How to Choose Among 10 AI Tools? A Practical Guide Organized by Use Case

Why You Need a Clear Framework for Choosing AI Tools

ChatGPT, Gemini, Claude, Perplexity, NotebookLM… Faced with an overwhelming array of AI tools, most people either stick with just one or constantly switch between tools without knowing which is best suited for their current task.

An experienced creator shared his approach to using roughly 10 AI tools to accomplish 90% of his work. He breaks them down into two categories—"Everyday General AI" and "Specialized AI"—and identifies the one thing each tool does best. This mental model helps you decide in seconds: which tool should I use for this task?

Everyday General AI: The Big Three Each Have Their Strengths

ChatGPT: The Most "Obedient" Model

ChatGPT's core advantage can be summed up in one word—Obedience.

When you give an AI a complex set of instructions, ChatGPT is the one least likely to cut corners. Other models may be equally intelligent, but when faced with lengthy instruction sets, they sometimes skip steps or decide on their own that certain requirements aren't important.

In the large language model space, "obedience" is an informal but widely discussed evaluation dimension, referring to a model's ability to strictly follow user instructions rather than independently judging which ones matter and which can be ignored. This is closely related to model "Alignment" training—OpenAI heavily used "instruction following" as a reward signal during ChatGPT's RLHF (Reinforcement Learning from Human Feedback) training process, making the model inclined to execute user requirements item by item. In contrast, other models may prioritize "helpfulness" or "conciseness" during training, causing them to make autonomous trade-offs when facing lengthy instructions. This difference is particularly critical in enterprise applications—when you're building automated workflows with a dozen steps, any skipped step could cause the entire process to collapse.

An interesting way to verify this: ask each model to optimize a rough prompt for itself. ChatGPT will generate a noticeably longer, more detailed optimized version because it knows it can handle that complexity. When you then give the same optimized prompt to both ChatGPT and Gemini, you'll notice ChatGPT takes longer to think (because it's checking each requirement one by one), while Gemini tends to take shortcuts.

Real-world example: Give both models the same hiring evaluation criteria containing over a dozen requirements. ChatGPT delivers on every single one, while Gemini's output looks fine at first glance, but cross-referencing against the original checklist reveals it quietly dropped several rules.

Rule of thumb: If a task has many interconnected steps where missing one would cause the whole thing to fail, choose ChatGPT.

Gemini: The King of Multimodal Processing

Gemini's core advantage lies in its native multimodal processing capability. It can truly "watch" videos and "listen" to audio, while ChatGPT and Claude can only access this information through indirect means.

To understand the deeper implications of this advantage, you need to distinguish between "native multimodal" and "pipeline multimodal" architectures. Native multimodal means the model learned text, image, audio, and video representations simultaneously during pre-training—these different modalities share the same representation space within the model, enabling genuine cross-modal understanding. Pipeline multimodal (like early GPT-4V) first uses specialized encoders to convert images or audio into text descriptions, then feeds them to the language model—information inevitably loses detail during this conversion. Google DeepMind adopted a from-scratch multimodal training strategy when developing Gemini, enabling it to directly understand temporal changes in video, tonal emotions in audio, and other non-textual information.

In terms of media type support, only Gemini can natively process all four media types: video, audio, images, and text. Combined with its 1-million-token ultra-large context window, it can simultaneously handle long video recordings, hour-long audio files, and complete slide decks—content volumes that would overwhelm other models. This ultra-large context window is based on an architectural optimization called "Mixture of Experts" (MoE), which allows the model to process extremely long inputs without proportionally increasing computational costs.

Real-world example: Suppose you just finished a weekly meeting and have a video recording, a 20-page slide deck, and a messy whiteboard photo. You can upload all three to Gemini and ask it to summarize the discussion, extract key decisions, and draft follow-up emails. Gemini is the only tool that can comprehensively process all three materials at once.

Rule of thumb: When a task involves video, audio, or very large files, Gemini is the clear choice.

Claude: The Best "First Draft" Performer

Claude's core advantage is higher quality first-time output—its first draft is typically closest to "done."

This advantage manifests in two areas:

1. Coding Ability

An interesting fact: the latest Gemini beats the older Claude on almost every benchmark—except coding. The developer community widely agrees that Claude's consistency in generating runnable code on the first attempt is superior to other models.

Claude's advantage in coding is closely tied to Anthropic's training strategy. Anthropic uses a "Constitutional AI" training approach that emphasizes output consistency and reliability—qualities especially important in code generation, where code either runs or it doesn't, with no middle ground. In evaluations like SWE-bench (Software Engineering Benchmark), Claude models consistently perform well, particularly in first-attempt success rates for generating executable code.

The author's personal experience: He needed to bulk-export conversation records from a customer service platform, and support said only a developer could do it. He described the problem to Claude, which not only provided step-by-step guidance but also wrote a Go script—it ran successfully on the first try. The author didn't even know what Go was. Go (Golang) is a programming language released by Google in 2009, known for its clean syntax and high concurrency performance, commonly used for backend services and API development. Claude's ability to generate a runnable Go script for a non-programmer demonstrates its code generation practicality—not just syntactically correct, but properly handling API authentication, error handling, and data parsing.

2. Copy Polishing

Claude's text drafts sound more human-written and require fewer edits. It's particularly excellent at style matching—once you share samples of your existing work, it can almost perfectly replicate your tone.

Rule of thumb: When you need to generate runnable code or high-quality final copy, choose Claude.

