The Smartest Chatbot for Your Work: How to Compare Answers
There is no single smartest chatbot — the best one for your work depends on the task. This guide shows how to compare ChatGPT, Claude, Gemini and others on what you actually do, and how to keep the results of that comparison.
Updated 4 min read
The smartest chatbot is the one that gives you the most useful answer on the task you are doing right now. That changes by task: the best choice for writing a legal summary is different from the best choice for debugging code or planning a marketing campaign. Knowing which chatbot is best overall matters less than knowing which one is best for your specific work.
The way to find out is to run the same task in each and compare the results. Tools like fenbs help by keeping a consistent brief across sessions so you are comparing apples to apples, not asking a slightly different version of the question each time.
Why "smartest" is the wrong question
Intelligence rankings for AI chatbots use benchmarks — standardized tests on math, reading comprehension, coding and science. These tests are useful for researchers comparing model capabilities, but they do not predict performance on your specific prompts, your domain vocabulary, or the output format you need.
A chatbot that scores first on a coding benchmark may still give you a worse answer on your particular codebase than one that scores second. The benchmark tests a representative sample of tasks; your actual work may not be representative. Trust the comparison you run yourself.
The main chatbots to compare
| Chatbot | Known strengths | Where to check current plans |
|---|---|---|
| ChatGPT (OpenAI) | Broad coverage, versatile, large ecosystem of integrations, strong at tasks that benefit from step-by-step reasoning (o-series models). | openai.com/chatgpt |
| Claude (Anthropic) | Long-form writing, careful reasoning, large context window, nuanced editing. | anthropic.com/claude |
| Gemini (Google) | Google Workspace integration, large context, multimodal input (images, documents), Google Search integration. | gemini.google.com |
| Perplexity | Cited sources from the web, good for research questions that need current information. | perplexity.ai |
| Microsoft Copilot | Built into Microsoft 365 and Windows; useful for users already in the Microsoft ecosystem. | microsoft.com/copilot |
| fenbs | Keeps the test context, decisions and results of your comparison outside any one chatbot so you do not repeat the evaluation from scratch. | Not a chatbot; pairs with the tools above. |
How to run a meaningful comparison
A comparison that produces useful results needs a consistent method. The key is to use the same prompt in each chatbot, judge the output on consistent criteria, and write down the results while you can still distinguish one answer from another. Memory is unreliable when you are switching between several tools in a row.
- Choose two or three tasks from your actual work. One writing task, one research question, one analytical task.
- Write the prompts carefully before you start. Use the same wording in each chatbot — do not optimize it for one tool.
- Run the task in each chatbot and save the outputs.
- Rate them on criteria you decide in advance: accuracy, relevance, format, clarity, how much editing you would need to make the output usable.
- Record the scores and your notes. One chatbot may win on writing but lose on research.
What the o-series and extended thinking models add
OpenAI's o-series models (o3, o4-mini) and Claude's extended thinking mode add a reasoning step before the response. For tasks that require working through several logical steps — complex analysis, multi-step math, argument structures — these modes can produce noticeably better answers. For simple questions or creative writing, they add latency without improving the output.
If you are comparing chatbots on reasoning-intensive tasks, include the reasoning-mode versions in your comparison. A standard GPT-4o response and an o3 response on the same problem can be meaningfully different.
Comparing answers on writing tasks
For writing, the useful comparison dimensions are tone consistency, whether the structure fits the document type, how much editing the output needs, and whether the argument holds together over a long piece. Claude is often preferred for long-form writing; ChatGPT handles a broader range of formats well.
Run the comparison on a document type you actually produce: a client proposal, a technical explanation, a contract summary, a project update. A comparison on a generic "write an essay about X" prompt does not tell you much about your real work.
Comparing answers on research and factual questions
For research, check whether the answer includes sources, whether the sources are authoritative for your field, and whether the claim matches what the source actually says. Perplexity cites sources inline, making it easy to check. ChatGPT and Gemini include sources with web search enabled; Claude can search with it enabled too.
All AI chatbots can produce plausible-sounding incorrect claims. The smartest-seeming answer that cites no sources is less useful than a well-cited answer you can verify.
Try a different chatbot without losing the context
Connect your next chatbot to the fenbs project and ask it to read the context. The current tasks, decisions and Where we left off are the same. If the new chatbot is not better for your work, connect the previous one again — no briefing required.
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How to do this with fenbs
- Create a free account and start a project for the comparison.
- Write the test prompts and evaluation criteria in the project context.
- Connect your first AI app from /integrations and ask it to read the context.
- Run the test tasks and save the results in tasks in fenbs.
- Connect a second app and repeat. Compare the results from the same brief.