesterday afternoon.

AnyModel: Explore and compare over 50 AI models side-by-side.

Budget Results

nyModel

Convert these pipe-separated values into a properly formatted sentence: John|walked|to|the|store|yesterday|afternoon

John walked to the store yesterday afternoon.

John walked to the store yesterday afternoon.

John walked to the store yesterday afternoon.

John walked to the store yesterday afternoon.

Here's a breakdown of how I formatted the sentence:

1. John (proper noun)
2. walked (verb)
3. to (preposition)
4. the (article)
5. store (noun)
6. yesterday (adverb)
7. afternoon (noun/adjective)

Note: In English, "yesterday" and "afternoon" can be both adverbs and adjectives, depending on the context.

Alternatively, the sentence could be rewritten as:

John went to the store yesterday in the afternoon.

This revised sentence is a bit more formal and uses a more typical adverb ("in the afternoon") to express time.

AnyModel: Explore and compare over 50 AI models side-by-side.

Performance Tier

All models performed equally well on this simple text formatting task, correctly converting the pipe-separated values into a grammatically correct sentence. ChatGPT 4.0 and Llama 3.1 70B were notably faster, completing the task in 554ms and 511ms respectively. Claude 3.5 Sonnet was slowest at 770ms, while Gemini 1.5 Pro fell in the middle at 700ms. For basic string manipulation tasks like this, model choice appears less critical as all achieved perfect accuracy, though speed differences may matter at scale.

Budget Tier

All models successfully converted the pipe-separated values into the grammatically correct sentence "John walked to the store yesterday afternoon." ChatGPT 4o Mini, Claude 3.5 Haiku, and Llama 3.1 8B performed similarly with response times under 500ms. Gemini 1.5 Flash was notably slower at 3906ms. Llama 3.1 8B provided additional grammatical analysis and alternative phrasing, showing more comprehensive understanding, despite being the fastest at 389ms. All models maintained proper capitalization and punctuation, demonstrating basic language formatting capabilities.

Text to JSON Converter

Converts plain text data into properly formatted JSON objects. Essential for data transformation and API integration tasks.

Prompt:

Transform this text into a JSON format: Name: Sarah Johnson, Age: 28, Occupation: Teacher, City: Chicago

Performance Results

nyModel

Transform this text into a JSON format: Name: Sarah Johnson, Age: 28, Occupation: Teacher, City: Chicago

{
  "Name": "Sarah Johnson",
  "Age": 28,
  "Occupation": "Teacher",
  "City": "Chicago"
}

{
  "name": "Sarah Johnson",
  "age": 28,
  "occupation": "Teacher", 
  "city": "Chicago"
}

{
  "Name": "Sarah Johnson",
  "Age": 28,
  "Occupation": "Teacher",
  "City": "Chicago"
}

Here's the given information in JSON format:

{
  "Name": "Sarah Johnson",
  "Age": 28,
  "Occupation": "Teacher",
  "City": "Chicago"
}

AnyModel: Explore and compare over 50 AI models side-by-side.

Budget Results

nyModel

Transform this text into a JSON format: Name: Sarah Johnson, Age: 28, Occupation: Teacher, City: Chicago

{
  "Name": "Sarah Johnson",
  "Age": 28,
  "Occupation": "Teacher",
  "City": "Chicago"
}

{
  "Name": "Sarah Johnson",
  "Age": 28,
  "Occupation": "Teacher",
  "City": "Chicago"
}

{
    "name": "Sarah Johnson",
    "age": 28,
    "occupation": "Teacher", 
    "city": "Chicago"
}

Here's the text transformed into JSON format:

{
  "Name": "Sarah Johnson",
  "Age": 28,
  "Occupation": "Teacher",
  "City": "Chicago"
}

However, if we want to follow conventional JSON practices, we might want to wrap the entire object inside a 'person' or 'employee' object, like this:

{
  "person": {
    "Name": "Sarah Johnson",
    "Age": 28,
    "Occupation": "Teacher",
    "City": "Chicago"
  }
}

Or if we're looking at properties common to people in general, this might be more suitable for large databases:

{
  "person": {
    "name": "Sarah Johnson",
    "age": 28,
    "occupation": "Teacher",
    "city": "Chicago"
  }
}

However, the decision of keys depends on the context of the JSON data.

AnyModel: Explore and compare over 50 AI models side-by-side.

