Choosing (and Combining) AI Assistants in 2025: What Practitioners Are Actually Doing

Many power-users don’t pick a single “best” model. They run ChatGPT, Claude, and Gemini in parallel, let them critique each other, then compose their own final answer.

Broad patterns from real-world use:

• ChatGPT = dependable day-to-day default; strong general programming support.
• Claude = standout for front-end/UI work, clear explanations, translation/proofing quality.
• Gemini = fast, and especially handy inside Google’s ecosystem (Meet notes, Sheets, Docs).
• Also seen in the mix: DeepSeek V3 (impressive coding help, often free), o1 (favorite “all-rounder” for some), Perplexity (web access/research), Grok (surprisingly solid on some calculus tasks), AugmentCode/Cursor (editor-integrated coding), and Microsoft Copilot (useful across Teams/Outlook email; Windows Copilot drew mixed reviews).

When each tool tends to shine

Programming

• Claude: Frequently preferred for front-end development; produces practical, clean code and clarifies trade-offs well.
• ChatGPT: Reliable default for general coding and everyday problem-solving.
• DeepSeek V3: Called out as strong for programming, with some users praising the experience and cost.
• o1: Considered by some as the best all-round model; others keep 4o for lighter tasks due to familiarity.
• Editor companions: Reports of AugmentCode outperforming Cursor on certain codebases; GitHub Copilot mentioned less often lately.

Writing & translation

• Claude: Repeatedly noted as best for translation/proofreading quality across tests (prompt quality still matters).
• Gemini: On some structured writing tasks (e.g., short research essays), newer variants produced denser, ready-to-use text where other models felt repetitive.

Research & web access

• Perplexity: Brought in specifically when web lookups and sourcing are key.
• Gemini: Good inside Google workflows (Meet, Sheets) and fast for quick fact-finding.

Math & STEM

• Grok: Surprised some users by doing better on certain undergrad-level calculus tasks.

Enterprise workflows

• Microsoft Copilot: Valued for Teams and email integration at work; Windows Copilot feedback was negative in at least one recent trial.

A simple playbook: Make your models “argue” constructively

Goal: harvest the best ideas fast, reduce blind spots, then author your own final output.

1. Parallel prompts

   • Pose the same task to ChatGPT, Claude, and Gemini.
   • Keep the prompt scoped (requirements, constraints, acceptance criteria).

2. Cross-critique

   • Ask each model to critique the others’ answers:

     • “List 3 strengths and 3 weaknesses in Solution A vs. yours.”
     • “Point out missing edge cases, security risks, performance pitfalls.”

3. Focused tiebreakers

   • Where the critiques disagree, run targeted follow-ups (benchmarks, small test inputs, or concrete examples).

4. You write the final

   • Use the best fragments from each, resolve conflicts, and author the final version yourself. (This was a common pattern: the AI gets you unstuck and speeds research; you own the final.)

Handy meta-prompt you can paste into any model:

“Critique the following solution against mine in terms of correctness, clarity, completeness, and edge cases. Provide a bullet list of strengths/weaknesses, then propose a merged, improved approach with concrete code or steps.”

Quick chooser (task → good first pick)

Practical tips that consistently help

• Rotate models, not just prompts. If output feels “watery” or repetitive, switch models before over-tuning the prompt.
• Pin acceptance criteria. Ask for tests, edge cases, performance notes, and a short “assumptions” section.
• Prefer short iterative loops. Smaller, verifiable steps beat one giant ask.
• Keep a “model diary.” Jot down which tasks each model nailed or fumbled; you’ll build a fast instinct for routing.

How Feluda supercharges your assistants (ChatGPT, Claude, Gemini)

Feluda adds a portable “skill layer” on top of any assistant you use. Each Skill Pack can include:

• Tools — API/MCP connectors and utilities (e.g., web scrapers, data fetchers, validators) that the assistant can call directly inside a session.
• Knowledge — curated corpora (docs, PDFs, notes, datasets) mounted as trusted context with citations and retrieval.
• Roles — purpose-built system instructions (tone, guardrails, objectives) that shape how the assistant thinks and responds.
• Resources — reusable templates, checklists, prompts, dashboards, and snippets to speed up common workflows.

Bottom line

There isn’t a single winner. Practitioners who get the most out of AI treat models like peers—they compare, cross-examine, and then synthesize. If you only adopt one habit, make it the parallel-prompt + critique loop and finish with a human-written final pass. It’s simple—and it works.