We Used 6 AI Tools on Real Client Projects for 6 Months — Here's What Actually Survived

Every comparison article runs the same test: give each AI a coding prompt, compare the outputs, declare a winner. That test is almost meaningless for production development.

The questions that actually matter are different. Does the model flag its own uncertainty, or does it generate confidently wrong code? How does it handle a 200-line codebase pasted for context? Does it maintain the constraints you gave it three exchanges ago, or does it quietly drift? What happens when you push it on edge cases it is not confident about?

We did not run structured benchmarks. We used these tools daily across real projects and noticed patterns. What follows is those patterns.

We use Claude more than any other AI on the team, and the reason is not capability — it is honesty.

Krishna built an invoice OCR extraction pipeline for ScrapCity, a B2B SaaS serving Maruti Suzuki dealerships. The pipeline needed to extract part names, quantities, and vehicle model data from scanned invoices, then cross-reference against an inventory system. The edge cases were nasty: handwritten corrections on printed forms, inconsistent part naming across dealerships, invoices where the same part appeared under three different line items.

When he worked through the architecture with Claude, the responses consistently included what the model was uncertain about. Not hedging for the sake of it — specific flags. 'This approach assumes the OCR output preserves table structure. If your scanner flattens the layout, you will need a different extraction strategy.' That kind of heads-up, given before you have committed two days to an approach, is worth more than fast code generation.

On the frontend side, I use Claude for anything touching production Redux logic or complex TypeScript generics. Not because it is faster than ChatGPT — it is not, especially during peak hours. But on tasks where a wrong answer has a real cost, I want the model that says 'I am not confident about this' rather than the model that sounds confident whether it is right or wrong.

The weak spots are real: slower response times, smaller plugin ecosystem, no image generation worth using. For rapid brainstorming or first-draft generation, ChatGPT is still the faster call.

ChatGPT is still the tool both of us open first for general tasks, and it earns that position. For writing a first-draft API route, generating test data structures, or brainstorming approaches to a new feature, it is faster and more versatile than anything else in our stack.

The limitation is the same one that caused the BanquetFirst webhook incident: ChatGPT generates confident output regardless of how confident it should actually be. Across six months, we noticed this pattern consistently — it produces code that looks correct and runs correctly in isolation, but misses systemic issues that only surface under load or in edge conditions.

We have not stopped using it. We have just stopped treating its output as production-ready without a review pass. The Custom GPTs feature is genuinely useful — I have one configured for writing StackNova blog briefs and another that generates Redux Toolkit boilerplate in our team's specific patterns. For repeatable, structured tasks, those are real time-savers.

For anything touching client-facing systems or database logic, ChatGPT output goes through a Claude review pass before it ships. That is the workflow we landed on after a few expensive lessons.

Both tools generate frontend code from prompts. That is where the similarity ends, and confusing them leads to the wrong choice.

I have used both. My honest take: v0.dev produces better UI. Lovable scaffolds a full app faster. Those are different jobs.

v0.dev (Vercel) generates React component code with noticeably better design sensibility. The output looks like something a frontend developer with real design taste produced. Layouts are tighter, spacing is considered, component structure is cleaner. For building UI that needs to look good — landing pages, dashboards, client-facing product screens — v0 consistently produces output closer to what you actually want to ship. I reach for v0 when I care about the visual result.

Lovable scaffolds entire application structures faster. It is stronger at wiring together full pages with routing, form handling, and data flow — not just individual components. Krishna used Lovable for ScrapCity specifically because he needed a full-app scaffold, not a single polished component. As a backend developer who needed a functional frontend he could wire to his Node.js APIs, Lovable gave him a working structure to iterate on. He was not optimizing for visual polish — he was optimizing for speed to something functional.

The honest ceiling on both: complex state management, deep business logic, and domain-specific validation need developer judgment on top of whatever they generate. Neither tool removes the need for the developer — they just collapse the time between blank canvas and reviewable first draft.

If your question is 'which one should I use' — ask what you actually need. Better looking UI with cleaner component code: v0.dev. Full app scaffold for a solo full-stack project where you are the backend developer crossing over: Lovable.

Before Claude became our go-to for production work, there was a period where the workflow was ChatGPT as primary, Gemini as secondary, and DeepSeek as the last resort when both failed to solve something.

That framing — last resort — is actually the most honest description of how DeepSeek entered our stack. There were debugging sessions, particularly on complex recursive logic and data pipeline issues in early EdgeNRoots work, where ChatGPT would give a confident answer that was wrong, Gemini would give a similar wrong answer slightly rephrased, and DeepSeek running locally would actually trace through the logic correctly and identify the problem.