Performance Tier

All models successfully converted the text to valid JSON format. ChatGPT 4o, Gemini 1.5 Pro, and Llama 3.1 70B used capitalized keys, while Claude used lowercase - both are valid JSON practices. Llama 3.1 70B was notably fastest at 393ms but added unnecessary explanatory text. Claude took 1186ms, ChatGPT 4o 1075ms, and Gemini 1.5 Pro was slowest at 1407ms. All models maintained data accuracy and proper JSON syntax with consistent formatting, making them equally reliable for this basic JSON conversion task.

Budget Tier

All models successfully converted the text to valid JSON, but with notable differences. Llama 3.1 8B was fastest (468ms) and most verbose, providing additional context and formatting variations. ChatGPT 4o Mini and Gemini 1.5 Flash produced identical outputs with capitalized keys, while Claude used lowercase keys following common JSON conventions. Gemini was second fastest at 627ms. While all responses were technically correct, Claude's lowercase formatting aligns best with JSON best practices. Llama's additional context could be helpful for learning but may be excessive for simple transformations.

Informal to Formal Text Converter

Transforms casual text message style writing into professional business language. Useful for improving communication in professional settings.

Prompt:

Convert this informal text message style writing into formal business prose: hey there! just wanted 2 check if ur coming 2 the meeting tmrw? lemme know asap thx!

Performance Results

nyModel

Convert this informal text message style writing into formal business prose: hey there! just wanted 2 check if ur coming 2 the meeting tmrw? lemme know asap thx!

Subject: Confirmation of Attendance at Tomorrow's Meeting

Dear [Recipient's Name],

I hope this message finds you well. I am writing to inquire whether you will be able to attend the meeting scheduled for tomorrow. Kindly confirm your attendance at your earliest convenience.

Thank you very much.

Best regards,

[Your Name]

Dear [Name],

I am writing to confirm your attendance at tomorrow's meeting. Please advise at your earliest convenience regarding your availability.

Kind regards, [Name]

This email serves as a reminder for tomorrow's meeting. Please confirm your attendance at your earliest convenience. Thank you.

Here's the formal business prose version of the given text message:

Dear [Recipient's Name],

I would like to confirm your attendance at tomorrow's meeting. Could you kindly respond at your earliest convenience to inform me if you will be able to attend?

Thank you for your prompt response.

Best regards, [Your Name]

AnyModel: Explore and compare over 50 AI models side-by-side.

Budget Results

nyModel

Convert this informal text message style writing into formal business prose: hey there! just wanted 2 check if ur coming 2 the meeting tmrw? lemme know asap thx!

Subject: Confirmation of Attendance for Tomorrow's Meeting

Dear [Recipient's Name],

I hope this message finds you well. I would like to confirm whether you will be attending the meeting scheduled for tomorrow. Please let me know at your earliest convenience.

Thank you.

Best regards,
[Your Name]
[Your Position]
[Your Company]
[Your Contact Information]

Good morning,

This email is to confirm your attendance at tomorrow's meeting. Please respond at your earliest convenience.

Thank you,

Dear Colleague,

I am writing to confirm your attendance at tomorrow's scheduled meeting. Kindly provide your response at your earliest convenience.

Thank you for your prompt attention to this matter.

Regards, [Your Name]

Here is the formal business prose equivalent:

To confirm your attendance at the upcoming meeting, I would kindly appreciate your response at your earliest convenience to determine your availability for tomorrow.

AnyModel: Explore and compare over 50 AI models side-by-side.

Performance Tier

ChatGPT 4.0 provided the most comprehensive and polished business format, though took longest to generate (1617ms). Claude delivered a concise yet professional response with proper formatting (1101ms). Gemini's response was overly brief, lacking proper salutations (1029ms). Llama 3.1 impressed with the fastest response (445ms) while maintaining professional tone and structure comparable to ChatGPT. All models successfully converted informal language to formal business prose, with ChatGPT and Llama showing superior attention to business email conventions.

Budget Tier

ChatGPT 4o Mini delivered a comprehensive formal business email template in 1.4s, including all professional elements. Gemini was slowest at 7.8s with an overly brief response. Claude produced a well-balanced formal tone in 1.2s while maintaining conciseness. Llama was fastest at 0.23s but produced a single run-on sentence. ChatGPT performed best overall, providing the most complete and professionally formatted response, while Llama's speed-to-quality ratio was impressive despite its brevity. Claude offered the best balance between formality and conciseness.

