Which AI Tool Should I Actually Use? A No-BS Guide for Social Science Researchers
How I learned to stop worrying and use multiple AIs
If you’re like most social science researchers I know, you’ve probably tried ChatGPT, maybe Claude, and wondered, “Which AI should I actually be using for my work?” The answer, unfortunately (or fortunately?), is “it depends.”
Let’s be honest: the AI landscape is a mess of model names and hype. You aren't concerned with benchmarks—you just want to get your literature review, coding, or analysis going without drowning in token limits or copy-pasting context between chats.
Here’s how to choose based on what you’re actually doing.
The Two-Minute Decision Tree
1. Complex, Document-Heavy Workflow?
You’re juggling 15 PDFs, a dataset, and half-written R/Python code. You need the AI to remember everything.
→ Use Claude Projects (web/apps) or Google AI Studio (web) with Gemini 2.5 Pro.
Claude Projects handles massive context windows seamlessly—your entire research project stays in one place. Gemini 2.5 Pro in AI Studio has killer Google Drive integration (just point it at a folder) and lets you adjust a “thinking budget” for ambiguous, multi-perspective problems. Perfect for synthesizing contradictory papers or exploring methodological tradeoffs.
2. Overwhelmed by Too Much Material?
You have 50+ documents, interview transcripts, or audio files and need help identifying themes, connections, and gaps.
→ Use NotebookLM by Google.
Upload your sources, and it automatically creates summaries, mind maps, and even AI-generated podcasts that explain your material back to you. The mind mapping feature is particularly powerful: create separate maps for different document sets (e.g., archival data vs. theoretical papers), then compare them to spot connections you might have missed.
Bonus: It automatically transcribes audio files, making it invaluable for multimodal data projects.
3. Quick Gut Check or One-Off Question?
You need a fresh pair of eyes on a paragraph or a sanity check on an interpretation. No need to carry this conversation forward.
→ Use Kimi.
It’s fast, sharp, and perfect for one-off checks. Think of it as a reliable colleague that you can ping when you are unsure about a particular code or interpretation.
The “One AI” Problem (And How to Hack It)
It’s tempting to pick one tool and stick with it. But none are great at everything. The real friction? Losing your work when you switch.
Solution: Make the AI write you a memo.
After any substantive conversation—say, after you’ve worked through a coding scheme or mapped out a theoretical framework—ask:
“Generate a standalone memo summarizing what we discovered, why it matters, and what specific changes to make to [document/code]. Assume future AI systems won’t have access to this chat. Include full context: what to modify, how to modify it (e.g., line numbers, exact text), and the rationale.”
Now you can paste that memo into any AI (Claude, Gemini, or ChatGPT) for seamless handoffs. It’s like giving your colleague a perfect briefing doc instead of making them read your entire email thread.
But here’s the real benefit: The memo isn’t just for AI handoffs—it’s for you.
This is basic qualitative research practice. When you review that memo, you get to:
Clarify your own thinking
Spot issues you missed in the heat of the conversation
Surface biases or assumptions you hadn’t noticed
See if the AI actually understood your research context
Think of it as member-checking your AI collaborator. If something in the memo feels off or unclear, that’s valuable information about where your analysis needs work. Always add your own notes to the memo. Treat it as a living document, not just a handoff artifact.
Pro tip: Test your memo by feeding it to a different model than the one that wrote it. If the second AI can execute the task cold, your memo is good enough.
For the Tinkerers: API Clients
Optional: skip if you prefer sticking with standard web interfaces.
If you’re comfortable getting API keys and using tools like Cherry Studio, you can treat models as interchangeable building blocks. These clients let you:
Export chats automatically to a local folder
Tag and search past conversations
Run multiple models side-by-side in the same thread to compare perspectives on the fly
This is the “model-agnostic” workflow: use Claude for deep analysis, Gemini for large context windows, and Kimi for speed (and costs)—whatever fits the subtask. Optionally, the client also handles the memory management.
Try it out: Follow the instructions here at Research Memex to set up Cherry Studio with your own API keys
Model Cheat Sheet for Social Science Work
For lit reviews, qualitative coding, visualization scripts, or theory-building:
Top Tier
Claude models: Still the best for nuanced reasoning and long documents. Pricey, but worth it for core projects where interpretive depth matters.
Gemini 2.5 Pro: Best value. Drive integration + thinking budget = long context magic. Great for working with archival data stored in Drive.
Kimi K2 (Thinking/Turbo): Low cost, excellent for multilingual sources and quick deep dives into unfamiliar literatures.
ChatGPT (GPT-5 Thinking): If your task requires live web search or iterative fact-checking. It’s optimized for tool-calling, which is great for red-teaming AI outputs or verifying citations across multiple sources.
Worth Experimenting
Grok 4: Underrated.
GLM-4.6: Surprisingly solid.
Minimax M2: Haven’t tested it myself. Worth experimenting.
The Bottom Line
Stop asking, “What’s the best AI?”
Start asking, “What’s this task asking for?”
Deep, archival work? Claude/Gemini Projects.
Document overload and theme identification? NotebookLM.
Lightning round? Kimi.
Switching between tools? Memo everything.
Web sleuthing? ChatGPT.
Your workflow will be messier than a single-app evangelist’s—and far more effective.
The good news? Most of these AI tools are good enough that your choice matters less than knowing how to use them effectively. Start with one, get comfortable, and expand from there.
What’s working (or not working) in your AI workflow? Drop a comment—I’m always curious about what other researchers are discovering.