How the Three Work Together in Practice

In real work, these three tools can be used in sequence:

• Starting phase: ChatGPT or Gemini handles brainstorming, research, and drafting outlines
• Finishing phase: Claude polishes rough output into publishable final products

Regarding Grok, its advantage lies in direct access to Twitter/X's real-time data stream, making it suitable for those who need to analyze breaking news. But the core principle is: Don't use a tool for the sake of using it—only add it to your toolkit when it solves an actual problem you have.

According to research based on Open Router data, models from different labs (like ChatGPT and Gemini) collectively expand AI's application landscape precisely because they each excel at different things. Open Router is a unified AI model API gateway that allows developers to call models from OpenAI, Google, Anthropic, and other providers through a single interface—its usage data provides valuable first-hand insights into the real-world application scenarios of different models.

Specialized AI: Precision Tools for Precision Scenarios

Perplexity: A Search Engine for Fast, Accurate Information

First, let's clear up a common misconception: Perplexity is not a foundation model. It's a product fine-tuned on existing foundation models, specifically optimized for search scenarios.

Perplexity's core technology is Retrieval-Augmented Generation (RAG). Traditional large language models' knowledge is cut off at the time of their training data, and they tend to fabricate answers when uncertain. The RAG paradigm retrieves relevant information from external data sources (like live web pages) before generating an answer, injecting the retrieved content as context into the model, thereby significantly improving response timeliness and accuracy. Perplexity deeply optimizes on this foundation: it performs intent analysis and query rewriting on user queries, retrieves information from multiple sources simultaneously for cross-verification, and ultimately generates answers with clearly cited sources.

Key distinction: General chatbots help you think, brainstorm, and draft; Perplexity is built for "retrieval"—you need a specific fact, and you need it now.

Real-world examples:

• Planning a Japan trip → use ChatGPT (creative task requiring trade-offs and narrative building)
• Checking whether a specific restaurant is foreigner-friendly → use Perplexity (needs accurate, real-time information)
• Verifying whether Gemini's context window is 1 million or 2 million tokens → use Perplexity (fact-checking)

Pro tip: You can use Google-style search operators like site:reddit.com to limit results to specific sources. This feature inherits from traditional search engine query syntax, letting users precisely control the scope of information sources—especially useful when you need information from specific communities or authoritative websites.

Rule of thumb: Treat Perplexity as an AI-upgraded version of traditional search engines—use it for retrieving information, not as a replacement for general chatbots.

NotebookLM: A Zero-Hallucination Document Q&A Assistant

NotebookLM's core advantage is that it only answers questions from the sources you provide, meaning it virtually never fabricates content.

It's like a "walled garden": you upload documents, and NotebookLM answers questions based solely on those documents. Since it has no external knowledge to draw from, it essentially cannot hallucinate.

From a technical implementation perspective, NotebookLM achieves "zero hallucination" through a core mechanism called "Grounding"—forcing every model output to be anchored to user-provided source documents. The system first chunks and vectorizes uploaded documents for indexing. When a user asks a question, it finds the most relevant document fragments through semantic search, then provides these fragments as the sole context to the underlying language model (currently based on Gemini), with system prompts explicitly requiring the model to "answer only based on the provided context, and clearly state if the context contains no relevant information." This design is essentially a constrained RAG system—the retrieval scope is strictly limited to user-uploaded documents. "Hallucination" is a known problem with large language models, referring to the model generating content that seems plausible but is actually incorrect or fabricated. Its root cause is that the model's generation mechanism is based on probabilistically predicting the next token rather than fact-based reasoning. NotebookLM solves this problem through architectural constraints rather than relying solely on prompts, making it significantly more reliable than simply telling a general model "don't make things up."

Real-world examples:

• While working at Google, uploading final drafts and source documents before publishing marketing materials, having NotebookLM check whether drafts contain any claims that contradict the sources
• Before making a video, uploading the script and all research materials, having NotebookLM flag any content not directly supported by sources

Important caveat: Output quality depends on input source quality. If the sources themselves are wrong, NotebookLM will "confidently make mistakes." This is the classic computer science principle of "Garbage In, Garbage Out"—no system, however sophisticated, can produce correct output from incorrect input.

Rule of thumb: When accuracy matters more than creativity and you have source materials to verify against, use NotebookLM.

Other Specialized AI Tools Worth Noting

Beyond the core tools above, several other AI tools excel in specific scenarios:

• Gamma: An AI-powered presentation tool that automatically generates visually polished slides from text content, dramatically shortening the time from content to presentation
• ElevenLabs: A high-quality voice cloning and synthesis platform based on deep learning technology. It can clone a speaker's timbre, tone, and rhythm from just a few minutes of voice samples, widely used in podcast production, audiobooks, and multilingual dubbing
• Zapier/N8N: Workflow automation tools. Zapier is a commercial no-code automation platform, while N8N is an open-source alternative. Both can connect different applications and AI tools to achieve "when A happens, automatically do B" workflow automation
• Excalidraw/Napkin AI: Quick visualization and diagramming tools, ideal for turning abstract concepts into intuitive visual representations—especially useful during brainstorming and communicating proposals

Summary: The Core Mental Model for AI Tool Selection

Do you really need all these tools? The answer is no. For most people, a paid ChatGPT subscription is sufficient—the key is mastering it thoroughly. But if your workflow can genuinely benefit from different tools' unique strengths and your budget allows multiple subscriptions, then mix and match as needed.

Quick decision guide:

Task Type	Recommended Tool
Complex instructions, high obedience requirements	ChatGPT
Video/audio/large file processing	Gemini
Code generation, final copy	Claude
Quick fact queries	Perplexity
Document-based zero-hallucination Q&A	NotebookLM

Remember: Tools exist to solve problems, not to be collected. Finding the right combination for your work scenarios matters more than blindly pursuing "having them all."