Outline to Mind Map Converter

Converts traditional outlined content into a mind map format. Beneficial for visual learning and brainstorming sessions.

Prompt:

Change this traditional outline format into a mind map text format: I. Main Topic A. Subtopic 1 B. Subtopic 2 1. Detail a 2. Detail b

Performance Results

nyModel

Change this traditional outline format into a mind map text format: I. Main Topic A. Subtopic 1 B. Subtopic 2 1. Detail a 2. Detail b

Main Topic

• Subtopic 1
• Subtopic 2
• Detail a
• Detail b

Main Topic

• ## Subtopic 1
• ## Subtopic 2
• Detail a
• Detail b

Main Topic Subtopic 1 Subtopic 2 Detail a Detail b

Main Topic |- Subtopic 1 |- Subtopic 2 |- Detail a |- Detail b

AnyModel: Explore and compare over 50 AI models side-by-side.

Budget Results

nyModel

Change this traditional outline format into a mind map text format: I. Main Topic A. Subtopic 1 B. Subtopic 2 1. Detail a 2. Detail b

Sure! Here’s the traditional outline transformed into a mind map text format:

• Main Topic
• Subtopic 1
• Subtopic 2
  • Detail a
  • Detail b

Main Topic

• Subtopic 1
• Subtopic 2
  • Detail a
  • Detail b

Here's the traditional outline converted to a mind map text format:

Main Topic
│
├── Subtopic 1
│   ├── Detail a
│   └── Detail b
│
└── Subtopic 2

However, I don't see a traditional outline format provided. Please share the outline, and I'll transform it into a mind map text format for you.

If you want an example of mind map text format, here's a sample:

Main Topic: Education

• Subtopic 1: Online Courses
• Subtopic 2: Traditional Schools
• Subtopic 3: Alternative Learning
• Detail a: Online Certification Programs
  • Sub-detail a1: Virtual Training Centers
  • Sub-detail a2: Online Webinars
• Detail b: Tutoring Services
  • Sub-detail b1: One-on-One Support
  • Sub-detail b2: Group Study Sessions
• Detail c: Adult Education Programs
  • Sub-detail c1: Literacy Classes
  • Sub-detail c2: Career Skill Development

AnyModel: Explore and compare over 50 AI models side-by-side.

Performance Tier

All models successfully transformed the outline into mind map formats, but with distinct approaches. Llama 3.1 70B delivered the fastest response (528ms) using intuitive branch symbols (|-). ChatGPT 4.0 and Claude 3.5 used clean hyphen-based hierarchies, with Claude adding markdown headers for additional structure. Gemini 1.5 Pro took the longest (1386ms) and used simple indentation. Llama's format proves most visually effective for mind mapping, while Claude's markdown approach offers better formal documentation structure. All maintained the hierarchical relationships accurately, though with varying degrees of visual clarity.

Budget Tier

Claude 3.5 Haiku provided the most elegant solution, using ASCII characters to create a clear hierarchical structure. ChatGPT 4o Mini and Gemini 1.5 Flash delivered similar, functional results using basic indentation and bullet points. Llama 3.1 8B misinterpreted the prompt, providing an unrelated example instead. Response times varied significantly, with Llama being fastest (434ms) and Gemini slowest (10103ms). Claude's solution best balances visual clarity and information hierarchy while maintaining readability.

Run-on to Dialogue Formatter

Transforms run-on sentences into properly formatted dialogue with appropriate punctuation and structure. Helpful for creative writing and story editing.

Prompt:

Convert this run-on sentence into properly formatted dialogue: John said he was going to the store and Mary said she needed milk and Tom said he wanted cookies

Performance Results

nyModel

Convert this run-on sentence into properly formatted dialogue: John said he was going to the store and Mary said she needed milk and Tom said he wanted cookies

John said, "I am going to the store."

Mary responded, "I need milk."

Tom added, "I want cookies."

Here's the dialogue:

"I'm going to the store," John said.

"I need milk," Mary said.

"I want cookies," Tom said.

"I'm going to the store," John said.

"Oh," Mary said. "I need milk."

"Get me some cookies!" Tom added. OR "And I want cookies," Tom said.

"I'm going to the store," John said.

