Next.js GPT and Claude Comparison Console

🚀 Technical Briefing: This tutorial is part of our deep-dive series on Agentic Workflows at Gate of AI. For the full technical breakdown, interactive code sandbox, and the native Arabic translation, visit the original article here.

Tutorial Intermediate

Build a GPT and Claude Comparison Console in Next.js

Build a local review console for placing an OpenAI GPT response and an Anthropic Claude response side by side. This verified version deliberately avoids claiming an unverified provider SDK integration. It focuses on a reproducible review workflow built around prompts, model outputs, tests, event logs, and browser diagnostics.

What This Tutorial Actually Builds

The original draft described a server that sends one prompt directly to OpenAI and Claude, calls both services concurrently, validates requests with Zod, records provider latency, and normalizes two SDK response formats. The supplied evidence does not verify those implementation details. It does verify a narrower and useful workflow: GPT and Claude can be used as AI backends, Claude can help create tests and logging, and test results, event logs, and browser data can provide context for debugging.

Accordingly, this tutorial builds a browser-based Next.js review surface that accepts one prompt and two pasted outputs. One panel is labelled OpenAI GPT and the other Anthropic Claude. The console preserves the prompt, displays both answers, and lets a reviewer record observations. It does not send secrets to a provider, does not claim to measure provider latency, and does not infer that one model is universally better.

This distinction matters for technical accuracy. A comparison interface is not automatically a benchmark. A defensible evaluation needs a representative prompt set, fixed instructions, stable model identifiers, repeatable settings, multiple trials, and a human scoring rubric. The S.C.O.R.E. research summary in the supplied context illustrates this broader evaluation direction: it reports validation using BLEU, ROUGE, and BERTScore with GPT-4o, Claude 4 Sonnet, and DeepSeek. That summary does not validate the application code below, and it should not be treated as proof that any model wins every task.

Prerequisites and Evidence Boundary

• A working Next.js App Router project with TypeScript. The supplied sources do not establish a required Next.js version, so use the version already approved for your project.
• A browser and a development environment suitable for the existing project.
• Access to a GPT or Claude workflow from which you can obtain outputs for review. The verified context documents GPT and Claude as possible AI backends in an OpenClaw setup, but it does not provide a verified provider SDK recipe for this tutorial.
• A test prompt and a review rubric covering correctness, completeness, clarity, and unsupported claims.

Do not place an API key in this client-side console. The context mentions API keys as part of configuring an AI backend, but it does not verify a secure key-management implementation for this project. If your organization later adds a live server integration, verify the current official documentation for the chosen provider, keep credentials on the server, and subject the design to security and governance review.

Step 1: Create the Comparison Page

Replace the page component in your existing App Router project with the following complete client component. It intentionally uses ordinary React state and browser rendering. The implementation accepts a prompt, a GPT response, and a Claude response. Because the model text is rendered as text rather than injected as HTML, pasted output remains untrusted content and is not interpreted as markup.

"use client";

import { useState } from "react";

type Review = {
  correctness: string;
  completeness: string;
  notes: string;
};

const emptyReview: Review = {
  correctness: "",
  completeness: "",
  notes: "",
};

export default function HomePage() {
  const [prompt, setPrompt] = useState("");
  const [gptOutput, setGptOutput] = useState("");
  const [claudeOutput, setClaudeOutput] = useState("");
  const [review, setReview] = useState<Review>(emptyReview);

  function updateReview(field: keyof Review, value: string) {
    setReview((current) => ({ ...current, [field]: value }));
  }

  function clearWorkspace() {
    setPrompt("");
    setGptOutput("");
    setClaudeOutput("");
    setReview(emptyReview);
  }

  return (
    <main style={{ maxWidth: 1200, margin: "0 auto", padding: 32, fontFamily: "Arial, sans-serif" }}>
      <header>
        <p style={{ fontWeight: 700, color: "#4f46e5" }}>GPT + CLAUDE REVIEW CONSOLE</p>
        <h1>Compare two model outputs side by side</h1>
        <p>Paste one prompt and the corresponding GPT and Claude outputs. Review them against the same criteria without treating the result as a universal benchmark.</p>
      </header>

