Which model wins Claude Opus vs GPT 5 for resume skills?
GPT-5 wins when you need a tight, ATS-safe skills section in fewer words. Claude Opus wins when your raw material is messy and you need the model to infer what your real strengths are from long project history, performance notes, or scattered bullets. Most people use the wrong order. Let Claude Opus expand the signal, then let GPT-5 compress it into a clean skills section rewrite that reads like a person, not a prompt template.
If you're searching claude opus 2026, the live comparison is not old Claude 3 versus GPT-4o. Anthropic's current lineup includes Claude Opus 4.5 and Sonnet 4.6, while OpenAI retired GPT-4o from ChatGPT on February 13, 2026 and moved the app to newer GPT-5.x models. That matters because prompt behavior changed: GPT-5 follows formatting constraints more tightly, and current Claude Opus is better at extracting buried skill patterns from long context. ([anthropic.com](https://www.anthropic.com/news/claude-opus-4-5?pubDate=20250519&utm_source=openai))
My rule is simple. Use GPT-5 for final resume output, Claude Opus for discovery, Gemini for job-description clustering, Perplexity for market research, Copilot when your raw material lives in Word or Outlook, and Grok when you want a fast angle on live company chatter. DeepSeek and Le Chat are solid alternates when you want low-friction drafting or multilingual rewrites. Meta AI is fine for quick reframes, but I wouldn't make it the last editor before you apply.
Why do most AI resume prompts fail?
Most AI resume prompts fail because they're lazy. Improve my resume gives the model zero evidence, zero target, and zero constraints, so it fills the gap with glossy nonsense. The output looks polished for five seconds, then collapses under scrutiny. Seniority disappears. Achievements get flattened into filler. Real signals turn into mushy claims like strategic thinker or results-driven leader. That's exactly the language recruiters skim past and ATS scoring can't reward.
A useful resume prompt always includes four things: the target role, the source evidence, the house rules, and the output format. Source evidence means your current CV, the job description, and a short brag doc with concrete wins like cut AWS spend 18 percent or grew outbound pipeline from 0 to 1.2 million dollars. House rules mean no invented tools, no adjectives without proof, no first person, and a separate skills section plus achievement bullets.
The framework that works is blunt: extract, map, compress, prove. First ask the model to extract hard skills, soft skills, domain knowledge, and tools from your past work. Then map them to the job description. Then compress duplicates. Then force proof by attaching each important skill to a project, metric, or system. If a skill can't be tied to evidence, it doesn't belong in the skills section. Most resume advice on this is wrong. Keyword stuffing is not strategy; evidence density is.
What did my GPT 5 resume test show?
My gpt 5 resume test kept producing cleaner final skills sections than Claude Opus, but Claude found better raw material. GPT-5 cut repetition, grouped overlapping tools faster, and stayed inside a strict format like Core skills, Platforms, Methods, and Certifications. Claude Opus wrote the more interesting draft. It surfaced transferable skills a recruiter would actually care about, like stakeholder alignment, experiment design, or incident ownership, even when those phrases weren't stated cleanly in the source CV.
Use this GPT-5 prompt when your experience is already solid and you need sharp packaging. GPT-5 prompt: You are a resume editor. Using my current CV and the target job description, rewrite only the skills section. Keep it ATS-friendly, factual, and compact. Group skills into 3 to 5 logical buckets. Remove duplicates and weak buzzwords. Keep every skill grounded in evidence from my experience. If a skill is not supported, drop it. Return plain text only, with no intro, no explanation, and no invented tools or certifications.
Use this Claude Opus prompt when your CV is messy, long, or undersells you. Claude Opus prompt: Read my CV, project history, and performance notes. Infer the strongest hard skills, domain skills, and execution skills that a hiring manager would trust for this target role. Show me three outputs: hidden strengths you detected, missing but provable skills I should surface, and a final skills section rewrite in my voice. Flag anything that sounds inflated, generic, or unsupported. Prioritize specificity over polish.
| Task | GPT-5 | Claude Opus | Gemini 3.1 Pro | Perplexity |
|---|---|---|---|---|
| Tight ATS formatting | ✓ Excellent | Good | Good | Average |
| Hidden skill extraction | Good | ✓ Excellent | Good | Average |
| Market language clustering | Good | Good | ✓ Excellent | Good |
| Live research on roles | Average | Average | Good | ✓ Excellent |
| Quick final polish | ✓ Excellent | Good | Good | Average |
Which prompts work best across every major LLM?