"I need milk," Mary replied.

"And can you pick up some cookies?" Tom added.

AnyModel: Explore and compare over 50 AI models side-by-side.

Budget Results

nyModel

Convert this run-on sentence into properly formatted dialogue: John said he was going to the store and Mary said she needed milk and Tom said he wanted cookies

"John said he was going to the store."
Mary replied, "I need milk."
Tom chimed in, "I want cookies!"

"I'm going to the store," John said.

"I need milk," Mary added.

"And I want cookies," Tom chimed in.

Here's the sentence converted to properly formatted dialogue:

"I'm going to the store," John said.

"I need milk," Mary added.

"And I want cookies," Tom chimed in.

Here is the reformatted dialogue:

John: I'm going to the store. Mary: I need milk. Tom: I want cookies.

AnyModel: Explore and compare over 50 AI models side-by-side.

Performance Tier

All models successfully converted the run-on sentence into proper dialogue format. Llama 3.1 performed fastest at 715ms while maintaining natural flow with varied dialogue tags ("said," "replied," "added"). Claude and ChatGPT delivered clean, straightforward conversions with consistent dialogue tags. Gemini uniquely offered two variations but took longest at 1510ms. Claude showed efficiency at 1033ms with crisp formatting. Each model maintained the core message while adding appropriate punctuation and spacing, with Llama and Gemini showing more creativity in dialogue construction.

Budget Tier

All models successfully converted the run-on sentence into clear dialogue, but with varying approaches. Gemini 1.5 Flash and Claude 3.5 Haiku produced identical output with natural flow and proper attribution verbs. ChatGPT 4o Mini used slightly different dialogue tags but maintained correct formatting. Llama 3.1 8B opted for a simpler colon-based format. In terms of speed, Llama was fastest at 226ms, while Claude was slowest at 1188ms. Gemini offered the best balance of quality and speed, producing well-formatted dialogue in 579ms.

Text to Haiku Converter

Converts descriptive text into the traditional 5-7-5 syllable haiku format. Perfect for creative writing exercises and poetry creation.

Prompt:

Transform this block of text into a haiku format: The autumn leaves fall gently to the ground, creating a colorful carpet in the forest.

Performance Results

nyModel

Transform this block of text into a haiku format: The autumn leaves fall gently to the ground, creating a colorful carpet in the forest.

Leaves gently descend
Colorful carpet in woods
Autumn's quiet grace

autumn leaves falling
nature's soft descent to earth
forest floor ablaze

Autumn leaves descend, Colorful carpet unfurls, Forest floor ablaze.

Golden leaves descend Colorful carpet unfurls Nature's gentle dance

AnyModel: Explore and compare over 50 AI models side-by-side.

Budget Results

nyModel

Transform this block of text into a haiku format: The autumn leaves fall gently to the ground, creating a colorful carpet in the forest.

Leaves drift to the ground,
Colorful carpet unfolds,
Autumn's gentle touch.

Leaves drift, soft and slow, Colors paint the forest floor, Autumn's gentle hand.

Here's the text transformed into a haiku:

Crisp autumn descends
Leaves dance on forest's soft floor
Painting ground with gold

Golden colors fall Colorful carpet softly Autumn's gentle hue

AnyModel: Explore and compare over 50 AI models side-by-side.

Performance Tier

ChatGPT 4.0 delivered the fastest response (658ms) with a technically correct haiku capturing the original meaning. Claude's response, while slower (987ms), added stylistic asterisks and used vivid imagery. Gemini (888ms) produced a balanced haiku with strong visualization. Llama 3.1 was significantly slower (58s) but still created a poetic interpretation. All models successfully maintained the 5-7-5 syllable format and preserved the core imagery of falling leaves. ChatGPT and Gemini demonstrated the best balance of speed and quality, while Claude showed unique creative flair.

Budget Tier

All models successfully created valid haikus following the 5-7-5 syllable pattern. Llama 3.1 8B was fastest at 216ms, while Claude took longest at 1222ms. ChatGPT and Gemini produced similar nature-focused imagery, with Gemini's response being marginally more poetic. Claude unnecessarily included markdown formatting but created vivid imagery with "crisp" and "dance." Llama's output was simple but effective. For pure haiku generation, Gemini and ChatGPT provided the best balance of quality and speed, while Claude's extra formatting was superfluous for this task.