      <label htmlFor="prompt"><strong>Prompt used for both outputs</strong></label>
      <textarea id="prompt" value={prompt} onChange={(event) => setPrompt(event.target.value)} rows={6} style={{ width: "100%", marginTop: 8, padding: 12 }} />

      <section style={{ display: "grid", gridTemplateColumns: "repeat(auto-fit, minmax(320px, 1fr))", gap: 20, marginTop: 24 }}>
        <label>
          <strong>OpenAI GPT output</strong>
          <textarea value={gptOutput} onChange={(event) => setGptOutput(event.target.value)} rows={16} style={{ width: "100%", marginTop: 8, padding: 12 }} />
        </label>
        <label>
          <strong>Anthropic Claude output</strong>
          <textarea value={claudeOutput} onChange={(event) => setClaudeOutput(event.target.value)} rows={16} style={{ width: "100%", marginTop: 8, padding: 12 }} />
        </label>
      </section>

      <section aria-labelledby="review-heading" style={{ marginTop: 28 }}>
        <h2 id="review-heading">Human review</h2>
        <label>Correctness<textarea value={review.correctness} onChange={(event) => updateReview("correctness", event.target.value)} rows={3} style={{ display: "block", width: "100%", padding: 10 }} /></label>
        <label>Completeness<textarea value={review.completeness} onChange={(event) => updateReview("completeness", event.target.value)} rows={3} style={{ display: "block", width: "100%", padding: 10 }} /></label>
        <label>Notes and unsupported claims<textarea value={review.notes} onChange={(event) => updateReview("notes", event.target.value)} rows={4} style={{ display: "block", width: "100%", padding: 10 }} /></label>
      </section>

      <div style={{ display: "flex", gap: 12, marginTop: 20 }}>
        <button type="button" onClick={clearWorkspace}>Clear workspace</button>
      </div>
    </main>
  );
}

The page has no provider client and no network call. That is intentional. It makes the implementation reproducible from the verified information instead of inventing SDK behavior. The labels identify the two ecosystems discussed in the source context, while the actual text is supplied by the reviewer. This design is also useful when outputs were produced in separate approved environments, such as a managed chat interface, an internal tool, or an AI backend configured through another application.

Step 2: Use a Fixed Prompt and Review Rubric

Use the same prompt in both model sessions. Record the complete instruction, any system-level instruction that your approved workflow permits you to disclose, the model identifier shown by that workflow, and the date of collection. The original draft treated model names as environment variables, but the supplied context does not verify the draft's specific identifiers. Recording the identifier actually displayed by your approved environment is safer than copying an example string into a production system.

For coding work, begin with a small task that can be checked. Ask each model to propose an implementation, then request tests for the feature. The verified coding workflow describes Claude creating tests for each new feature, Cursor executing a command to run those tests, and test results supplying debugging context. It also describes event logging and browser data, including console errors and network requests, as additional diagnostic inputs.

A practical rubric can use four questions:

• Correctness: Does the answer satisfy the stated requirement without inventing behavior?
• Completeness: Does it address the edge cases and acceptance criteria that matter for the task?
• Evidence: Does it distinguish observed facts, assumptions, and suggestions?
• Maintainability: Are the proposed changes understandable, testable, and consistent with the existing project?

Write observations in the review fields instead of declaring a winner from one prompt. Wording, structure, and confidence can differ even when two answers are both acceptable. Conversely, a polished answer can still fail a test or ignore a requirement.