It earns its place specifically on pure code reasoning tasks. Not writing tasks, not architecture discussions, not anything requiring broad knowledge — pure code. Given a specific function, a specific error, and enough context, DeepSeek's reasoning on what is actually happening in the code is genuinely strong. Stronger than its reputation suggests, particularly because most comparisons test it on general tasks where it is clearly weaker.

The other real advantage is local deployment. For EdgeNRoots projects handling booking data, OTA revenue calculations, or insurance industry pipelines — anything where client code leaving your infrastructure is a concern — DeepSeek running locally is the only option on this list that solves that problem. Claude and ChatGPT are cloud tools. DeepSeek does not have to be.

Once Claude arrived in the workflow, it displaced DeepSeek for most debugging and architecture work because Claude flags uncertainty better and handles longer context more reliably. But DeepSeek stayed in the stack for local-deployment scenarios and as a useful secondary opinion on stubborn code problems.

We tested Gemini thoroughly, and our honest conclusion is that it earns its place in exactly one scenario: when you are working inside Google Workspace and need AI assistance that integrates natively with Docs, Sheets, or Drive.

For processing a large set of Google Sheets reports or pulling structured data from Drive into a document draft, Gemini's native integration is genuinely faster than copy-pasting into another tool. The 2-million-token context window is also real, and for processing large document sets in one pass, it has an advantage.

As a standalone reasoning tool or coding assistant, we did not find it competitive with ChatGPT or Claude. Response quality on complex tasks was consistently a tier below both. Its knowledge cutoff and reliance on live search for recent information introduces accuracy inconsistencies that we ran into several times during testing.

We are not on a Gemini subscription. If you are already in Google Workspace all day, the integration case might justify it. For everyone else, the $20/month is better spent on the two tools above.

After six months of iteration, here is what our actual daily AI workflow looks like — not what we planned, but what stabilized through use.

For frontend work (my side): ChatGPT for first drafts of components and utility functions. Claude for anything touching production Redux logic, complex TypeScript, or code that needs to be reviewed before it ships. DeepSeek locally when I am working with client ERP data that should not leave our infrastructure.

For backend work (Krishna's side): Claude as the primary tool for architecture decisions, database schema review, and production API logic. ChatGPT for scaffolding and boilerplate. Lovable for frontend screens on the ScrapCity project where he is solo full-stack. DeepSeek for local testing of sensitive codebase logic.

The rule we follow is not 'use the best tool for each task' — it is 'use the minimum number of tools that cover your actual needs, and learn them deeply.' Switching mid-task costs more than using a slightly inferior tool consistently.

Total monthly AI spend between us: roughly $60-80 across both subscriptions. The productivity return on that spend, measured in hours not spent on tasks these tools now handle, is not close.

None of the tools above are reliable enough to remove the human review step from production code. That is the most important thing to understand after six months of heavy use, and it is not close to changing.

Hallucination is not solved. Every tool on this list will occasionally generate plausible-sounding but incorrect logic — Claude least, ChatGPT most consistently, but none are immune. The rate is lower than it was in 2024, but it has not gone to zero and it will not in the near term.

Context consistency degrades across long sessions. Past roughly 40% of a model's advertised context window, coherence drops measurably. If you are debugging a complex issue across a 30-message conversation, the model's awareness of earlier constraints will quietly drift. We re-paste key constraints at intervals on long sessions.

Output quality varies session to session. The same prompt on Tuesday and Thursday can produce meaningfully different quality results. Do not build automated workflows that assume perfectly consistent AI output — they will fail in ways that are hard to debug.

Data privacy is not uniformly resolved. Cloud AI tools process inputs on external servers. For client-sensitive code, we use DeepSeek locally or, when that is not practical, we redact client-specific details before pasting. Treating cloud AI tools as safe for all code by default is a mistake.

If you are a solo developer or freelancer building client projects: start with ChatGPT Plus ($20/month) for speed and versatility. Add Claude Pro ($20/month) as a second tool specifically for production review passes — it is worth the extra $20 if you ship client-facing code. Total spend: $40/month.

If you are a developer building a product solo and need frontend help: look at Lovable. It is the most significant change to the solo developer workflow we have seen in the past year. Use Claude for backend architecture alongside it.

If your work involves sensitive client data: keep DeepSeek in your stack for free, run it locally, and use it specifically for the code that should not go to cloud AI. You do not need to choose between AI assistance and data privacy if you deploy locally.

If you are already embedded in Google Workspace: Gemini may be worth evaluating for the integration. Test it for a month before committing.

If you are tempted to run four or five AI tools simultaneously: do not. The switching cost eats the productivity gain. Two tools used well will outperform five tools used inconsistently, every time.

After six months and three shipped products, here is how we honestly position each tool — not by spec sheet, but by what we actually reach for and when.