We Ran the Same 10 Prompts Through Every Major AI Model. Here's What Happened.

We took 10 prompts across different task types — writing, analysis, coding, research, creative work — and ran each through Claude Opus 4.8, GPT-5.5, Gemini 3.1 Pro, and Grok 4.3. The same prompt, the same temperature, evaluated on the same criteria.

The results confirmed some conventional wisdom and overturned some of it.

The prompts

Write a 400-word blog introduction about remote work productivity
Explain quantum entanglement to a high school student
Write a Python function to find duplicate values in a nested dictionary
Analyze the business risk of a small business that relies on a single supplier
Write a cold email for a freelance graphic designer targeting tech startups
Summarize the tradeoffs between REST and GraphQL APIs
Create a fictional opening chapter for a thriller novel (200 words)
Write a LinkedIn post about a career pivot to AI
Debug: explain why this SQL query returns duplicate rows [with example query]
Create 5 headline variations for a landing page selling productivity software

The results

Writing (Post 1, 5, 8, 10)

Long-form introduction (Post 1): Claude Sonnet 4.6 produced the most natural-sounding introduction with the best rhythm and least editorial work needed. GPT-5.5 had a stronger opening hook but drifted toward generic phrasing mid-paragraph. Gemini was structurally fine but had the most AI-textured prose. Grok 4.3 was competitive but slightly more formulaic.

Winner: Claude (clear)

Cold email (Post 5): GPT-5.5 produced the most conversion-focused email with tighter language and a cleaner CTA. Claude's email was warmer and more natural-sounding but slightly less punchy. Both were usable with minimal editing.

Winner: GPT-5.5 (slight edge)

LinkedIn post (Post 8): close contest. GPT-5.5 produced the better hook; Claude produced the better body and closing. Gemini produced a solid but generic post. Grok 4.3 was competitive.

Winner: Tie (GPT hook + Claude body = best result)

Headlines (Post 10): GPT-5.5 generated the highest-variance set of headlines — the weakest were weaker but the strongest were stronger. Claude's set was more consistent in quality but less diverse. For A/B testing, GPT's variety is an advantage.

Winner: GPT-5.5 for volume/variety, Claude for consistent quality

Technical (Post 3, 6, 9)

Python function (Post 3): all four models wrote correct code. Claude added type hints and docstring unprompted. GPT-5.5 wrote cleaner, more minimal code. Gemini's solution was correct but slightly verbose. Grok 4.3 was fast and clean.

Winner: Tie (Claude for documentation-first development, GPT for clean minimal code)

REST vs GraphQL (Post 6): Claude and GPT-5.5 produced equivalently strong analyses. Gemini's was slightly less nuanced but faster. Grok 4.3's was solid.

Winner: Tie (Claude/GPT)

SQL debugging (Post 9): all four models correctly identified the source of the duplicate rows and explained why. Differences were in explanation quality — Claude's was the most thorough, GPT-5.5's was the most concise. Both correctly identified the fix.

Winner: Claude for thorough explanation; GPT-5.5 for quick answers

Explanatory (Post 2)

Quantum entanglement (Post 2): Claude produced the most beginner-friendly explanation with the best analogies. GPT-5.5's was accurate and clear but slightly more technical in vocabulary. Gemini's was good and fast. Grok 4.3's was accurate but occasionally used more technical terms.

Winner: Claude

Creative (Post 7)

Thriller opening (Post 7): the most contested result. Claude's prose had the best literary quality — more atmospheric, better sentence rhythm, more vivid. GPT-5.5's was more plot-driven with a stronger opening hook but less nuanced writing. Gemini's was competent. Grok 4.3's was fast but the prose was flatter.

Winner: Claude (literary quality); GPT-5.5 (hook strength)

Research-grounded (Post 4)

Business risk analysis (Post 4): this task benefited from being asked with and without current context. Claude and GPT-5.5 produced equivalent analyses from training data. Gemini, given live search access, incorporated current case studies of supplier-dependent business failures from 2025-2026 — making the analysis more grounded in recent examples.

Winner: Gemini (with search grounding) for research-grounded analysis

What this shows

Claude leads on long-form writing quality, creative prose, thorough explanation, complex technical analysis. It's the model that most consistently sounds like a human wrote it.

GPT-5.5 leads on hooks, short-form commercial copy, variety in creative outputs, and tight concise answers when you want less.

Gemini leads on any analysis that benefits from current data — its search grounding gives it information the others can't access.

Grok 4.3 is competitive but rarely #1 on quality-sensitive tasks at this tier. Its advantages are price, speed, and context window — not output quality on the tasks tested here.

The honest conclusion

No single model dominates across all task types. The performance gap between Claude and GPT-5.5 on most tasks is smaller than most people think — usually a matter of preference and style rather than dramatically better quality. The clearest differentiation is where Gemini's live search access is relevant, and where Claude's prose quality on long-form content matters.

The implication: for anyone who genuinely uses all four models for different tasks, having access to all of them — rather than maintaining loyalty to one — produces noticeably better overall output.