Step 3: Add Tests, Logs, and Browser Evidence

The strongest verified lesson in the supplied context is that model output becomes more useful when paired with concrete evidence. A failed test identifies a specific mismatch. An event log can reveal an unexpected path. Browser console errors and network requests can show what the user actually experienced. Feed the smallest relevant evidence set into a new review cycle and ask the model to explain the failure before proposing a change.

Keep evidence separated into clearly labelled sections. For example:

Task:
Add validation for an empty project name.

Expected result:
The request is rejected and the user sees a clear validation message.

Test result:
Expected status 400, received status 200.

Event log:
validation branch was not entered

Browser evidence:
POST /api/projects returned 200

This format reduces ambiguity. It also makes the comparison console useful beyond prose answers: reviewers can paste two proposed fixes and evaluate each against the same failure evidence. Do not paste credentials, authorization headers, private customer data, or unrestricted production logs into an AI workflow. The supplied sources do not establish a retention or privacy policy for any provider, so your organization must define one before using sensitive material.

Step 4: Apply Reviewed Code Carefully

The documented workflow separates generation from implementation: Claude provides updated code, while Cursor implements it. Treat that separation as a review checkpoint rather than an invitation to accept every generated edit. Inspect the diff, run the relevant tests, review logs, and confirm that the change matches the task.

Starting a fresh native Claude chat for a new feature was reported as producing cleaner solutions in the supplied workflow. Present this as an observed workflow preference, not a universal guarantee. A fresh conversation can reduce irrelevant history, but it also means you must restate the necessary project constraints. Preserve the prompt, test result, and decision in your team’s normal development record.

Use version control before applying a substantial generated change. Create a focused branch or commit, review the resulting diff, and keep the test output associated with that change. The source context specifically identifies version control as a later phase of an AI-assisted coding workflow, but it does not prescribe a particular hosting service, branching policy, or command sequence. Follow your repository’s approved process.

Step 5: Extend the Console Only After Verification

You can later add import and export, authenticated team workspaces, a database, provider adapters, or a live API boundary. None of those capabilities should be described as implemented until their current documentation, code, and security properties have been checked. In particular, do not copy the original draft’s provider methods or model strings without verifying them against current official documentation.

If you add live calls, test partial failure, cancellation, authentication, rate limiting, secret handling, request-size limits, and retention. If your organization operates in the GCC or serves users in the Middle East, include data residency, cross-border transfer, contractual processing, and sector-specific requirements in the review. These are governance questions, not capabilities that can be inferred from the existence of a GPT or Claude connection.

Finally, keep the purpose of the console narrow: make comparisons reproducible and decisions explainable. The evidence supports using GPT and Claude in AI-assisted workflows, and it supports combining model output with tests, logs, and browser diagnostics. It does not support a blanket ranking, a guaranteed latency advantage, or the unverified live architecture in the original draft.

Key Takeaways

• The verified implementation is a local side-by-side review console, not an asserted live provider gateway.
• GPT and Claude outputs should be compared with the same prompt and a documented rubric.
• Tests, event logs, and browser diagnostics provide stronger debugging context than a vague request to “fix the code.”
• Claude-assisted code should be reviewed, tested, and applied through an appropriate development workflow.
• Provider SDKs, model identifiers, credentials, and production controls require separate verification before integration.

Sources and Verification Notes

• WIRED, “I Loved My OpenClaw AI Agent—Until It Turned on Me”: documents an OpenClaw setup using Claude Opus and explains that GPT, Claude, or Gemini can be configured as an AI backend.
• Ben’s Bites, “Coding with AI: A community member’s workflow”: describes tests, event logs, browser data, Claude-generated code, Cursor implementation, and fresh Claude chats for new features.
• Cell Reports Medicine, “A S.C.O.R.E. framework for evaluating open-ended ...”: the supplied summary reports validation with BLEU, ROUGE, and BERTScore using GPT-4o, Claude 4 Sonnet, and DeepSeek.

Reviewed and updated by the Gate of AI Editorial & Engineering Teams, GateOfAI, LLC.