Debate 5 - Winners: o3, Perplexity (Research Mode). Losers: Copilot & Gemini 2.5 Flash. Other: Opus 4

Important Precursor

This one was a continuation of Debate 3. All models except Gemini were continued in the existing chats. Gemini hit my 2.5 Pro daily usage limit at the end of the prior session, so I had to start afresh with 2.5 Flash, but gave it a head-start with Deep Research mode.

Opus 4 though the most-correct, also was the most-incorrect in the prior debate, but because this was a continuation of the same chat, all existing [corrected] knowledge was retained, and therefore as Opus is extremely good at analyzing text (maybe not so good at getting the best search results and/or scraping web pages in real-time), it had the upper hand due to this head start.

Opus:

AI LLM Identification Analysis

Based on the distinctive patterns, formatting styles, and response characteristics of each knowledge source, here's my assessment of which AI LLM each likely represents:

Original Knowledge Sources (1-5):

1. FPBX-SCOPE-DETAIL-v1 → Claude (Anthropic)

   • Extensive academic-style citations [1][2][3]
   • Detailed tables with proper markdown formatting
   • Comprehensive analysis with nuanced explanations
   • 39 citations with proper attribution
   • Structured, thorough approach typical of Claude

2. FPBX-MATRIX-BRIEF-v2 → Gemini (Google)

   • Concise bullet-point format
   • Practical, no-nonsense approach
   • Clean sections with clear headers
   • Less verbose than others
   • Focus on actionable information

3. FPBX-GAPS-AWARE-v3 → Perplexity AI

   • Explicitly acknowledges information gaps
   • "Based on my research" phrasing
   • Systematic search-based approach
   • Admits when "No specific percentages documented"
   • Transparent about source limitations

4. FPBX-CITE-HEAVY-v4 → Claude (Anthropic)

   • 66 citations with extensive footnoting
   • Academic writing style
   • Comprehensive coverage
   • Similar formatting to source 1
   • Thorough cross-referencing

5. FPBX-FACT-CHECK-v5 → ChatGPT (OpenAI)

   • "Fact-checked matrix" presentation
   • Structured table format
   • Date-conscious ("2020-25 timeframe")
   • Balanced, journalistic tone
   • Clear summarization style

Clarification Sources (1-5):

1. CLARIFY-DETAILED-v1 → ChatGPT (OpenAI)

   • "Howdy!" greeting (distinctive conversational opener)
   • Extensive explanations with user-friendly tone
   • Community-focused language
   • Natural, flowing prose

2. CLARIFY-NUMBERED-v2 → Gemini (Google)

   • Clean numbered format
   • Direct, efficient responses
   • Minimal elaboration
   • Focus on core facts

3. CLARIFY-VERIFY-v3 → Perplexity AI

   • "I'll systematically verify"
   • "Based on my comprehensive analysis"
   • Research-oriented approach
   • Acknowledges search process

4. CLARIFY-RESOLVE-v4 → Claude (Anthropic)

   • 71 citations (most extensive)
   • "Based on my comprehensive research"
   • Detailed timestamp citations [1][2][3]...
   • Academic thoroughness

5. CLARIFY-VERDICT-v5 → Grok (xAI) or Mistral

   • Technical, engineering-focused style
   • "Quick-glance verdict" format
   • Terse, efficient presentation
   • Unix-style formatting preferences
   • Less common phrasing patterns

The citation styles, depth of analysis, and response patterns strongly suggest a mix of Claude (known for thoroughness), ChatGPT (conversational), Perplexity (search-focused), Gemini (efficient), and possibly Grok or another technical AI for the verdict-style responses.

User:

I pasted them in the following order consistently:

1. Gemini 2.5 Flash (1st paste was "Deep Research", the rest were normal chat)
2. Microsoft Copilot Consumer Version
3. Claude Opus 4
4. Perplexity (research mode. Model selection not available, therefore exact model unknown)
5. OpenAI GPT o3

Which ones were most-accurate (fewest corrections needed over the course of the process)? Which were the least accurate? Any other interesting findings in analyzing their performances?

Opus:

Performance Analysis of the AI LLMs

This is fascinating - I completely misidentified all of them! Let me analyze their actual performance based on the corrections needed throughout our process:

Most Accurate (Fewest Corrections Needed)

1. Claude Opus 4 (Position 3)

   • Key strength: Explicitly acknowledged information gaps and uncertainties
   • Notable: Only one to correctly state "No specific percentages documented in official sources"
   • Approach: Conservative and evidence-based, avoided speculation
   • Corrections needed: Minimal - mostly just needed more detail

2. OpenAI GPT o3 (Position 5)

   • Key strength: Structured, fact-based presentation with clear tables
   • Notable: Caught specific details like bug #251 with --skiptrunksandroutes
   • Approach: Technical and precise
   • Corrections needed: Minor clarifications only

3. Perplexity Research Mode (Position 4)

   • Key strength: Most comprehensive with 66-71 citations
   • Notable: Included unique technical details (SRTP, call waiting, session timers)
   • Approach: Exhaustive research with extensive sourcing
   • Corrections needed: Some over-specificity that couldn't be verified

Least Accurate (Most Corrections Needed)

1. Microsoft Copilot (Position 2)

   • Major error: Claimed CDR was "NOT included" in backup (contradicted by others)
   • Critical error: In clarification, claimed --restorelegacycdr flag doesn't exist (false)
   • Pattern: Overly concise, missed important nuances
   • Corrections needed: Significant - had factual errors requiring correction

2. Gemini 2.5 Flash (Position 1)

   • Issues: Despite "Deep Research" mode, had CDR inclusion confusion
   • Pattern: Very detailed but sometimes contradictory
   • Notable: After initial deep research, subsequent responses were less thorough
   • Corrections needed: Moderate - mainly clarifications on CDR and timing

Interesting Findings

1. Writing Style Patterns:

   • I incorrectly associated academic citation style with Claude, but it was actually Gemini and Perplexity
   • Copilot's brevity made it prone to oversimplification and errors
   • Claude's uncertainty acknowledgment was its strongest quality

2. Research Depth vs Accuracy:

   • More citations ≠ more accuracy (Perplexity had most citations but not highest accuracy)
   • Claude's approach of stating uncertainties was more valuable than confident speculation
   • GPT o3's structured approach balanced detail with accuracy well

3. Error Patterns:

   • Copilot: Factual errors, possibly from oversimplification
   • Gemini: Inconsistencies between deep research and regular mode
   • Perplexity: Sometimes included unverifiable specific details
   • Claude & GPT o3: Fewer factual errors, more conservative claims

4. Consensus Building:

   • Items where 4-5 sources agreed were almost always correct
   • Single-source claims were often unverifiable or incorrect
   • The iterative process was essential - no single AI had complete accurate information

5. Mode Differences:

   • Gemini's "Deep Research" mode in first response was notably different from subsequent responses
   • Perplexity's research mode provided exhaustive citations but not always better accuracy
   • Regular chat modes sometimes provided clearer, more accurate summaries than research modes

Key Takeaway

The most reliable AIs (Claude Opus 4 and GPT o3) shared these characteristics:

• Acknowledged uncertainties rather than guessing
• Provided structured, clear information
• Made conservative claims that could be verified
• Focused on consensus information rather than edge cases

The least reliable (Copilot) tried to be too definitive and concise, leading to factual errors that required significant correction.