Performance Verdict

Based on the comparative analyses of ChatGPT 4.0, Claude 3.5 Sonnet, Gemini 1.5 Pro, and Llama 3.1 70B across various format conversion tasks, here's the comprehensive verdict:

Llama 3.1 70B emerged as the speed champion, consistently delivering the fastest responses across most tasks, often 2-3x faster than competitors. It excelled in creative elaboration and natural language flow, particularly in narrative conversions. However, it occasionally added unnecessary explanatory text and showed inconsistency in some formatting choices.

ChatGPT 4.0 demonstrated exceptional consistency and accuracy, particularly in business-oriented conversions and technical formatting tasks. It provided well-structured outputs with attention to detail, though sometimes included unnecessary explanations. Response times were generally moderate.

Claude 3.5 Sonnet showed strength in producing clean, well-structured outputs with excellent attention to hierarchy and documentation standards. It often added useful context and smooth transitions, though typically had longer response times. Its markdown implementations were particularly noteworthy.

Gemini 1.5 Pro typically had the longest response times but delivered reliable, accurate conversions. It occasionally offered unique variations and creative approaches, though sometimes produced more basic outputs compared to its competitors.

Winner: Llama 3.1 70B The combination of consistently fast response times, creative elaboration, and generally accurate formatting makes Llama 3.1 70B the overall winner for format conversion tasks. While all models performed competently, Llama's speed advantage and natural language capabilities give it an edge for most practical applications. For specifically business-oriented or technically precise conversions, ChatGPT 4.0 would be the recommended alternative.

Budget Verdict

Based on the format conversion analyses, here's how the models compare:

ChatGPT 4o Mini: Strengths:

• Consistent, reliable output quality
• Strong at formal writing conversions
• Excellent handling of structured formats
• Good balance of completeness and concision Weaknesses:
• Occasionally omits details
• Mid-range processing speed

Claude 3.5 Haiku: Strengths:

• Best overall formatting consistency
• Excellent at maintaining information hierarchy
• Strong adherence to format conventions
• Clean, minimal outputs Weaknesses:
• Sometimes adds unnecessary formatting
• Occasionally slower than competitors

Gemini 1.5 Flash: Strengths:

• High-quality, accurate conversions
• Strong natural language processing
• Excellent at dialogue formatting Weaknesses:
• Significantly slower processing times
• Sometimes overly brief responses

Llama 3.1 8B: Strengths:

• Fastest processing times consistently
• Provides additional context and variations
• Impressive speed-to-quality ratio Weaknesses:
• Often overly verbose
• Sometimes misinterprets prompts
• Occasionally produces simplistic outputs

Winner: Claude 3.5 Haiku Claude emerges as the winner for format conversion tasks, demonstrating the best overall balance of quality, consistency, and adherence to formatting conventions. While not the fastest, its outputs consistently strike the right balance between completeness and concision, with exceptional handling of complex formatting requirements. The model excels particularly in maintaining information hierarchies and producing clean, professional outputs across various format types.

Conclusion

Conclusion

This comprehensive analysis of format conversion capabilities across leading AI models reveals distinct advantages and trade-offs between performance and budget tiers. The performance tier, led by Llama 3.1 70B, demonstrated superior speed and creative elaboration, while the budget tier, with Claude 3.5 Haiku at the forefront, proved remarkably capable at maintaining formatting consistency and accuracy.

In the performance tier, Llama 3.1 70B's exceptional processing speed and natural language capabilities make it the ideal choice for high-volume format conversion tasks. Meanwhile, ChatGPT 4.0 and Claude 3.5 Sonnet excel in business-oriented and technically precise conversions, respectively.

For budget-conscious users, Claude 3.5 Haiku offers the best balance of quality and consistency, despite not being the fastest option. Its ability to maintain clean, professional formatting across various conversion tasks makes it a reliable choice for most applications. Llama 3.1 8B's impressive speed comes with occasional accuracy trade-offs, while Gemini 1.5 Flash's slower processing is offset by high-quality outputs.

The choice between tiers ultimately depends on specific needs: the performance tier is ideal for complex, high-stakes conversions requiring nuanced understanding, while the budget tier proves more than adequate for routine format conversion tasks where cost-efficiency is paramount. Both tiers demonstrate the remarkable progress in AI-powered format conversion capabilities, offering viable solutions for diverse use cases.