The best prompts are model-specific, not universal. In 2026, the lineup keeps moving: Gemini 3.1 Pro is Google's current complex-task model, Microsoft says Copilot is model-diverse with Claude and OpenAI options, Perplexity has added Computer, Grok 4.1 is live across grok.com and apps, Meta AI's app and site now run on Muse Spark, DeepSeek's latest published model is DeepSeek-V4, and Le Chat is available on web, iOS, and Android. ([blog.google](https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-3-1-pro/?utm_source=openai))
ChatGPT GPT-5 prompt: Turn my master resume and this job description into a recruiter-ready skills section and summary. Keep only skills I can prove. Claude Sonnet or Opus prompt: Find the strongest skills I am under-claiming, then rewrite them in plain English with proof signals. Gemini prompt: Compare five job descriptions for this role, cluster the repeated skill demands, then rewrite my skills section to match the market language without copying any employer verbatim.
Copilot prompt: Using my Word resume and the job description in this document, show tracked changes for a tighter summary, stronger action verbs, and a cleaner skills section rewrite. Perplexity prompt: Research the top skill requirements for senior product analyst roles at fintech companies this month, cite recent postings, and turn the findings into interview talking points. Grok prompt: Scan recent public discussion about this employer and role family, then suggest three resume angles and five interview questions I should be ready for.
Meta AI prompt: Rewrite my LinkedIn About section so it sounds sharp, social, and human, not like a corporate bio. DeepSeek prompt: Create three versions of my skills section for startup, enterprise, and remote-first roles, keeping the same evidence base. Mistral Le Chat prompt: Rewrite my CV in English and French, preserve metrics, and adapt the wording for EU recruiters. If you want speed, use Sonnet. If you want depth, use Opus. If you want a second opinion on structure, rotate in Gemini.
How do AI recruiters and screeners change the way you write skills?
AI recruiters and screeners reward clarity more than creativity. Workday's talent acquisition suite now folds in HiredScore AI, Greenhouse says its 2026 benchmarks drew on more than 6,000 companies and 640 million applications, and Lever still positions itself as an ATS plus CRM plus analytics stack. In plain English, your resume is entering systems designed to normalize, compare, and rank signals at scale. ([workday.com](https://www.workday.com/en-us/products/talent-management/overview.html?utm_source=openai))
That doesn't mean robots are hiring you alone. It means your skills section has to survive both parsing and downstream evaluation. HireVue says 77 percent of HR teams use AI weekly or daily and 85 percent plan to adopt generative AI in 2026, while its platform and Sapia.ai both push skills-based, AI-assisted assessment and interview workflows. A vague skills block full of leadership, communication, and team player won't survive that stack. ([hirevue.com](https://www.hirevue.com/blog/hiring/2026-global-ai-in-hiring-report-this-years-4-themes?utm_source=openai))
Here's the contrarian take: AI-proofing your CV is not about hiding that you used AI. It's about making the document too specific to be mistaken for generic AI output. Replace broad labels with operational detail: SQL becomes SQL for cohort retention analysis in Snowflake; stakeholder management becomes led weekly roadmap reviews across product, data, and sales; experimentation becomes ran 14 lifecycle tests and shipped 5 winners. The most AI-resistant career skills are judgment, prioritization, stakeholder management, experimentation design, and domain-specific problem framing because they show how you make decisions, not just what tools you touched.
How do you AI-proof your CV without sounding AI-generated?
You AI-proof your CV by tightening the evidence, not by deleting every polished sentence. Keep one clear claim per bullet, add the tool, scope, or metric that proves it, and cut any phrasing you'd never say out loud. If three bullets could belong to anyone, they're dead weight. A senior backend engineer at a Series B fintech sounds different from a customer success lead at a SaaS scaleup. Your resume should make that obvious fast.
My favorite workflow is two-pass. First, use Claude Opus or Sonnet to mine hidden strengths from messy history. Second, use GPT-5 to compress that material into ATS-safe output. Then run the draft through HRLens CV analysis to check keyword match, ATS scoring, and whether your skills section actually aligns with the job you want. That last step matters because even a good model can over-index on elegant phrasing and miss the exact tools, domain terms, or seniority markers recruiters screen for.
If your source material is weak, don't keep prompting your way around the problem. Rebuild the document. A cleaner master resume gives every model better raw material, whether you're using GPT-5, Claude Opus, Gemini, or Copilot. If you need a fresh structure, HRLens CV Builder is the faster move. Then reuse the same evidence bank for your LinkedIn About, cover letter, and interview stories. The only AI prompt you really need to land more interviews is the one that forces proof before polish.