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LLM Model Selection for Dataset Generation Experiments

This document outlines the 8 selected large language models (LLMs) from Anthropic, OpenAI, Google, and xAI for dataset generation and benchmarking. Each provider is represented by 2 models to ensure balanced comparison across the LLM landscape.

Provider Model Name Type Model Size Key Strengths Notes
OpenAI GPT-3.5 Turbo Fast/Efficient ~175B parameters[¹] Fast responses, cost-effective, general-purpose Conversational AI baseline
GPT-4o Balanced ~200B parameters[²] Speed-optimized, multimodal (voice/image/web), general-purpose Fast with versatility
Anthropic Claude Sonnet 4 Flagship ~175B parameters[³] Superior reasoning, extended thinking model, emotional intelligence, coding excellence Best for depth & quality
Claude Haiku 3.5 Fast/Efficient ~20B parameters[⁴] Speed-optimized, cost-effective, general tasks Quick responses, efficient
Google Gemini 2.5 Pro Flagship Undisclosed (est. >1T)[⁵] Adaptive thinking model, multimodal native, methodical reasoning, coding (63.8% SWE-Bench) Deliberate problem-solving
Gemini 2.5 Flash Balanced Undisclosed (est. 10–30B)[⁶] Speed-quality balance, thinking model, general-purpose Fast with good performance
xAI Grok 4 Advanced ~1T+ parameters[⁸] 256K context, parallel reasoning, tools-native, real-time data, uncensored Best raw reasoning (50.7% HLE)
Grok 3 Flagship ~300B parameters[⁷] Real-time X/Twitter data, "Big Brain" mode, conversational Deep Search capability

Key Notes:

  • Thinking Models: Sonnet 4, Gemini 2.5 Pro, and Gemini 2.5 Flash feature advanced reasoning capabilities with enhanced problem-solving abilities.
  • Selection Rationale: Each provider is represented by flagship and advanced/balanced models for comprehensive comparison across 4 major LLM providers.
  • Focus: Quality and capability over cost considerations for research purposes.
  • xAI Addition: Grok models provide unique real-time data capabilities and alternative architectural approaches for diverse dataset generation.
  • Grok Specifications: Both models support 131K output tokens with identical pricing ($3/$15 per 1M tokens, $0.75 cached input). Grok 3: 131K context, 600 RPM. Grok 4: 256K context, 2M TPM, 460 RPM.

Model Capabilities Deep Dive

Reasoning & Thinking Models

  • Claude Sonnet 4: Features "extended thinking with tool use" mode, allowing internal problem decomposition and scratchpad reasoning. Excels at emotional intelligence and nuanced understanding.
  • Gemini 2.5 Pro: Uses adaptive thinking controls and parallel thinking techniques. Methodical, step-by-step approach ideal for debugging and complex problem-solving.
  • Grok 4: Implements parallel processing with multiple thought chains. "Big Brain" mode leverages additional compute for complex problems. Tools-native training gives edge in logic-heavy tasks.

Model Specializations

  • General Purpose: GPT-3.5 Turbo, GPT-4o, Haiku 3.5, Gemini 2.5 Flash
  • Coding Focus: Claude Sonnet 4 (superior for code review/debugging), Gemini 2.5 Pro (63.8% SWE-Bench)
  • Reasoning/Analysis: Grok 4 (50.7% HLE with tools), Claude Sonnet 4 (75% AIME with extended thinking)
  • Multimodal: GPT-4o (voice/image/web), Gemini 2.5 Pro (native multimodality)
  • Real-time Data: Grok models (X/Twitter integration, Deep Search)

Performance Trade-offs

  • Speed vs Depth: GPT-4o and Haiku 3.5 prioritize speed; Sonnet 4 and Gemini 2.5 Pro prioritize quality
  • Cost vs Capability: Haiku 3.5 and GPT-3.5 Turbo offer budget options; flagship models justify higher costs with superior performance
  • Generalist vs Specialist: GPT models are versatile generalists; Claude excels at text/code; Grok leads in raw reasoning

Sources for Unofficial Parameter Estimates:

  • [¹] GPT-3.5 Turbo: OpenAI's published parameter count, widely used baseline model with proven performance characteristics.
  • [²] GPT-4o: Estimated parameter count from community analysis and benchmarking (reddit.com discussion).
  • [³] Claude Sonnet 4: Approximate parameter size based on community estimates (Anthropic's model tiers on Reddit).
  • [⁴] Claude Haiku 3.5: Estimated at ~20B from community discussion (Anthropic Model Estimates on Reddit).
  • [⁵] Gemini 2.5 Pro: Speculated parameter size is undisclosed but widely assumed large-scale (over 1T) based on industry analysis (arXiv paper on Gemini).
  • [⁶] Gemini 2.5 Flash: Community estimation on smaller, optimized variant (Gemini Flash Discussion).
  • [⁷] Grok 3: Estimated parameter count from xAI announcements, 131K context/output tokens, 600 RPM, $3/$15 per 1M tokens (xAI API Documentation).
  • [⁸] Grok 4: Advanced model with 256K context window, 131K output tokens, 2M TPM, 460 RPM, $3/$15 per 1M tokens (xAI Official Specs).