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.
Task: Security review of this Go handler.
══ CLAUDE FORMAT ══ Staff Go engineer, production security focus Review for: SQL injection, auth bypass, data exposure, error handling gaps 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) } <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]
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
Native: XML structured prompts, long-context document analysis, extended thinking on Opus. Best for nuanced reasoning and long-form quality.