Claude, GPT, and Gemini have genuine architectural differences that produce different strengths. Writing the same prompt for all three leaves quality on the table. Each has a native idiom.
Knowing the differences is the gap between a 6/10 and a 9/10 on high-stakes tasks.
02 Weak vs. Strong
EX 01Same code review, three native formats
Task: Security review of this Go handler.
══ CLAUDE FORMAT ══
<role>Staff Go engineer, production security focus</role>
<task>Review for: SQL injection, auth bypass, data exposure, error handling gaps</task>
<code>
func GetOrder(w http.ResponseWriter, r *http.Request) {
id := r.URL.Query().Get("id")
order, _ := db.QueryRow("SELECT * FROM orders WHERE id = " + id).Scan(...)
json.NewEncoder(w).Encode(order)
}
</code>
<output_format>Numbered findings: [SEVERITY] quoted line → attack vector → working fix</output_format>
══ GPT FORMAT ══
System: Staff Go engineer, production security focus.
User: Review for security issues. Return JSON only:
{"issues":[{"line":"quoted code","severity":"CRITICAL|HIGH|MEDIUM","attack":"string","fix":"working go code"}]}
[paste code]
══ GEMINI FORMAT ══
Staff Go security engineer. Review for production issues.
Markdown table ONLY: Line | Severity | Vulnerability | Fix
Quote the actual vulnerable code in Line column.
[paste code]
→ Why it works
Claude gets XML structure. GPT gets JSON schema. Gemini gets markdown table. Same task, three native idioms.
EX 02When to use which model
Model selection guide:
Claude (Sonnet/Opus):
• Document analysis: contracts, research papers, codebases up to 200K tokens
• Nuanced long-form writing: strategy docs, complex technical explanations
• Multi-step reasoning with auditable thinking (extended thinking on Opus)
• Instruction-following in structured production pipelines
GPT-4o:
• Code generation with JSON output schemas
• Agents with function/tool calling (Assistants API)
• Tasks needing broad world knowledge + live search
• Consistent structured output at scale
GPT o1/o3:
• Hard math or algorithmic problems
• Complex multi-step planning requiring deep reasoning
Gemini 2.5 Pro:
• Multimodal: images, PDFs, and video together
• Very long context (100K–1M tokens: full codebases, document libraries)
• Research needing live Google Search grounding
• Google Workspace integration (Gmail, Docs, Drive)
Smaller models (Haiku, GPT-4o-mini, Gemini Flash):
• Classification, extraction, or routing tasks
• High-volume pipelines where cost compounds
• Tasks with clear right/wrong answers
→ Why it works
Model selection has real quality AND cost implications.
03 Key Points
01Claude: XML tags, 200K context, nuanced reasoning, instruction compliance
02GPT: JSON mode, function calling, strong code generation, broad knowledge
03Gemini: multimodal (images/PDFs/video), 1M context, Google Search grounding