GEMINI vs GPT vs CLAUDE : Le TEST ULTIME en 2025 ! đŸ”„

GEMINI vs GPT vs CLAUDE : Le TEST ULTIME en 2025 ! đŸ”„

GEMINI vs GPT vs CLAUDE: The ULTIMATE TEST in 2025! đŸ”„

by iAlan · View the channel on PSI

Applied Sciences & Engineering AI — Models & Research UYQArtificial intelligence (AI)UYQFGenerative artificial intelligence
🎙 iAlan đŸ‘„ 8K 📅 December 7, 2025 ⏱ 11 min 👁 241 📄 documentary 🧭 2026-09-12
Available in: English (current) Français

Keywords

comparisonAI codingGeminiGPTClaude

Summary

In this video, the creator tests three leading AI models—Claude 4.5 Opus, Gemini 3 Pro, and GPT 5.1 Codex—using identical coding prompts to evaluate their practical performance. The video begins with a tutorial on obtaining GitHub Student Developer Pack for free access to AI features in VS Code. It then reviews benchmark scores (SWE-bench and LiveCodeBench) but sets them aside in favor of concrete tests. Four challenges are presented: building an auto-clicker (Python vs web), creating a horror escape game website, making a gym website ‘FitZone’, and developing a YouTube video downloader. Each model receives the same spec, and the creator shows real-time results, noting successes and failures. Claude impresses with polished, complete websites, while Gemini and GPT produce functional but less visually refined outputs. For the downloader, only Claude successfully executes the task; Gemini and GPT fail due to unfinished functionality. The creator concludes that Claude appears superior in these tests, but acknowledges the subjective nature of the comparison and the varying complexity of prompts. No rigorous statistical analysis is provided, yet the practical demonstrations offer tangible insights for viewers.

181 words

Critical Evaluation

Value of the Information & Strength of the Argument

The video provides valuable hands-on evidence of AI coding assistant performance in real-world tasks, using identical prompts to compare three leading models. This approach goes beyond benchmark metrics and gives viewers a tangible sense of each model’s capabilities and limitations. The argumentation is based on direct observation, but it lacks controlled conditions: the creator occasionally simplifies prompts between tests for Claude, and failures are not systematically diagnosed. The reasoning is transparent, acknowledging when a model underperforms, but the sample size (four tasks) is small and non-representative. Overall, the value lies in its practical demonstrations, while the argumentation is anecdotal and not scientifically robust.

Scientific Rigor, Source Quality, Title Accuracy

The video’s rigor is limited: it mentions benchmarks (SWE-bench, LiveCodeBench) and shows screenshots of leaderboard positions, but does not cite original sources or explain methodologies. The only provided link is to the GitHub Student Developer Pack, which is accurate and useful but unrelated to the AI comparison. The title accurately matches the content—a head-to-head coding test—and the conclusion aligns with the demonstrations. However, the lack of citations and the subjective evaluation of ‘quality’ (e.g., ‘police’ and aesthetics) reduce scientific credibility. No technical verification of the models’ outputs is performed beyond visual inspection. The title is consistent with the video, and the content delivers what is promised.

224 words

Title / Content Match

The title accurately reflects the content: a head-to-head comparison of Gemini, GPT, and Claude in coding tasks. The emphasis on 'ultimate test' matches the practical evaluation, though the title is somewhat sensationalist.

Quality & Reliability

4/10

The video offers a hands-on, subjective comparison of AI coding assistants. No rigorous methodology, no citations to scientific sources, and results are anecdotal. However, it provides practical demonstrations and honest reporting of failures, giving a moderate level of insight. Overall reliability is low-medium.

Chapters

Cited Sources

  • GitHub Student Developer Pack — The creator instructs viewers to use this link to obtain VS Code Premium and GitHub Copilot for free as students.

Contribution & Novelties

This video contributes a practical, side-by-side comparison of three major AI coding models using identical prompts, offering concrete examples of their output quality and reliability. It also demonstrates a method for viewers to access premium coding tools via the GitHub Student Pack. The originality lies in the hands-on, unscripted testing that highlights real-world strengths and weaknesses beyond benchmark scores.

Pour aller plus loin :

149 words

Radar Profile

The scores form a profile with moderate information quantity and quality, low technical depth, and very low reliability. This indicates a vide that is accessible but superficial, aiming for entertainment and practical demonstration rather than rigorous analysis. The low fiabilite reflects the absence of scientific methodology and reliance on anecdotal evidence.

Reliability 3/10