AI Model Comparison: Which LLM

LLM

Click to read the full definition in our AI & Automation Glossary.

for Which Play?

Your firm does not need model loyalty. The intelligence layer of every workflow is a commodity component you can swap in 90 seconds.

Most firms make the mistake of standardizing on a single provider because their IT director signed an enterprise agreement or their managing partner read a TechCrunch article. This is operational malpractice. Each model has distinct strengths. Deploy them surgically.

n8n workflows let you swap the underlying model with two clicks. No code changes. No prompt rewrites. Here's the deployment matrix for the Big Three across all 12 Plays.

Deploy For: Document extraction, client-facing drafts, RFP response, email composition.

Core Strength: Claude writes like a senior associate, not a chatbot. It avoids the robotic phrasing that plagues GPT outputs ("I hope this message finds you well", "I wanted to reach out", "circling back"). The 200k token context window is rock-solid stable. Feed it a 100-page contract and ask about clause 47 on page 83. It will not hallucinate or lose the thread.

Pricing (as of Q1 2025):

• Sonnet 3.5: $3 per million input tokens, $15 per million output tokens
• Opus 3: $15 input, $75 output
• Haiku 3: $0.25 input, $1.25 output (use for high-volume classification tasks)

Speed: Sonnet 3.5 is the fastest frontier model in production. Opus is deliberate but thorough.

Best-Fit Plays:

Play 3 (Dead Lead Reactivation): Claude generates outreach emails that pass the "would a partner actually send this" test. No cringe. No obvious AI tells. It mirrors your firm's existing communication style when you feed it 5-10 sample emails in the system prompt.

Play 4 (RFP Generator): Ingest the full RFP PDF (typically 40-120 pages). Claude extracts every requirement, maps them to your capability statements, and drafts section-by-section responses without dropping critical compliance items. We have never seen it miss a mandatory submission requirement when properly prompted.

Play 7 (Email Assistant): Provide 3-5 examples of a partner's actual sent emails. Claude will match tone, sentence structure, and signoff style. The output requires minimal editing.

Document Summarization: Superior at producing executive summaries that preserve nuance. GPT tends to over-simplify. Claude maintains the appropriate level of detail for professional services contexts.

Weakness: Slightly worse than GPT-4o at strict JSON schema adherence. If your workflow depends on perfectly formatted structured output for CRM

updates, test thoroughly.

Deploy For: Structured data extraction, CRM

updates, qualification scoring, complex reasoning chains.

Core Strength: GPT-4o is the industry standard for JSON generation. When you need a workflow to output a perfectly structured object array for Salesforce or HubSpot, GPT-4o delivers the most reliable schema compliance. The new o1 reasoning models (o1-preview and o1-mini) excel at multi-step logic problems, financial modeling validation, and technical due diligence.

Pricing (as of Q1 2025):

• GPT-4o: $2.50 per million input tokens, $10 per million output tokens
• GPT-4o-mini: $0.15 input, $0.60 output (use for simple classification and routing)
• o1-preview: $15 input, $60 output (reserve for complex reasoning only)
• o1-mini: $3 input, $12 output (faster reasoning for less complex tasks)

Speed: GPT-4o is extremely fast. o1 models are intentionally slow (they "think" before responding). Do not use o1 for real-time user-facing applications.

Best-Fit Plays:

Play 1 (Hands-Free CRM

): GPT-4o extracts clean contact records, meeting notes, and next actions from messy email threads. Output maps directly to your CRM

CRM

Click to read the full definition in our AI & Automation Glossary.

's API schema with minimal post-processing.

Play 2 (Lead Qualification): Excels at rigid scoring matrices. Provide a qualification rubric (budget range, decision timeline, authority level, pain severity). GPT-4o applies it consistently across hundreds of inbound leads without deviation.

Play 4 (RFP Generator): Reliable at merging boilerplate sections with custom content while maintaining consistent formatting. Handles conditional logic well (if client is in financial services, include SOC 2 attestation; if client is healthcare, include HIPAA compliance).

Play 9 (Meeting Prep): Aggregates CRM

notes, past proposals, email history, and LinkedIn activity into a structured pre-meeting brief. Output format is highly consistent.

Time Entry Automation: Best at parsing calendar events and email activity into billable time entries. Understands legal/accounting time entry conventions (0.1 hour increments, task code mapping, matter number extraction).

Weakness: Tone is noticeably artificial in client-facing content. Requires heavy editing for external communications. Context window (128k tokens) is smaller than Claude and Gemini.

Deploy For: Massive document sets, video processing, firm-wide knowledge base queries, bulk resume screening.

Core Strength: The 2-million token context window is a structural advantage. You can load your entire employee handbook, all standard operating procedures, every template library, and 50 recent proposals into a single prompt. No vector database required. No chunking strategy needed. Just dump the files and query.

Pricing (as of Q1 2025):

• Gemini 1.5 Pro: $1.25 per million input tokens (up to 128k), $2.50 per million (128k-2M), $5 per million output tokens
• Gemini 1.5 Flash: $0.075 input (up to 128k), $0.15 (128k-2M), $0.30 output (use for high-volume, low-complexity tasks)

Speed: Moderate. Slower than Sonnet 3.5 and GPT-4o but acceptable for batch processing workflows.

Best-Fit Plays:

Play 10 (Hiring Screening): Load 50 resumes (full text, not summaries) plus the complete job description, your firm's culture deck, and examples of high-performer profiles. Gemini ranks all candidates in a single API

call with detailed justification for each ranking.

Play 11 (Knowledge Base Q&A): If your firm's reference library totals under 1.5 million tokens (roughly 1.1 million words or 3,000 pages), skip the vector database

entirely. Load everything into Gemini's context window. Answers are sourced from the actual documents, not embeddings approximations.

Contract Review: Can process multiple related contracts simultaneously (master service agreement + 12 statements of work + amendment history) and identify inconsistencies across the entire set.

Weakness: Output quality for creative writing tasks lags behind Claude. Tone is serviceable but generic. Not recommended for client-facing content that requires personality.

Write model-agnostic system prompts. Your instructions should work identically across Claude, GPT, and Gemini with zero modifications.

Bad Prompt (model-specific): "You are ChatGPT, a helpful assistant. Please analyze this document and provide insights."

Good Prompt (model-agnostic): "You are a senior legal analyst. Extract all indemnification clauses from the attached contract. Output as a numbered list with page references."

When OpenAI experiences an outage (they average 2-3 per quarter), you open n8n, swap the OpenAI node for Anthropic, and your firm continues operating. No downtime. No emergency Slack messages.

Default Stack for Professional Services Firms:

• Client-facing content: Claude Sonnet 3.5
• CRM

  CRM

  Click to read the full definition in our AI & Automation Glossary.

  automation and data extraction: GPT-4o
• Bulk document processing: Gemini 1.5 Pro
• High-volume classification: GPT-4o-mini or Haiku 3
• Complex reasoning (financial models, technical validation): o1-preview

Cost Optimization Rule: Start every new workflow with the cheapest model that might work (GPT-4o-mini or Haiku 3). Only upgrade to frontier models when quality issues emerge. We have seen firms cut AI costs by 60% by properly tiering their model usage.

Testing Protocol: Run the same prompt through all three models on 10 real examples from your firm. Measure output quality, edit time required, and cost per execution. The "best" model is the one that minimizes (cost + editing time), not the one with the most impressive benchmark scores.

Update this comparison quarterly. Model capabilities and pricing shift rapidly. What is true today may be obsolete in 90 days.